Multi-Objective-Based Intelligent Lubrication System Performance Evaluation Technology for Construction Machinery

Abstract

The infrastructure construction process cannot be separated from construction machinery; it will inevitably produce wear and tear in the work. The level of wear and tear is severe and could cause mechanical accidents. There are safety hazards involved with wear and tear; thus, the study of the lubrication systems of construction machinery is crucial. This paper addresses the problems with the intelligent lubrication systems of construction machinery and establishes a performance evaluation index system for the intelligent lubrication systems of construction machinery by analyzing and selecting appropriate evaluation indexes. Based on the built evaluation system, a performance evaluation model was established based on the hierarchical analysis (analytic hierarchy process, AHP)–entropy weight method and a topological object element model. The feasibility of the model was tested using the example of an off-road mining dump truck. This model analyzes the performance strengths and weaknesses of smart lubrication systems and suggests improvement measures and recommendations for weak links. It also provides a reference for analyzing the performance of smart lubrication systems for other mechanical devices.

1. Introduction

With the accelerated process of modernization and construction, there is an increasing demand for various large-scale engineering construction tools [1]. In the construction process, which is affected by the complexity and variability of working conditions, frequent load changes, and other factors, construction machinery is often in a high-load operation state [2,3]. Therefore, it is necessary to ensure that the construction machinery is well lubricated and is reliable to ensure its regular operation [4]. In recent years, with the development of artificial intelligence, the internet, and other technologies, the manufacturing industry has been gradually changing in the direction of intelligence [5,6]. Construction machinery is essential to modern manufacturing, and its intelligent development is also attracting attention [7]. In the use of construction machinery, the lubrication system is one of its core components. Before the advent of intelligent lubrication, the lubrication field used three traditional lubricants, namely, single-wire progressive lubrication, double-wire and multi-wire lubrication, and double-wire and multi-wire hybrid lubrication. The traditional lubrication systems for construction machinery, with their increasingly obvious shortcomings, have not been able to meet the lubrication needs of modern construction machinery [8,9]. Therefore, intelligent lubrication systems came into being, which combine hydraulic technology, sensor technology, numerical control technology, other technologies, a high degree of intelligence in the control system, monitoring, and detection, and strong human–computer interaction, and they can realize multi-point simultaneous lubrication and can control the amount of oil supplied [10,11,12,13].

Intelligent lubrication systems are further developments in lubrication system intelligence and information technology, and the features of smart lubrication system mainly include the following: all lubrication points are maintained in a completely isolated state without any interference from external factors, and even if one lubrication point is blocked, the other lubrication points are not affected. Each lubrication point independently detects the grease displacement and can immediately reflect the lubrication point failure to the control cabinet. The independent detection of the grease displacement at each lubrication point can relay the lubrication point failure immediately to the control cabinet and directly to the central controller. The real-time monitoring of the lubricant supply, better control of the lubricant spraying volume, and the prevention of lubricant waste can be achieved. With the visualization of the monitoring interface, the user can adjust the cycle time and lubricant supply volume at each lubrication point according to the demand, and the same lubrication system can be adjusted on a large scale. However, in the use of construction machinery, friction-versus-material characteristics, environmental temperature changes, load conditions, and other factors will have an impact on the performance of the intelligent lubrication system [14,15], so it is crucial to evaluate the performance of the intelligent lubrication system, not only to repair the system in advance of potential failures but also to greatly improve the lubrication effect.

