Fatigue Analysis of Shovel Body Based on Tractor Subsoiling Operation Measured Data

Abstract

This paper aims to investigate the effects of soil penetration resistance, tillage depth, and operating speeds on the deformation and fatigue of the subsoiling shovel based on the real-time measurement of tractor-operating conditions data. Various types of sensors, such as force, displacement, and angle, were integrated. The software and hardware architectures of the monitoring system were designed to develop a field operation condition parameter monitoring system, which can measure the tractor’s traction force of the lower tie-bar, the real-time speed, the latitude and longitude, tillage depth, and the strain of the subsoiling shovel and other condition parameters in real-time. The time domain extrapolation method was used to process the measured data to obtain the load spectrum. The linear damage accumulation theory was used to calculate the load damage of the subsoiling shovel. The magnitude of the damage value was used to characterize the severity of the operation. The signal acquisition test and typical parameter test were conducted for the monitoring system, and the test results showed that the reliability and accuracy of the monitoring system met the requirements. The subsoiling operation test of the system was carried out, which mainly included two kinds of soil penetration resistances (1750 kPa and 2750 kPa), three kinds of tillage depth (250 mm, 300 mm, and 350 mm), and three kinds of operation speed (4 km/h low speed, 6 km/h medium speed, and 8 km/h high speed), totaling 18 kinds of test conditions. Eventually, the effects of changes in working condition parameters of the subsoiling operation on the overall damage of subsoiling shovels and the differences in damage occurring between the front and rear rows of subsoiling shovels under the same test conditions were analyzed. The test results show that under the same soil penetration resistance, the overall damage sustained by the subsoiling shovels increases regardless of the increase in the tillage depth or operating speed. In particular, the increase in the tillage depth increased the severity of subsoiling shovel damage by 19.73%, which was higher than the 17.48% increase due to soil penetration resistance and the 13.07% increase due to the operating speed. It should be noted that the front subsoiling shovels consistently sustained more damage than the rear, and the difference was able to reach 16.86%. This paper may provide useful information for subsoiling operations, i.e., the operational efficiency and the damage level of subsoiling shovels should be considered.

1. Introduction

With increasing mechanization in all countries, agricultural equipment compacts the soil during field operations [1,2]. Authoritative soil tillage research organizations have confirmed that soil compaction ultimately causes soil hardening and that it takes a long time for the soil to return to its natural condition, which can be accelerated by the use of human-induced equipment [3,4,5]. Recently, as one of the critical measures of conservation tillage, subsoiling operation has been recognized as an effective method to address soil consolidation [6,7]. Subsoiling could reduce soil strength, increase soil porosity, and improve the flow of water and air in the soil, which is beneficial for grain growth and is one of the important means to improve crop yields [8,9].

Generally, the subsoiling operation aims to destroy the soil’s subsoil layer with a longer plow body while maintaining the surface vegetation and topsoil layer [10]. Therefore, this means that subsoiling operations need to achieve greater tillage depths. During the operation, the tillage resistance generated by the interaction between the soil and the subsoiling machine is the workload that needs to be overcome for the tractor to move forward. Undeniably, the distribution of soil properties is the key factor affecting agricultural tractors’ performance and the maximum mechanical load, and the depth of tillage is obviously an influencing factor. In addition, the operating speed also affects the operating load according to the formula of the tillage resistance [11,12,13]. The increase in operational load during subsoiling may cause damage to the tractor and subsoiling machine implements. At this point, ensuring good operating results while reducing the possibility of tractor and subsoiling machine damage becomes an important concern, and this issue will directly affect farmers’ incomes [14,15]. Therefore, the soil tilth must be managed using the best implementation method and power match to reduce energy and fertilizer consumption. Economic and environmental considerations compel farmers to use the optimal implementation situation for deep tillage to achieve the desired final soil conditions.

Existing research has shown that to improve the subsoiling tillage effect and implement reliability, two aspects should focus on the structure design and implement–soil interactions. The former mainly includes factors such as the implement shank, geometry, and mounting angle. The latter mainly involves applying the discrete element method and actual field trials to obtain empirical values based on a large amount of data.

Numerous studies have shown that the shank, geometry, and mounting angle of tillage implements play a decisive role in tillage effectiveness and implement reliability. Owen [16] investigated the force–depth relationship of three different wing types of chisel plow tines in compacted clay soils and arrived at a primary trend of vertical force versus tillage depth. Ucgul [17] used DEM to model the effect of soil-cutting-edge geometry on tillage forces and the effect of soil-cutting-edge geometry on soil movement. McKyes and Hoseinian [18] thought DEM simulations could predict force contributions from the share and shank of the tool. The rake angle, draft, vertical force, and soil pulverization increased. The minimum specific draft for a 15° and 25° rake angle was at a tilt angle of 20°. Wang [19] developed a discrete element model and evaluated it using laboratory tests to investigate the effect of varying particle radii (ranging from 3 to 19 mm) on soil–subsoiler interactions. Shafaei [20] communicated the prediction of the required draft force of disk plow implementation during tillage operations. The simultaneous or individual increment of the forward speed and plowing depth caused a nonlinear increment of draft force. The findings of the above studies indicate that optimizing the tillage tool’s design will help improve energy efficiency. Further analysis reveals that an accurate representation of soil–tool interactions is essential to such optimization.

Research on the interaction between implements and soil during crew operations has been broadly divided into two directions. The first direction is the broad application of the discrete element method (DEM), which simulates granular materials to simulate the interaction process between soil layers and between soil and implements [21,22]. Numerous scholars have used the discrete element method (DEM) and CFD simulation methods to study the dynamic changes in soil during the simulation process to predict the soil tillage characteristics, structural changes, and unreasonable effects of implements of different structures on soil [23,24]. The discrete element approach demonstrates superiority in modeling the theoretical changes in soil–soil and soil–implement processes. Still, there are few detailed studies incorporating practical experiments to analyze plow forces. The above research utilizes the discrete element method (DEM) and other simulation methods to analyze the interaction between soil and implements, providing a theoretical basis for parameter selection in field experiments. To this end, a comprehensive monitoring system was established in this study to collect data on the tillage depth, operating speed, and traction force, analyzing the effects of soil on subsoiling tool damage under different working conditions.