Both domestic and foreign research have been carried out on intelligent lubrication systems. For example, as early as 1997, Griffith et al. developed an automatic lubrication system for mining machinery that could detect the system function signal and automatically perform the lubrication system function, improving the efficiency of the mechanical equipment [16]. The centralized lubrication system developed by Germany’s Fukuo Bird Vogel Company significantly improves upon the inadequacy of traditional manual lubrication, saving on lubricant by up to 80% and providing a more convenient service for users [17]. In 2014, Wang [18], of the Nanjing University of Aeronautics and Astronautics, researched and designed a multi-point quantitative lubrication pump structure, which increased the output performance of the lubrication systems for distributed construction machinery. In 2018, Wu [19], of the Overseas Chinese University, proposed the method of working attitude detection for the automatic lubrication systems of excavators, which constitutes a new idea for construction machinery. The working lubrication strategy provides a unique idea. In 2019, Zhou [20] proposed an intelligent lubrication optimization model to solve the problems encountered when there are several port equipment lubrication points that are not easy to maintain and repair. Based on this, he designed an intelligent lubrication equipment health management system, which realizes the on-demand and precise lubrication of port equipment. In 2021, Liu [21], from the Central Plains Institute of Technology (CPIT), by studying the structure of lubrication systems, lubrication control systems, and end detection for research, designed a set of intelligent lubrication systems to realize the lubrication of construction machinery, which provides a new direction for the design of intelligent lubrication systems for construction machinery. In the same year, Lu et al. [22] designed the software part of an intelligent track lubrication system using fuzzy group analysis based on the results of a curve radius and train speed simulation. In 2022, You Yintao [23] and others, in order to solve the problems of roller press lubrication systems using traditional lubrication control methods, proposed an intelligent roller press bearing lubrication system based on PLC and remote control. The system can provide real-time remote monitoring and remote control, and it can detect the lubrication amount and operation status of on-line equipment, which solves the problems of traditional lubrication systems. In 2023, Chai Xiaohui [24] and others proposed a PLC and remote control-based intelligent system for roller press bearing lubrication to solve the problem of using traditional lubrication control methods for roller press lubrication systems. The system is capable of real-time remote monitoring and remote control. It can detect the amount of lubrication and the operational status of the device online, solving the problems of conventional lubrication systems. In the same year, Zheng et al. [25] reviewed the current research status of intelligent technology in construction machinery. They analyzed the current research status of Prognostic Health Management (PHM), environment sensing, and automation control in construction machinery, which indicates that construction machinery has been increasingly converging to intelligence. In the same year, Shi et al. [26] developed a THED lubrication model for coupled bearings in diesel engines based on thermal pressure coupling effects. They experimentally verified that the lubrication characteristic parameter and the dynamical characteristic parameter of the bearing associated with the journal thrust increase significantly when thermal pressure coupling effects are taken into account.

In terms of evaluation methods, traditional evaluation methods mostly use the single-assignment method. For example, in 2020, Luo et al. [27] constructed a model based on fuzzy synthesis evaluation to assess the construction risk of soft soil modified shield machines. It weights risk factors by triangular fuzzy numbers, which helps avoid subjectivity and better reflects the fuzzy nature of risk factors without the need for consistency tests as in hierarchical analysis methods. In 2021, C.X. et al. [28] used the hierarchical analysis method to design the pumping station from the technical and economic aspects, but the hierarchical analysis method is more subjective and is better suited to evaluate subjective indicators and lacks objectivity. However, as evaluation methods become more mature and individual evaluation methods become susceptible to competency or objective influences, comprehensive evaluation methods are born in response. In 2019, Zhao et al. [29] performed a comprehensive evaluation of AC/DC hybrid microgrid planning based on hierarchical analysis and the entropy weighting method. The effectiveness and rationality of the evaluation methods are also demonstrated through examples. The combination of hierarchical analysis method and entropy weight method for evaluation not only improved the objectivity of index weights, but also reduced the errors caused by insufficiency or omission so that the evaluation results are scientific and realistic. In 2022, Xie et al. [30] developed an evaluation model based on fuzzy hierarchical analysis and hierarchical analysis to evaluate the adaptability of shield machines to address the difficulty of efficiently and quantitatively assessing the effectiveness of shield machines during construction. In 2023, Xue et al. [31] constructed an energy efficiency assessment model for substation buildings using AHP and fuzzy synthesis theory. They chose a substation in Shandong, China as a case study to validate the effectiveness and feasibility of the model. However, the model is only applicable to substation buildings and cannot be generalized to other buildings.

Intelligent lubrication systems can monitor lubrication status in real-time, detect lubrication problems in a timely manner, and respond to them, thereby improving lubrication efficiency, prolonging device life, and reducing maintenance costs. [32,33]. While domestic and foreign research on the design of intelligent lubrication systems and their application to various mechanical devices is becoming more mature, research on the performance evaluation techniques of intelligent lubrication systems for construction machinery is also crucial. Until now, the study of evaluation techniques has been relatively decentralized, with research areas limited to one aspect of the lubrication system and few comprehensive evaluations of multiple aspects of the entire lubrication system. In this paper, we propose a multi-objective-based performance evaluation technique for intelligent lubrication systems in construction machinery. Its specific research content is as follows: First, we analyze the factors that affect the regular operation of intelligent lubrication systems of construction machinery, select suitable evaluation metrics, and develop a system of performance evaluation metrics for intelligent lubrication systems of construction machinery. We then develop a multi-objective performance evaluation model for intelligent lubrication systems of construction machinery based on the AHP–entropy weight method and a topological object model. Finally, the established evaluation model is used to evaluate intelligent lubrication systems with examples, and the evaluation results are analyzed to suggest improvements and measures.

2. Construction Machinery Intelligent Lubrication System Performance Evaluation System

2.1. Influence Factors of Intelligent Lubrication System of Construction Machinery

Many different types of influencing factors need to be considered when establishing the performance evaluation index system of intelligent lubrication system for construction machinery. The influencing factors can be divided into the following aspects to be analyzed: (1) equipment characteristics; (2) working environment; (3) component materials; (4) lubricant quality. The following section will discuss these four aspects in detail.

2.1.1. Equipment Characteristics

(1) The size of the load borne by construction machinery directly determines the degree of wear and tear and the demand for lubrication systems. In general, the heavy load of construction machinery on the lubrication system is more dependent [34]. (2) The working conditions of construction machinery include normal operation, bad weather, emergency stops, and sharp turns, etc. Different operating conditions correspond to different lubrication systems. Different operating conditions correspond to different lubrication methods and cycles. (3) The consumption of lubricant is relatively high in new equipment because the internal parts are not sufficiently broken in. In addition, due to the long-term lack of maintenance of old equipment, lubrication system components are prone to failure.

2.1.2. Working Environment

(1) High temperatures tend to reduce the viscosity of the oil, which increases the risk of leakage and reduces lubrication. Low temperatures are the opposite of the above. (2) High humidity accelerates corrosion of metal surfaces and shortens bearing life. Excessive humidity will also increase the suspended particulate matter in the air, aggravating mechanical wear. (3) The lower the air pressure, the lower the amount of lubricant fluid entering the lubrication system, which will have an impact on the operational efficiency of the entire lubrication system.

2.1.3. Component Materials

(1) Friction sub-material selection will have an impact on the performance of the lubrication system, and different materials have different lubrication effects under the same conditions. (2) The sealing performance of the lubrication system is essential to the lubrication effect; therefore, choosing the right sealing material can improve the reliability and efficiency of the system. (3) Different materials have different needs for lubricants; therefore, choosing the right lubricant can improve the lubrication effect of the system. (4) Wear and tear cannot be avoided during the operation of the components, and the selection of suitable wear-resistant materials can enhance the durability and stability of the lubrication system and reduce the cost of repair and maintenance.

2.1.4. Lubricant Quality

(1) The viscosity–temperature curve of the lubricant reflects its fluidity and oxidation resistance, which is of guiding significance in determining the optimum oil temperature and replacement interval [35]. (2) The anti-wear performance of lubricants is closely related to their chemical composition, which is mainly reflected in the extreme pressure additives. (3) The cleaning and dispersing performance of lubricating oil is directly related to the generation of harmful substances such as carbon and varnish produced during fuel combustion, which in turn affects the reliability and life of machinery. (4) During the operation of construction machinery, the viscosity, antioxidant properties, and cleaning and dispersing properties of lubricating oil will be affected by the operating conditions and the external environment and reduced; thus, regular replacement of lubricating oil is essential for the normal operation of the intelligent lubrication system of construction machinery [36].