The second direction is to carry out field tests, combining theory with practice, to analyze the effects of tractor driving conditions on the operational performance of tractor tillage units concerning a single operating parameter, such as the type of operation, the choice of transmission gear, and the depth of tillage [25,26,27]. The parameters included in the test systems built by existing studies are generally the tillage depth, traction force, speed, angle, etc. Kichler et al. [28] demonstrated that ground speed (choice of transmission gears) affects tractor fuel use, affecting operating costs and the productivity of the two subsoiling implements. Moeinfar et al. [29] established a theoretical model for blade force prediction based on the magnitude analysis method and investigated the effects of tillage depth, angle, and operating speed on the draft force of a thin blade based on a traction force testing system. Kim et al. [30] set up a field monitoring system to analyze the effects of different soil conditions and tillage depths on the tractor tillage unit and calculated PTO damage based on measured load profiles, rainflow counting, and the modified Miner’s rule. It should be noted that the field load monitoring system can measure the engine load, traction force, vehicle speed, wheel axle load, tillage depth, etc. Bauer et al. [31] verified that setting different upper tie bar lengths would have an effect on the change in the tractor furrow wheel and landing wheel loads during plowing and also investigated the rear wheel loads, tire inflation pressures, and their effects on the tractor under-wheel contact pressures. In summary, the study of implement–soil interaction based on the monitoring system of operating conditions parameters and field experiments is still a hot research topic, and the related research mostly focuses on the direction of plowing, while the research results related to deep-pine are still in the accumulation stage. Moreover, we have not yet seen systematic research on taking subsoiling operation as the research object, building the monitoring system to obtain the unit parameters such as tillage depth, vehicle speed, and traction force, and integrating the strain testing technology to obtain the real-time strain of the shovel body to systematically study the role and influence of various environmental and operational parameters on the deformation and damage of the shovel body of subsoiling machine. Based on the analysis of the aforementioned field test studies, soil penetration resistance, tillage depth, and operating speed have been identified as the three key variables influencing implement damage. Therefore, in this study, these three variables were introduced as independent variables in our field experiments. Under different combinations of two levels of soil penetration resistance, three levels of tillage depth, and three levels of operating speed, the effects on the fatigue damage of the subsoiling implement were systematically investigated.

The paper aims to build a tractor-operating condition parameter monitoring system, combined with the real-time strain measurement technology of the subsoiling operation shovel body, to analyze the role and influence of different soil penetration resistance, tillage depth, and operating speed on the deformation and fatigue damage of the shovel body under the subsoiling working conditions. This paper discusses subsoiling operation shovel body deformation under different operation conditions.

The main contributions of this paper are as follows.

(1)

This paper proposes a complete set of fatigue damage calculation and comparison methods for subsoiling shovels, which mainly includes four parts: real-time measurement of field condition data, extraction and extrapolation of stress load spectrum, linear accumulation damage calculation, and severeness analysis.

(2)

This study develops a tractor-operating condition parameter monitoring system to comprehensively monitor the operating parameters of key components during tractor operation. The system meets the data acquisition requirements of various tests, such as the tractor driving performance, hydraulic lifting performance, traction performance, and implementation of strain measurements.

(3)

With soil penetration resistance, farming depth, and operating speed as test variables, 18 test cases were carried out to verify the accuracy and effectiveness of the proposed method. This research provides a theoretical analysis method for optimizing machine tools and improving shovel body structure, which has certain practical value.

2. Materials and Methods

2.1. Monitoring System Design

This study develops a tractor-operating condition parameter monitoring system to comprehensively monitor the operating parameters of key components during tractor operation. The system meets the data acquisition requirements of various tests, such as tractor driving performance, hydraulic lifting performance, and traction performance, and implements strain measurement, which greatly improves the efficiency of tractor operation monitoring.

The tractor-operating condition parameter monitoring system developed in this paper mainly comprises sensors, a data acquisition controller, and an upper computer monitoring platform. Corresponding sensors are selected according to the tractor parameters to be measured. The condition parameter acquisition controller is developed using the NI-C DAQ 9135 controller and the supporting data acquisition module. The control software of the monitoring system and the monitoring interface are designed through LabVIEW virtual instrument software, and the system is remotely controlled and monitored through a wireless touch screen. The overall design scheme of the monitoring system is shown in Figure 1.

Comprehensively analyze the common tractor plowing, rotary plowing, subsoiling, and other operating conditions and the actual operation of the trajectory, mechanical characteristics, and other factors, combined with the engine, wheels, front axle, rear axle and suspension system, and other components of the operating characteristics of the tractor, selected tractor speed, body attitude, engine speed, load ratio, wheel speed, wheel acceleration in all three directions, the front and rear axle vibration, implements, tillage depth, horizontal traction, upper and lower tie rod inclination, upper tie rod pull pressure, lower suspension point pin force, suspension system hydraulic pressure and some structural strain as the key operating conditions parameters design monitoring system. Tie rod inclination angle, upper tie rod pulling pressure, lower suspension point pin force, suspension system hydraulic pressure, and part of the structure strain are the key operating conditions parameters in designing the monitoring system. The installation position of the above sensors and other equipment and the installation effect are shown in Figure 2, and the selection of the sensors used and their characteristics are shown in

Table 1. Sensor selection and characteristics.

Sensor Type	Model Number	Parameters	Output Method
Vibration sensors	INV9822	Uniaxial; Piezoelectric; IEPE Type	Analog Voltage
Strain gauges	BHF350-3CA	Triaxial 45 ° Strain Flower; Sensitive Grid Resistance 350 Ω	Analog Voltage
Tension sensors	TZ207	12 V supply, 0–5 t range	RS485
Traction sensors	TL08	12 V supply, 0–3 t range	Analog Voltage
Inclination sensor	BWM415	12 V supply, 0.01° accuracy, ±180° range	CAN
Hydraulic pressure sensor	MIK-P300	12 V supply, 0–40 MPa range	Analog Voltage
Acceleration sensor	ACC345	5 V power supply, output frequency 200 Hz, range 16 g	TTL
GNSS Receivers	AMG_PFZ202	GPGGA, GPVTG statement output, output frequency 10 Hz	RS232

.

In this study, strain electrodynamics was used to obtain strain/stress data in critical stress concentration areas of the shovel body of a subsoiling shovel. A triaxial 45° strain flower was selected. The strain gauge sensitive grid material is con-copper alloy, the substrate is phenolic material, the nominal resistance value is 350 Ω, the length of the sensitive grid is 3 mm, and the sensitivity coefficient is 2.1. Considering the primary vulnerable position of the subsoiling shovels and the difficulty of pasting the strain flower, this study chooses the shovel shank position of the six deep-pine shovels of the deep-pine machine as a strain measurement point and the location of the strain gage arrangement shown in Figure 3.