2.2. Construction of Intelligent Lubrication System Performance Evaluation System for Construction Machinery

The working environment of construction machinery is dusty and harsh [37], meaning that the performance of construction machinery intelligent lubrication system will be affected by the working conditions. In order to guide the construction machinery intelligent lubrication system to achieve the expected lubrication effect, the construction machinery intelligent lubrication system is required to be able to automatically adjust the lubrication cycle, lubrication volume, and other parameters; this ensures that the equipment is always in the best lubrication state and reduces equipment wear and tear, so as to prolong the service life of the equipment [38]. In addition, the intelligent lubrication system also needs to have a certain degree of predictive analysis capability to ensure that it can analyze historical data and machine operating conditions, predict possible failures in advance, and take timely measures for maintenance to avoid equipment damage or failure.

Evaluation indexes are the basis for establishing the evaluation system, and also the most basic constituent elements [39]. In order to build an efficient performance evaluation index system, on the basis of reference to the relevant literature, according to the performance requirements of the intelligent lubrication system of construction machinery and the influencing factors and combined with the attributes and characteristics of the construction machinery itself, through the screening and optimization of the evaluation indexes, the performance evaluation system is finally divided into three layers, namely, the target layer, the intermediate layer, and the factor layer, as shown in

Table 1. Engineering machinery intelligent lubrication system and performance evaluation system.

Objective Layer	Intermediate Layer	Factor Layer
Engineering machinery intelligent lubrication system performance evaluation system	Lubrication effect	Equipment load
		Operating temperature
		Viscosity of the lubricant
		Anti-wear properties of lubricants
		Thickness of oil film
	Fault maintenance	Failure rate
		Mean Time Between Failure (MTBF)
		Mean Time To Repair (MTTR)
	Economic cost	Selection of friction sub-materials
		Selection of lubricants
		Payback period
		Profitability
	Energy saving and reduction of consumption	Lubricant replacement intervals
		Lubricant consumption
		Electricity consumption

. Combining the influencing factors of the intelligent lubrication system of construction machinery, the four indicators of the intermediate layer are first-level indicators, including lubrication effect, fault maintenance, economic cost, energy saving, and consumption reduction. The fifteen factors in the factor layer are secondary indicators [40].

3. Performance Evaluation Model of Intelligent Lubrication System for Construction Machinery

The performance evaluation of intelligent lubrication system of construction equipment can help enterprises discover the lubrication failure of equipment in time. In addition, corresponding maintenance measures can be taken to improve equipment reliability and stability. The selection of indicators should be reasonable and objective and in line with reality, so as to be conducive to obtaining accurate evaluation results. The weight allocation of different indicators will have an impact on the design, optimization, monitoring, fault diagnosis, degree of intelligence, and ease of use of the intelligent lubrication system of construction machinery, which will affect the performance evaluation results of the system. For the construction machinery intelligent lubrication system, performance weighting needs to be in line with the actual construction machinery, but also objective and fair, in line with the intelligent lubrication system in previous years, as well as the operation and maintenance of the characteristics.

This paper aims to provide a practical and feasible performance evaluation method for assessing the operation of intelligent lubrication systems for construction machinery. The construction machinery intelligent lubrication system needs to work properly under the conditions of harsh working environment, high load working condition, a long time operation, variable working conditions, and remote monitoring and control. The system involves multiple factors, and the factors are highly interrelated. The hierarchical analysis method can positively reflect the relationship between the factors within the Construction machinery intelligent lubrication system, but also serves to simplify the evaluation problem. In addition, most of the indicators are qualitative, making it difficult to measure them using quantitative indicators [41]. The entropy weighting method relies on raw data to calculate weights, which are highly objective and accurate [42]. The construction machinery intelligent lubrication system has objective operational data from previous years. For the multi-indicator factors, non-linear problems, the evaluation needs to be from multiple perspectives, multi-faceted, and combined with multiple factors to objectively give a realistic conclusion. Therefore, the comprehensive evaluation method combining AHP and the entropy weight method is chosen to determine the evaluation index weights, and the specific steps are as follows.

3.1. Determination of Indicator Weights

3.1.1. AHP Determines Subjective Weights

Hierarchical analysis was first proposed by Saaty, an American operations researcher in the 1970s, and it is a combination of qualitative and quantitative analysis methods [43]. Its basic thinking is: firstly, based on the evaluation objectives, decompose the objectives into various constituent elements, then according to the degree of correlation and affiliation between elements, construct an evaluation hierarchy from high to low, then compare the factors of each layer, so as to construct a judgment matrix, and then, through the calculation of the hierarchical single ranking and consistency test, calculate the weights of the elements of this layer to the relative importance of the previous layer, and finally carry out a hierarchical total ranking and consistency test. Finally, calculate weights of factors in this layer in terms of their relative importance to the previous layer, and finally carry out total hierarchical ordering and the consistency test [44]. The basic steps of AHP are shown in Figure 1 below.

3.1.2. Entropy Weighting Method to Determine Objective Weights

The entropy weighting method is used to determine objective weights. Entropy is a measure of the degree of disorder in a system [45]. The entropy weight method is an objective assignment method used to determine the degree of dispersion of a certain indicator through the entropy value. The smaller the value of the entropy information, the greater the degree of dispersion of the indicator, and the greater the corresponding weight. This method can effectively reflect the distribution of data and accurately assess the weight of indicators [46]. The entropy weighting method processes the raw data collected and constructs them into an evaluation matrix, which can fully utilize the information contained in the indicators. This can then be used to analyze the variability between indicators by finding entropy values and coefficients of variance. The basic steps of the entropy weighting method are shown in Figure 2 below.

3.1.3. AHP–Entropy Weighting Method to Determine the Combined Weights

The AHP–entropy weighting method to determine the combined weights; AHP relies on the experience of experts and the results obtained are limited by personal preferences, ignoring objective factors, and are prone to incorrectly exaggerate or minimize the impact of certain factors [47]. The entropy weight method is supported by data and has strong objectivity. However, it is easy to ignore the correlation between indicators and focus too much on theoretical derivation to obtain results according to local conditions [48], considering the diversity and complexity of the indicators of the construction machinery intelligent lubrication system and the advantages and disadvantages of the AHP–entropy weight method. Choose to combine the subjective weights obtained by AHP and the objective weights obtained by entropy weight method. The weights of the indicators of the construction machinery intelligent lubrication system are calculated using the comprehensive assignment method. The general formula for the AHP–entropy weighting method to calculate composite weights is:

$$X_{\mathrm{i}}^{}=\frac{W_i+Z_i}{{\displaystyle \sum _{i=1}^m{W}_i+Z_i}}, \mathrm{i}=1,2,\dots m$$

Combined with the actual situation, the formula is improved by reviewing relevant information, and the formula for the integrated weight after improvement is:

$${\displaystyle \sum _{\mathrm{i}=1}^m{W}_i+Z_i=}2$$

$$X_i=\frac{W_i+Z_i}{2}$$

$${\displaystyle \sum _{\mathrm{j}=1}^n{X}_j=1,(}j=1,2,3,\cdots \mathrm{n})$$

3.2. Construction of Extension Matter–Element Model

Topology is a field of mathematics and statistics that describes and analyzes multidimensional data using extension models [49], which can help us understand the characteristics and patterns of objective things more comprehensively. Conversely, the matter–element model is an evaluation model that combines qualitative and quantitative elements [50]. Combining topology and the matter–element model to establish the matter–element extension model for evaluation of the construction machinery intelligent lubrication system. The specific computational procedure for the matter–element extension model is as follows.