To obtain the vibration acceleration data of the vehicle’s critical parts, vibration acceleration sensors are arranged at the axle head of the front and rear axles to predict and analyze operational reliability. The selected vibration sensors can be directly powered and transmitted through the NI-9234 vibration acquisition module.

To comprehensively analyze the traction force characteristics of the suspension system, this system installs a pulling force sensor on the upper drawbar of the tractor and axle pin force sensors at the suspension points of the left and right lower drawbars, respectively. In addition, a hydraulic pressure sensor is installed at the pressure measurement point of the suspension lifting oil circuit to collect the hydraulic pressure of the hydraulic system. To fully acquire the angle information of the suspension system and derive the parameters such as suspension position and tillage depth, dynamic inclination sensors were installed at the upper tie rod and the suspension’s left and right lower tie rods, respectively. To determine the correspondence between suspension position and tillage depth, this paper carries out the tillage depth calibration test in the field, as shown in Figure 4.

A polynomial fit was performed on the data to obtain the correspondence between tillage depth and three-point suspension position as in Equation (1):

$$h=-0.00071x^3+0.09x^2-8.64x+332.73$$

(1)

where, h is the tillage depth, mm; x is the suspension position, 0~100%.

The data acquisition controller is the core of the monitoring system, including the acquisition control host and the corresponding signal acquisition module. NI-C DAQ 9135 is selected as the data acquisition control host. The various data acquisition modules are the NI-9205 voltage acquisition module, NI-9236 strain acquisition module, NI-9234 vibration acquisition module, and NI-9862 CAN transceiver module. The overall architecture of the designed monitoring system is shown in Figure 5. The selected equipment and main working parameters are shown in

Table 2. Data acquisition and control equipment and features.

Equipment	Parameters
Compact DAQ 9135 control host	8 slots; operating temperature −40~70 °C
NI-9205 Voltage acquisition module	Maximum sampling rate 200 kS/s/ch
NI-9236 Strain gauge module	Maximum sampling rate 10 kS/s/ch; 8 channels
NI-9234 Vibration acquisition module	Maximum sampling rate 51.2 kS/s/ch; 4 channels
NI-9862 CAN Interface module	Transmission rate 1 Mbit/s;
Portable displays	Touch screen; mobile network connectable

.

Based on LabVIEW platform development and design of monitoring system host computer software. The monitoring system software mainly realizes the following functions: (1) signal-receiving program to realize the real-time, synchronous, and appropriate frequency acquisition of signals from different sources; (2) data processing program to solve hexadecimal data, string data, etc., in real-time and directly obtain their numerical output results; (3) data saving program to set the channel name, time and other parameters of the data, and save the data packages to the specified binary TDMS file; (4) parameter setting program, used to set the data acquisition channel, frequency, file saving path and other parameters such as baud rate; (5) data display program, used to real-time display of all the collected data and waveform changes. Design the human–computer interaction interface to realize the system’s real-time remote monitoring and control. The interface of this system is shown in Figure 6.

2.2. Fatigue Analysis Method

The data on the loads sustained by the tractor during the subsoiling operation are the basis for this paper’s load and fatigue analysis of the subsoiling operation. These data will achieve different goals after different processing methods. Fatigue damage is when a component goes from no damage to crack failure. To predict the fatigue life of a component, fatigue damage is quantified and reduced to a definite relationship between stress and the ultimate cycle factor. In addition, Kim et al. [32,33] proposed the concept of severeness to represent the tractor load, defined as the ratio of the damage sum at each operation to the minimum damage sum from all the operations. It was applied to objects such as transmission input shafts and PTO shafts to analyze the severity of loads for typical operating conditions such as plowing and rototilling, and the concept demonstrated better performance.

This paper aims to analyze the effect of the variation in working condition parameters of subsoiling operation on the overall damage of subsoiling shovels and the difference between the front and rear rows of subsoiling shovels that incur damage under the same test conditions. Therefore, to realize the analysis goal, this paper adopts the load time-domain extrapolation method, rainfall counting analysis method, Miner’s linear damage accumulation theory, and severity assessment method based on the measured stress loads of deep-pine operation. Among them, the time-domain extrapolation method can be used to obtain the load spectrum based on the measured data, the rainflow counting analysis method and Miner linear cumulative damage theory can be used to obtain the fatigue damage caused by the load spectrum of the field operation to the subsoiling machine, and the severity assessment method can be used to quantitatively analyze the influence of the three deep-pine test variables on the fatigue damage of front and rear row deep-pine shovels. Figure 7 shows this paper’s schematic block diagram of the analysis process.

Limited by the cost and time of field testing, the amount of measured raw load data is limited, and there is a possibility that the extreme value has yet to appear, which cannot wholly reflect the actual loading situation of the subsoiling shovel. For the measured stress data during subsoiling operation, the load time-domain extrapolation method based on the Peak Over Threshold (POT) theory is used in this section to prepare the load spectrum [27]. The time-domain extrapolation method retains the temporal information of the original data and considers the potential influence of load loading sequence on fatigue damage; meanwhile, the method can predict the extreme load distribution of the actual samples more accurately.

The time-domain extrapolation is divided into three steps: firstly, the threshold is determined, and the appropriate upper and lower thresholds μ₀ max and μ₀ min are selected by using the Mean Excess Function (MEF) method to determine the suprathreshold. The distribution of extreme loads for the peak samples is then obtained by fitting the suprathreshold quantities based on the Generalized Pareto Distribution (GPD). Equations (2) and (3) are the GPD cumulative distribution and probability density function expressions, respectively; finally, the new extreme values are generated by combining the GPD fitting results, and the appropriate time-domain extrapolation multiplier is selected to obtain the target load spectrum.

$$G(z;{\mu }_0,{\sigma }_0,\xi )=\left\{\begin{array}{l}1-{(1+\xi {\displaystyle \frac{z}{{\sigma }_0}})}^{-{\displaystyle \frac{1}{\xi }}}, \xi \ne 0, x>{\mu }_0\\ 1-\exp (-{\displaystyle \frac{z}{{\sigma }_0}}), \xi =0, x>{\mu }_0\end{array}\right.$$

(2)

$$g(z;{\mu }_0,{\sigma }_0,\xi )=\left\{\begin{array}{l}{\displaystyle \frac{1}{{\sigma }_0}}(1+\xi {\displaystyle \frac{z}{{\sigma }_0}}{)}^{{\displaystyle \frac{1+\xi }{\xi }}}, \xi \ne 0, x>{\mu }_0\\ {\displaystyle \frac{1}{{\sigma }_0}}\exp (-{\displaystyle \frac{z}{{\sigma }_0}}), \xi =0, x>{\mu }_0\end{array}\right.$$

(3)

where z is the excess, z = x_i − μ₀ (i = 0, 1, 2, …, n); x_i is the load test value (MPa); μ₀ is the threshold value (MPa); ξ is the shape parameter; σ₀ is the scale parameter.

Among them, the goodness-of-fit directly affects the selection of the upper and lower thresholds for extrapolation, which is the critical basis for the time-domain extrapolation of the load spectrum. Moreover, the goodness-of-fit is extremely sensitive to the threshold value selection. The linear interval near the fluctuation section in the MEF diagram should be selected as the threshold interval as much as possible, and the extrapolation multiplier here is 6. This step aims to obtain the extreme loads of the measured data in this section. In addition, the threshold selection and extrapolation fitting under some test conditions are shown in detail to illustrate the extrapolation accuracy of this study.

On the other hand, this paper adopts the Palmgren–Miner law of linear fatigue damage theory to calculate the fatigue damage sustained by the subsoiling machine under operating conditions. The basis of the linear fatigue cumulative damage theory is the S-N curve, expressed by the Basquin formula.

$$N=\left\{\begin{array}{l}\alpha S^{-\beta }, S >S_f\\ \infty , S\le S_f\end{array}\right.$$

(4)

where N is the number of loading cycles at a certain stress level; S is the load stress, MPa; S_f is the fatigue limit, MPa; α, β are material property parameters.

Based on the material S-N curve, the Palmgren–Miner method introduces the concept of cycle ratio, i.e., n_i/N_i, where n_i is the number of cycles of load stress at a certain level, and N_i is the number of cycles of fatigue life of the material under the level of stress. A linear relationship between the fatigue damage and the cycle ratio, i.e., under a given cycle ratio, the damage D caused by various load stresses is the same, as shown in Equation (5).

$$D={\displaystyle \frac{n_i}{N_{i,f}}}$$

(5)

As a result, the damage caused by loads at different stress levels can be linearly summed, i.e., fatigue damage can be quantified, as shown in Equation (6).

$${\displaystyle \sum D_i}={\displaystyle \sum \frac{n_i}{N_{i,f}}}$$

(6)

When ${\displaystyle \sum D_i}\ge 1$, failure of the part is expected to occur. It should be noted that the fatigue damage calculation in this paper is based on the joint simulation environment of ANSYS Workbench and nCode designlife, which on the one hand, simulates and analyzes the stress and strain of the subsoiling shovels under the boundary environment of the field operation, and on the other hand imports the measured stress and load data of the subsoiling shovels during the field test so that the fatigue damage caused by changes in the soil penetration resistance, tillage depth and speed of the subsoiling shovels can be calculated comprehensively. The fatigue damage caused by soil penetration resistance, tillage depth, and speed change on the front and rear rows of subsoiling shovels are comprehensively calculated.

Finally, a severeness analysis was conducted to quantify the effects of the three subsoiling test variables on the fatigue damage of subsoiling shovels’ front and rear rows. For the severity, a test operation condition that caused the most minor damage was selected as the control group, and the ratio of the damage caused by each of the remaining test operation conditions to the base damage of the control group was calculated. The greater the intensity and severity of the operational loads to which the tractor is subjected during field operations, the shorter the fatigue life.

2.3. Field Test

This test used a 140 hp powershift tractor (T1404, CNH Industrial N.V., Amsterdam, The Netherlands). Its main parameters are shown in

Table 3. Specifications of the agricultural tractor.

Item	Specification
Engine rated power/hp	142
Engine rated speed/(r/min)	2200
Length × width× height/(mm × mm × mm)	5290 × 2414 × 3115
Total Weight/kg	5400
Track width/mm	1530~2230
Wheelbase/mm	2739

.

This study used a six-shovel subsoiling machine to carry out subsoiling operation tests with different operating conditions. Its main parameters are shown in

Table 4. Specifications of the agricultural implement.

Item	Specification
Total Weight/kg	1200
Length × width× height/(mm × mm × mm)	2600 × 2900 × 1350
Required power/kW	91.9~110.2
Working depth/mm	250~400
Working width/mm	2700
Number of rows	6

.

The tractor-subsoiling machine test set constructed in this paper is shown in Figure 8.

Soil physical property measurement tests were carried out on the test plots to determine the Soil Compactness (SC) information for two different soils. The soil compaction testing instrument used in this paper was SC-900, and the soil penetration resistance data were obtained once for every 2.5 cm drop of the instrument’s drill, as shown in Figure 9. For the soil cone, the penetrometer is a Scout 900 Field Scout™ electronic penetrometer by Spectrum Tech. Inc., Aurora, IL, USA. Two kinds of soil were selected for testing at 100 points; the test results were averaged to determine the relationship between soil depth and soil penetration resistance, as shown in Figure 10.

The analysis shows that the depth of the plow subsoil layer of the two soils is between 250 mm and 350 mm, and the soil in this range has the highest degree of penetration resistance. Therefore, this paper selected 250 mm to 350 mm as the depth range of the subsoiling test. Based on the average value of soil penetration resistance in this soil range, the two soil penetration resistance levels for this test were set at 1750 kPa and 2750 kPa, respectively.

To more accurately describe the degree of soil compaction under field conditions, this study simultaneously tested the moisture content of two types of soil within the scope of the deep loosening experiment. The moisture content was measured using an instrument model, JK-100F. The device is a JK-100F™ digital strain gauge tester by JK Instruments, Shenzhen, China. The instrument’s probe records soil moisture data every 5 cm as it descends, as shown in Figure 11. The average moisture content of the two types of soil was tested at depths ranging from 20 cm to 35 cm, and the data obtained at different soil layer depths are presented in

Table 5. Average moisture content at different depths.

Measured Depth (cm)	Soil Type
	Sandy and Loamy	Clayey
20	7.3%	24.7%
25	8.1%	25.6%
30	9.4%	26.4%
35	10.6%	27.2%

.

To analyze the effects of different factors of the shovel body’s deformation, this test used the test method of changing a single variable and a total of two soil penetration resistance levels (1750 kPa, 2750 kPa), three tillage depths (250 mm, 300 mm, 350 mm), and three operating speeds (4 km/h, 6 km/h, and 8 km/h) were selected as the levels of the three factors. All of the designed test working conditions are shown in

Table 6. Specific parameters of field working conditions.