3.2.1. Establishment of Object Element Model

An ordered triad R = (N,C,U) is introduced as the basic unit of the descriptor, and the R object element possesses n-dimensional features.

$$R=N,C,U=\left(\begin{array}{ccc}N& C_1& U_1\\ & C_2& U_2\\ \begin{array}{c}\\ \end{array}& \begin{array}{c}⋮\\ C_{\mathrm{n}}\end{array}& \begin{array}{c}⋮\\ U_n\end{array}\end{array}\right)$$

3.2.2. Determine the Classical and Sectional Domains

The classical domain is a matter–element matrix consisting of each evaluation level, evaluation indicator and indicator value interval together, denoted as R₀.

$$R_0=(N,C,U_0)=\left(\begin{array}{ccc}{N}_0& C_1& U_{O1}\\ & C_2& U_{O2}\\ \begin{array}{c}\\ \end{array}& \begin{array}{c}⋮\\ C_{\mathrm{n}}\end{array}& \begin{array}{c}⋮\\ U_{on}\end{array}\end{array}\right)=\left(\begin{array}{ccc}{N}_{\mathrm{ok}}& C_1& (a_{o1},b_{o1})\\ & C_2& (a_{o2},b_{o2})\\ \begin{array}{c}\\ \end{array}& \begin{array}{c}⋮\\ C_n\end{array}& \begin{array}{c}⋮\\ (a_{on},b_{on})\end{array}\end{array}\right)$$

N_ok represents the kth rank, C₁, … C_n represent the 1st to nth indicators, respectively, and U_oi = (a_oi, b_oi) (i = 1, 2, … n) represents the range of quantitative values taken by each rank N_ok to which each indicator C_i belongs.

The nodal domain is an interval of values about C, $U_{\mathrm{p}i}\in (a_{pi},b_{pi})(i=1,2,\cdots n)$, Denoted as R_p.

$$R_{\mathrm{p}}=(N,C,U_p)=\left(\begin{array}{ccc}N& C_1& U_{p1}\\ & C_2& U_{p2}\\ & ⋮& ⋮\\ & C_n& U_{pn}\end{array}\right)=\left(\begin{array}{ccc}N& C_1& (a_{p1},b_{p1})\\ & C_2& (a_{p2},b_{p2})\\ & ⋮& ⋮\\ & C_{\mathrm{n}}& (a_{pn}^{},b_{pn})\end{array}\right)$$

N denotes all evaluation levels.

3.2.3. Determination of the Matrix of Elements to Be Evaluated

According to the identified evaluation indicators, the quantitative values are processed and analyzed, and the results obtained are used to form the element-to-be-evaluated matrix, denoted as R_q.

$$R_{\mathrm{q}}=(N,C,U_q)=\left(\begin{array}{ccc}N& C_1& U_{q1}\\ & C_2& U_{q2}\\ & ⋮& ⋮\\ & C_n& U_{qn}\end{array}\right)$$

U_qi (i = 1, 2, … n) denotes the data obtained from the thing to be tested.

3.2.4. Determine the Correlation Function and the Degree of Correlation

$$K_{\mathrm{i}}(U_{\mathrm{i}})=\left\{\begin{array}{c}\frac{-d_1}{\left|U_{oi}\right|}(U_i\in U_{oi})\\ \frac{d_1}{d_2-d_1}(U_i\notin U_{oi})\end{array}\right.$$

$$d_1=\left|U-\frac{1}{2}(a_{oi}+b_{oi})\right|-\frac{1}{2}(b_{oi}-a_{oi})(i=1,2,\cdots n)$$

$$d_2=\left|U-\frac{1}{2}(a_{pi}+b_{pi})\right|-\frac{1}{2}(b_{pi}-a_{pi})(i=1,2,\cdots n)$$

3.2.5. Calculate the Overall Relevance and Evaluation Rating

The correlation of each level of the integrated evaluation is the weighted summary of the correlation function of each indicator. The expression is as follows:

$$K(\mathrm{t})={\displaystyle \sum _{i=1}^n{w}_i{K}_i(U_i)}$$

w_i is the indicator weight of X_i, indicating that the evaluation unit Ro belongs to evaluation level k, the level with the highest degree of relevance.