Work Condition Number	Soil Type		Tillage Depth (mm)			Operating Speed
	Sandy and Loamy (1750 kPa)	Clayey (2750 kPa)	250	300	350	Slow (4 km/h)	Normal (6 km/h)	Fast (8 km/h)
1	√		√			√
2	√		√				√
3	√		√					√
4	√			√		√
5	√			√			√
6	√			√				√
7	√				√	√
8	√				√		√
9	√				√			√
10		√	√			√
11		√	√				√
12		√	√					√
13		√		√		√
14		√		√			√
15		√		√				√
16		√			√	√
17		√			√		√
18		√			√			√

.

A field subsoiling test was conducted in Changping District, Beijing, where data such as tillage depth, operating speed, and deep-pine shovel strain were continuously measured. The test was conducted in April 2023 with cloudy weather, 18–20 °C, and a light breeze. The test plots used were 100 × 80 m in size, located at 40°10′52″ N and 116°26′36″ E, with sandy, loamy, and clayey soils, respectively. The tractor was operated in four-wheel-drive mode, and the information acquisition tests for the above 18 working conditions were conducted based on the ground speed collected by the GNSS receiver in real time and the tillage depth converted by the angle sensor. The actual situation of the test site is shown in Figure 12. Finally, after removing the dead time, such as stopping and turning, the measured time course was split into 30 s standardized data segments.

3. Results and Discussion

3.1. Monitoring System Test Results

3.1.1. Signal Error Test Results

To verify the reliability and accuracy of the developed monitoring system, the acquisition effect of each type of signal is first tested to analyze the acquired signal’s fundamental performance.

The accuracy of voltage signal acquisition is first tested by using a DC-regulated power supply to output a standard voltage and comparing the values acquired by the multimeter and the voltage acquisition module. Analysis shows that in the 0~15 V range of the voltage acquisition module of this system, the maximum absolute error is 0.03 V, and the maximum quoted error is 0.2%, which is a slight error. Therefore, the error of the voltage signal collected by this monitoring system meets the operational working condition parameter measurement needs.

To verify whether the packet loss rate when the system receives CAN data can meet the requirements, the CAN analyzer is used to send hexadecimal data to the monitoring system, and 500,000 pieces of data are sent for each test to count the proportion of correctly received data. The results show that the system correctly receives all the test data when they are sent according to the cycle of 20 ms; when it is sent according to the cycle of 10 ms, the monitoring system receives a total of 499,993 correct data, and the packet loss rate is 0.0014%, which can meet the reception needs. According to the tractor serial network communication ISO11783 [34] protocol, most of the data in the tractor CAN bus are sent in a period of not less than 10 ms, so the effect of the monitoring system receiving CAN signals is in line with the needs of the operational conditions parameters measurement.

This system converts RS232, RS485, and TTL output signals into serial port data for reception. Since the data received from the serial port are similar to the data received from the CAN bus, which are also prone to packet loss, the same verification test for the CAN receiver module is carried out for the serial port receiver module. The test results show that when the data sending period is 10 ms, the 500,000 pieces of data sent are successfully received; when the sending period is 1 ms, 499,873 pieces of data are received, and the packet loss rate is 0.03%. Considering that the minimum period of RS-232, RS-485, and TTL output signals in this system is 10 ms, the effectiveness of this test system in receiving serial data meets the needs of operational work parameters measurement.

The monitoring system has a maximum reference error of 0.2% for voltage acquisition, a packet loss rate of only 0.0014% for received CAN data (at 10 ms transmit cycle), and 0.03% for received serial data (at 1 ms transmit cycle). Therefore, the working condition parameters monitoring system of the tractors developed in this paper meets the acquisition requirements.

3.1.2. Typical Parameter Test Results

Field collection tests of typical parameters were carried out to verify the accuracy and reliability of the operating condition parameter monitoring system in field tests.

The speed test on the cement road was carried out. Due to the excellent condition of the cement road, it can be assumed that the tractor-driving wheel slip rate in the unloaded state is 0 at this time, i.e., the theoretical vehicle speed = wheel speed. The GNSS receiver collects the theoretical speed, and the wheel speed is acquired through the tractor CAN bus. Concerning the common traveling speeds of the tractor in actual operation, tests were carried out at three speeds of 4 km/h, 6 km/h, and 8 km/h, and the test results are shown in Figure 13.

The test results show that the tractor’s theoretical speed and actual speed error are small when traveling at a constant speed under the three speeds. When the speed is 4 km/h, the maximum relative error is 3.1%; when the speed is 6 km/h, the maximum relative error is 2.9%; when the speed is 8 km/h, the maximum relative error is 2.4%. The results show that during the tractor operation, the speed measurement error of the GNSS device in this monitoring system is small, which is in line with the demand for the measurement of operating conditions parameters.

The test of the horizontal traction force of the suspension system was carried out. In this system, there are two ways to obtain the traction force of the suspension system: one is to use the tractor’s force sensors to read the horizontal traction force from the CAN bus; the other is to utilize the data from the pull pressure sensors and the axle pin force sensors mounted on the upper and lower drawbars, and combine them with the data from the inclination sensors on the three drawbars for calculation, and finally obtain the actual value of the traction force. To verify the accuracy of the traction force measurement, three kinds of subsoiling operations with 250 mm, 300 mm, and 350 mm tillage depths were carried out, and the measured value of horizontal traction force was obtained through the CAN bus and at the same time, according to the sensor data, the calculated value of the horizontal component of the traction force was obtained, and part of the test results are shown in Figure 14.

It can be analyzed that, in the operation conditions of three kinds of tillage depths, the direct test results of horizontal traction force and the calculation results are basically the same, and the maximum measurement errors of the calculated values under three kinds of tillage depths are 4.5%, 4.2%, and 3.6%, respectively. The small error between the two indicates that the traction force acquisition system used in this system meets the demand for measuring the operating conditions parameters.

Due to the high test sensitivity of the strain gauges, incorrect mounting may introduce significant initial errors in the strain data. Therefore, an initial pre-test was carried out for the acquisition of the strain in the critical structures of the implement. After calibrating the bridge circuit voltage values of all the strain gauges, the tractor engine was turned on so that the tractor was run in situ with no load. The strain data of all the measurement points at this time were measured at a frequency of 2000 Hz, and some of the data are shown in Figure 15.