In summary, the comprehensive evaluation method combining the AHP–entropy weight method and topologizable object element model takes multiple factors into account, avoids the limitations of a single evaluation method, and improves the comprehensiveness of analysis and evaluation. The flow of the performance evaluation of the intelligent lubrication system for construction machinery is shown in Figure 3 below.

4. Case Study

4.1. Overview of Cases

The construction of machinery chassis travel systems and working devices involves various mechanical mechanisms such as sliding and rotating. In the course of work, wear and tear on the machinery is inevitable. Additionally, wear and tear that comes with friction is a complex process of many factors interacting with each other. Wear and tear is not only a major cause of material consumption, but also an important factor in the deterioration of the technical condition of the equipment and its impact on its lifetime. The wear process can be divided into three phases: break-in wear phase, normal wear phase, and sharp wear phase [51]. Lubrication is one of the most important means of preventing and delaying wear and other forms of part failure. In recent years, there have been a growing number of studies on wear and lubrication of construction machinery, both at home and abroad. For example, in 2020, K.M. et al. [52] optimized the setting of the milling parameters of the Inconel 718 alloy based on the multi-objective optimization technique. They optimized surface roughness, tool wear, and material-removal rates during DFA machining of Tinalox, Hyperlox and HSN2 coatings. In 2022, Zhang et al. [53] applied a combination of linear and numerically specific moving least squares methods to construct an approximate model of a sliding frame. They optimized the design of mining dump truck vehicles for lightweighting with the objectives of compartment mass, stiffness, and sixth-order frequency, which helped improve the economy, dynamics, braking, and safety of the dump trucks. In the same year, Huang Xu [54] established a platform for electromechanical–hydraulic integrated systems by analyzing and designing a centralized hydraulic lubrication system for construction machinery and using Simulink to build a fuzzy adaptive control strategy. Finally, the performance under different working conditions has been studied using a heavy digger as an example.

As heavy earth-moving engineering machinery, off-highway mechanical drive mining dump trucks are commonly used in mines, quarries, and other industrial areas. They are special vehicles used to transport and dump materials such as ore, sand, and gravel. Their lubrication system is complex, involving multiple key factors and indicators; thus, a comprehensive evaluation of it would be representative. [55]. Off-highway mechanically driven mining dump trucks are widely used in the engineering field, and optimization of their intelligent lubrication systems can improve their operational efficiency and reduce energy consumption. Therefore, the performance of the intelligent lubrication system of a certain model of off-highway mechanically driven mining dump truck is evaluated to demonstrate the feasibility of the evaluated model. Proposals and measures to improve weaknesses in its intelligent lubrication system have been put forward. A physical diagram of an off-highway mechanically driven mining dump truck is shown in Figure 4 below.

4.2. Performance Evaluation

4.2.1. Establishment of the Evaluation System

When evaluating the performance of an intelligent lubrication system for a certain model of mechanically driven off-highway mining dump truck, a performance evaluation system for mechanically driven off-highway mining dump truck is established as shown in Figure 5 below.

4.2.2. Calculation of the Weights of the Evaluation Indicators

(1)

AHP to determine subjective weights

①

Constructing judgment matrices

By consulting experts and university professors associated with the construction machinery industry, the judgment matrix was constructed using expert survey methods based on understanding the significance of each metric. The values of each judgment matrix are recorded in

Table 2. Intermediate level indicators.

Intermediate Level Indicators	B1	B2	B3	B4
Lubrication effect	1	3	4	5
Fault maintenance	1/3	1	2	3
Energy saving and reduction of consumption	1/4	1/2	1	1/2
Economic cost	1/5	1/3	2	1

,

Table 3. Lubrication effect.