Analysis shows that the variation range of all measurement points is within 10 με, the fluctuation is slight, and the data change is relatively smooth, which can meet the stress and strain testing demand. Therefore, the effect of the strain measurement of this monitoring system meets the demand for measuring operational working condition parameters.

The test results indicate that the monitoring system developed in this study meets the functional requirements and can reliably collect the operational parameters needed for field tests, demonstrating practical value.

3.2. Fatigue Damage Calculation Results of Shovel Body

3.2.1. Load Spectrum Processing Results

The strain flower load data of six measurement points on the subsoiling shovel were measured by the dynamic stress test system under 18 actual working conditions, and the strain flower measured data of some measurement points are shown in Figure 16.

Following this, the principal stress at the measurement point was calculated according to the principal stress calculation Equation (7).

$$\begin{array}{c}{\sigma }_{\max }\\ {\sigma }_{\min }\end{array}={\displaystyle \frac{E({\epsilon }_0+{\epsilon }_{90})}{2(1-\mu )}}\pm {\displaystyle \frac{\sqrt{2}E}{2(1+\mu )}}\sqrt{{({\epsilon }_0-{\epsilon }_{45})}^2+{({\epsilon }_{45}-{\epsilon }_{90})}^2}$$

(7)

where σ_max and σ_min are the maximum and minimum principal stresses; ε₀, ε₄₅ and ε₉₀ are the strains in three directions of the strain rosette; E is the elastic modulus; μ is Poisson’s ratio.

Some of the principal stress data are shown in Figure 17.

Further, we analyze the principal stress load data of all measurement points of the subsoiling shovel under 18 actual working conditions and obtain the average value of the principal stress of each measurement point. The main stress of each working condition is analyzed with the suspension traction force, as shown in

Table 7. Average value of principal stress at each measurement point.

Number	Field Operating Condition			Principal Stress (MPa)						Drawbar Pull (kN)
	Soil Type	Tillage Depth (mm)	Operating Speed (km/h)	Measurement Point
				1	2	3	4	5	6
1	Sandy and loamy (1750 kPa)	250	4	6.78	9.13	6.73	5.35	9.29	7.02	3.20
2			6	9.64	11.29	8.66	7.68	12.01	12.16	3.52
3			8	10.96	12.17	9.68	8.46	14.98	12.24	3.81
4		300	4	11.34	17.31	8.49	8.36	15.45	12.37	3.37
5			6	16.55	22.91	13.98	11.99	15.21	13.08	3.60
6			8	18.63	27.13	17.65	18.93	27.13	20.74	4.03
7		350	4	24.12	25.04	11.68	9.71	25.02	23.64	3.41
8			6	25.67	28.53	20.68	19.97	28.53	24.81	3.62
9			8	25.88	30.98	19.56	20.34	29.86	26.95	4.16
10	Clayey (2750 kPa)	250	4	14.28	17.07	12.96	13.35	14.24	10.21	3.29
11			6	23.27	25.55	17.67	17.68	21.45	19.78	3.86
12			8	25.98	27.58	22.78	25.46	28.78	27.96	4.75
13		300	4	26.71	26.72	17.44	18.36	21.27	19.34	3.92
14			6	30.21	32.37	25.97	21.99	31.08	26.55	4.64
15			8	30.61	33.39	27.65	28.93	32.78	29.75	5.18
16		350	4	28.68	29.16	26.68	19.71	27.02	26.12	4.66
17			6	33.86	35.26	29.68	29.97	28.53	25.67	5.47
18			8	34.89	36.83	33.56	32.34	35.86	33.78	5.88

.

Table 7 shows that all measurement points’ principal stress and drawbar pull increase with soil penetration resistance, tillage depth, and operation speed. Among them, the principal stress is more affected by soil penetration resistance and tillage depth and less by operation speed. Comparing different strain measurement points under the same working condition, it can be found that there is also a fixed pattern between their strain data, i.e., measurement point two and measurement point five are consistently higher than the other four measurement points. This is because they are at the front of the deep mulcher and are the first to contact the firm, untilled field subject to the most significant resistance.

Due to the time and economic cost constraints of field testing, the real loading situation of the subsoiling shovel may not be completely reflected, so this paper compiles and extrapolates the load spectrum of the measured stress data during the subsoiling operation in the field by using the load time-domain extrapolation method of the threshold peak theory.

Considering the computational memory and CPU processing speed of MATLAB, the goal of this paper is to analyze the influence of the change in working condition parameters on the overall damage of the subsoiling shovel and the difference between the front and rear rows of subsoiling shovels under the same test conditions, and not to accurately solve the actual life of the subsoiling shovel of the subsoiling machine in the whole life cycle, this paper only extrapolates the load spectrum of the 30 s by six times and then performs the subsequent fatigue analysis and the related processing. Related processing. The threshold values of some measurement points under each working condition of the subsoiling operation and the corresponding GPD fitting results are shown in

Table 8. Threshold values and the corresponding GPD fitting results of key measurement points for key working conditions.

Operating Condition	Measurement Point	Extreme Value Type	Threshold/MPa	Shape Parameter ξ	Scaling Parameter σ	Goodness of Fit
4	1	Maximum	18.7482	−1.10776	16.13427	0.998555
		Minimum	4.14824	−1.49567	22.43508	0.995136
	2	Maximum	24.1929	−1.17943	13.70053	0.996393
		Minimum	10.1929	−1.1883	17.03445	0.960837
	3	Maximum	14.7388	−1.35723	2.226067	0.985659
		Minimum	5.2388	−0.46093	1.680263	0.955347
15	4	Maximum	18.0215	−1.14000	7.628577	0.996982
		Minimum	30.1215	−1.16723	7.353549	0.995532
	5	Maximum	43.2032	−0.59923	4.876758	0.966772
		Minimum	21.1032	−1.19526	7.410575	0.993483
	6	Maximum	22.1272	−1.06036	1.165446	0.999607
		Minimum	17.7272	−0.75911	0.988476	0.993430
18	4	Maximum	44.2627	−1.45818	5.784182	0.974496
		Minimum	26.1373	−1.13002	18.53236	0.997188
	5	Maximum	42.507	−1.20883	64.52458	0.995447
		Minimum	21.8934	−1.32865	6.643293	0.990065
	6	Maximum	50.8316	−1.38609	6.518919	0.999079
		Minimum	17.1376	−1.08762	3.378114	0.998668

. As can be seen from Table 8, the goodness of fit of each measurement point is more significant than 0.95, and the fitting effect is good. In addition, compared to Wen [26], who extrapolated the stress load spectra of the front axle, gearbox, and rear axle, the accuracy of the present study is higher, with a fitting accuracy of more than 0.99 for 72% of the test cases [26].

The load spectrum before and after extrapolation is further compared and analyzed in both time and frequency domains using measurement point six under operating Condition 15 as an example. The time domain comparison and rainfall matrix comparison before and after extrapolation of measurement point six are shown in Figure 18 and Figure 19, respectively.

Analyzing the load time course of measurement point six before and after extrapolation in Figure 18, it can be seen that the original load data of 30 s are extrapolated to 180 s, and the change rule of the simulated load waveform after extrapolation is the same as that of the measured load. Further analysis of the rainfall matrix before and after extrapolation in Figure 19 shows that the number of load cycles after extrapolation is significantly higher than before. The mean amplitude distribution of load cycles before and after extrapolation maintains a certain degree of similarity, which effectively verifies that the results of extrapolation here can retain the actual distribution of load under the conditions of subsoiling cutting of the tractor.

3.2.2. Fatigue Damage Calculation Results

According to the extrapolation results in the previous section and combined with the fatigue above damage theory, the damage values caused by the extrapolated load spectrum on each measuring point of the subsoiling shovel are calculated through the joint simulation of ANSYS Workbench and nCode designlife, as shown in

Table 9. Damage values at actual operating points.

Work Condition Number	Soil Type	Tillage Depth (mm)	Operating Speed	Fatigue Damage (×10−5)						Duncan’s Multiple Range Tests
				Measurement Point
				1	2	3	4	5	6
1	Sandy and loamy (1750 kPa)	250	4	3.412	3.677	3.404	3.475	3.683	3.463	Bc
2			6	3.691	3.768	3.653	3.586	3.818	3.725	Ac
3			8	3.749	3.836	3.669	3.642	3.892	3.840	Ab
4		300	4	3.776	4.092	3.690	3.739	3.997	3.883	Bb
5			6	4.003	4.215	3.866	3.703	3.914	3.901	Ba
6			8	4.038	4.296	4.101	4.066	4.353	4.083	Ab
7		350	4	4.240	4.259	3.896	3.995	4.268	4.233	Aa
8			6	4.263	4.407	4.168	4.085	4.403	4.295	Aa
9			8	4.270	4.532	4.076	4.121	4.414	4.388	Aa
10	Clayey (2750 kPa)	250	4	3.773	4.113	3.813	3.828	3.996	3.762	Cc
11			6	4.221	4.263	3.998	4.016	4.101	4.089	Bc
12			8	4.288	4.410	4.163	4.208	4.409	4.302	Ac
13		300	4	4.415	4.423	3.989	4.086	4.096	4.088	Cb
14			6	4.707	4.843	4.269	4.112	4.398	4.330	Bb
15			8	4.726	4.858	4.540	4.605	4.862	4.658	Ab
16		350	4	4.670	4.782	4.385	4.243	4.326	4.300	Ba
17			6	4.801	4.915	4.616	4.691	4.833	4.677	Aa
18			8	4.883	4.988	4.872	4.773	4.985	4.878	Aa

Notes: Means with different superscripts are significantly different at p < 0.05 according to Duncan’s multiple range test (A, B, C for operating speed and a, b, c for tillage depth).

.

According to Table 9, it can be seen that at the same soil penetration resistance, the damage sustained by the subsoiling shovel increases both in the increase in tillage depth and the increase in operation speed. The damage of the subsoiling shovel in high soil penetration resistance is also significantly greater than its damage in low soil penetration resistance. It is also worth noting that the damage values of measurement points two and five were consistently higher than the other four measurement points regardless of the operating conditions, and the damage of measurement points one and six were generally higher than that of measurement points three and four. This is because measurement points two and five are located in the front row of the deep mulcher, and they are always the first to be pressed by the hard soil on both sides, which is the easiest to cause damage. Measurement points one and six are located at the outer side of the rear row of the subsoiling shovel. The hard soil layer between them and subsoiling shovels two and five have been destroyed by subsoiling shovels two and five, so the stress mainly comes from the outside soil layer. Measurement points three and four are located in the middle of the back row of the deep loading machine, and both sides of them have been damaged by subsoiling shovels two and five, so they are subjected to the least stress. The difference between the damage values of the measurement points in the front row and those in the rear row is up to 16.86%, while the difference between the inner and outer sides of the rear row is also up to 12.87%.

Through the developed monitoring system, parameters such as tillage depth, operating speed, and traction were obtained. Combined with field experiments, the effects of different combinations of soil penetration resistance, tillage depth, and operating speed on subsoiling shovel damage were analyzed, providing an important basis for future research.

3.3. Fatigue Damage Impact Analysis Results

3.3.1. Soil Penetration Resistance

The effect of different soil penetration resistance on the severity of shovel body damage during subsoiling is shown in Figure 20. The less damaged Conditions 1 and 10 were used as the first group of comparison conditions, both of which were carried out at an operating speed of 4 km/h and a tillage depth of 250 mm, and only the soil types were different, with sandy, loamy soil (1750 kPa) for Condition 1 and clayey soil (2750 kPa) for Condition 10, and the severeness of which is compared as shown in Figure 20a. The more damaged Conditions 9 and 18 were used as the second group of comparison conditions, both of which were operated at an operating speed of 8 km/h and a tillage depth of 350 mm, with only the soil type and soil penetration resistance being different, sandy, loamy soil (1750 kPa) for Condition 9 and clayey soil (2750 kPa) for Condition 18, and their severity pairs are shown in Figure 20b shows.

Figure 20a,b shows that when the soil type of subsoiling operation was changed from sandy, loamy soil (1750 kPa) to clayey soil (2750 kPa), the damage severity of each measurement point in the first group increased by 8.8~12.8%. In contrast, the damage severeness of each measurement point in the second group increased by 13.4~23.4%. The trend of damage changes in the two groups of measurement points was the same; both increased with the increase in soil penetration resistance, indicating that the change in soil penetration resistance was positively correlated with the change in fatigue damage. Meanwhile, the damage severeness increase values of the two groups of measurement points were compared. It was found that when the soil type was changed from sandy, loamy soil (1750 kPa) to clayey soil (2750 kPa), the damage severity of the second group increased by 17.48% on average, which was significantly higher than that of the first group (10.63%). Increased tillage depth and operating speed amplify the effect of soil penetration resistance on subsoiling shovel damage. In addition, the damage severity at measurement points two and five was higher than the other measurement points under different working conditions, consistent with the previous section’s findings.

3.3.2. Tillage Depth

The effects of different tillage depths on the severeness of shovel damage during subsoiling are shown in Figure 21. Taking the less damaged Conditions 1, 4, and 7 as the first group of comparison conditions, all three conditions were carried out in sandy, loamy soil (1750 kPa) at an operating speed of 4 km/h. Only the tillage depths were different, with 250 mm for Condition 1, 300 mm for Condition 4, and 350 mm for Condition 7, and the severity comparisons are shown in Figure 21a. The more damaged Conditions, 12, 15, and 18, were used as the second group of comparison conditions. All three conditions were operated in clayey (2750 kPa) at an 8 km/h operating speed. Only the tillage depths differed: 250 mm for Condition 12, 300 mm for Condition 15, and 350 mm for Condition 18, and the severity comparisons are shown in Figure 21b.

Figure 21 shows that when the cultivation depth of the subsoiling operation was increased from 250 mm to 300 mm, each measurement point’s damage severity in the first group increased by 7.7~12.4%. In contrast, the severity of damage of each measurement point in the second group increased by 10.4~13.3%. When the tillage depth was increased to 350 mm, the damage severity of each measurement point in the first group increased by 14.5% to 24.4%, while the damage severity in the second group increased by 16.6% to 20.8%. The trend of damage changes in these two groups of measuring points was the same, and the increase in tillage depth would lead to an increase in damage in measurement points, indicating that the change in tillage depth was positively correlated with the change in fatigue damage. At the same time, the damage severity increase values of the two groups of measuring points were compared. When the tillage depth was increased from 250 mm to 350 mm, the damage severity of the first group increased by 19.73% on average. The damage severity of the second group increased by 17.58% on average, and there was no significant difference between the two groups in terms of the damage severity increase values. This indicates that the effect of tillage depth on the damage of the subsoiling shovel is not affected by soil penetration resistance and operating speed.

3.3.3. Operating Speed

The effects of different operating speeds on the severity of shovel damage during deep loosening are shown in Figure 22. The less damaged Conditions 1, 2, and 3 were used as the first group of comparison conditions, which were all carried out in sandy, loamy soil (1750 kPa) with a tillage depth of 250 mm, and only the operating speeds were different, with 4 km/h for Condition 1, 6 km/h for Condition 2, and 8 km/h for Condition 3, and the severity comparisons are shown in Figure 22a. The more damaged conditions, 16, 17, and 18, were used as the second group of comparison conditions. All three conditions were performed in clayey soil (2750 kPa) at a tillage depth of 350 mm. Only the operating speeds were different: 4 km/h for Condition 16, 6 km/h for Condition 17, and 8 km/h for Condition 18, and their severity comparisons are shown in Figure 22b.

Figure 22 shows that when the operating speed of the subsoiling operation was increased from 4 km/h to 6 km/h, the damage severity of each measurement point in the first group increased by 2.7~8.2%. The damage severity of each measurement point in the second group increased by 3.8~14.9%. When the operating speed was increased to 8 km/h, the damage severity of each measurement point in the first group increased by 4.7~11.1%. In contrast, the damage severity of each measurement point in the second group increased by 6.0~19.3%. The trend of the damage changes in these two groups of measurement points is the same, and both increase with the increase in operating speed, indicating that the change in operating speed is positively correlated with the change in fatigue damage. At the same time, the damage severity increase values of the two groups of measurement points were compared. It was found that the damage severeness of the second group increased by an average of 13.07% when the operation speed was increased from 4 km/h to 8 km/h, significantly higher than that of the first group, which was 7.42%. This indicates that the greater the tillage depth and soil penetration resistance, the more significant the effect of the change in operating speed on the damage of the subsoiling shovel.

The above results of the severeness analysis indicate that the fatigue damage of the shovel body increases significantly with the increase in soil penetration resistance, tillage depth, and operating speed. By comparing the magnitude of the change in severity, it can be seen that when the remaining two factors are fixed, the degree of influence of tillage depth on the damage is the most obvious, followed by soil penetration resistance, and the operating speed has the slightest influence on the damage. At the same time, the increase in tillage depth does not affect the impact of the other two factors on subsoiling shovel damage.

The experiment further explored the effects of the three individual factors—soil penetration resistance, tillage depth, and operating speed—on the fatigue damage of the subsoiling shovel. Additionally, it analyzed the critical impact of the interaction between these factors under different operating conditions on the severity of the damage, providing strong theoretical support for the optimal matching of operating parameters.

4. Conclusions

In this paper, we studied how the variation in operating conditions affected the overall damage of subsoiling shovels and proposed a complete set of fatigue damage calculation and comparison methods for subsoiling shovels, which mainly included four parts: the real-time measurement of field condition data, extraction and extrapolation of stress load spectrum, linear accumulation damage calculation, and severeness analysis. With soil penetration resistances (1750 kPa and 2750 kPa), tillage depths (250 mm, 300 mm, and 350 mm), and operating speeds (4 km/h low speed, 6 km/h medium speed, and 8 km/h high speed) as the test variables, 18 kinds of test conditions were carried out, and the overall damage of the shovels was analyzed. The influence of parameter changes on the overall damage of subsoiling shovels was analyzed. The results indicate that, within the same subsoiler, the damage sustained by the front row of subsoiling shovels was consistently greater than that of the rear row, with the maximum damage difference reaching 16.86%. Additionally, within the same row, the outer subsoiling shovels always experienced more damage than the inner ones, with the maximum difference reaching 12.87%. Furthermore, when the other two factors were fixed, a single-factor analysis of the soil penetration resistance, tillage depth, and operating speed showed that an increase in the tillage depth had the most significant impact on the severity of damage to the subsoiling shovels, with an average increase of 19.73%. In contrast, the operating speed had the smallest impact, with an average increase of 13.07%.

In practical operations, farmers tend to use deeper depth and faster speed to carry out subsoiling operations, which provides higher work efficiency. However, the increase in the tillage depth and operating speed will increase the load on the subsoiling shovel body, significantly reducing its fatigue life. Therefore, in the actual subsoiling operation, it is necessary to choose the appropriate operating parameters to consider the operating efficiency and the severity of the load. This study can provide useful reference information for optimizing deep loosening operation parameters and extending the fatigue life of deep loosening shovels.