Lubrication Effect	B11	B12	B13	B14	B15
Equipment load	1	1/2	1/4	1	1/2
Operating temperature	2	1	3	4	2
Viscosity of the lubricant	4	1/3	1	2	1
Anti-wear properties of lubricants	1	1/4	1/2	1	1/2
Thickness of oil film	2	1/2	1	2	1

,

Table 4. Fault maintenance.

Fault Maintenance	B21	B22	B23
Failure rate	1	2	5
MTBF	1/2	1	1
MTTR	1/5	1	1

,

Table 5. Energy saving and reduction of consumption.

Energy Saving and Reduction of Consumption	B31	B32	B33
Lubricant replacement intervals	1	5	4
Lubricant consumption	1/5	1	2
Electricity consumption	1/4	1/2	1

and

Table 6. Economic cost.

Economic Cost	B41	B42	B43	B44
Selection of friction sub-materials	1	2	3	4
Selection of lubricants	1/2	1	5	3
Payback period	1/3	1/5	1	1
Profitability	1/4	1/3	1	1

.

②

Calculate weights

When computing the weights, the judgment matrix is first hierarchically unordered, and then the largest characteristic roots and eigenvectors of the judgment matrix are computed and consistency tests are performed; finally, the total hierarchical ordering is performed. The obtained weights of the primary and secondary indicators are shown in

Table 7. AHP subjective weighting results.

Level 1 Indicators	Weighting at Level 1	Secondary Indicators	Secondary Weights	Subjective Weights
Lubrication effect	0.5384	Equipment load	0.1074	0.0578
		Operating temperature	0.3818	0.2056
		Viscosity of the lubricant	0.2206	0.1188
		Anti-wear properties of lubricants	0.0967	0.0521
		Thickness of oil film	0.1935	0.1042
Fault maintenance	0.2326	Failure rate	0.6008	0.1397
		MTBF	0.2290	0.0533
		MTTR	0.1702	0.0396
Energy saving and reduction of consumption	0.1019	Lubricant replacement intervals	0.6768	0.0690
		Lubricant consumption	0.1925	0.0196
		Electricity consumption	0.1307	0.0133
Economic cost	0.1270	Selection of friction sub-materials	0.4476	0.0568
		Selection of lubricants	0.3391	0.0431
		Payback period	0.1069	0.0136
		Profitability	0.1064	0.0135

below.

(2)

Entropy weighting method to determine objective weights

①

Constructing the raw data evaluation matrix

In order to ensure the objectivity of the weights of the metrics, objective data on the operation of the intelligent lubrication system of a certain model of mechanically driven dump truck used in off-highway mining was collected over a period of years. Entropy weighting is applied to determine the weights of the metrics, thus ensuring that the evaluation results are more in line with the actual situation. Based on the hands to be evaluated, relevant staff were consulted, and a large amount of information was collected to obtain the score raw data for the indicators over the years, as shown in

Table 8. Data for previous years.

	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
B11	0.98	0.98	0.72	0.97	0.71	0.89	0.97	0.94	0.73	0.78	0.82	0.91
B12	0.99	0.73	0.84	0.87	0.93	0.91	0.79	0.92	0.88	0.79	0.87	0.89
B13	0.83	0.98	0.92	0.81	0.76	0.9	1.00	0.99	0.76	0.82	0.89	0.93
B14	0.86	0.73	0.92	0.97	0.85	0.89	0.84	0.78	0.71	0.79	0.93	0.98
B15	0.91	0.83	0.72	0.83	0.95	0.92	0.87	0.88	0.81	0.98	0.93	0.90
B21	0.9	0.93	0.86	0.81	0.87	0.79	0.74	0.89	0.98	0.97	0.92	0.83
B22	0.8	0.84	0.91	0.73	0.75	0.81	1.00	0.92	0.95	0.82	0.84	0.79
B23	0.99	0.95	0.86	0.81	0.79	0.94	0.91	0.73	0.86	0.83	0.79	0.80
B31	0.93	0.71	0.75	0.72	0.79	0.81	0.85	0.97	0.92	0.82	0.9	0.81
B32	0.95	0.91	0.76	0.74	0.89	1.00	0.93	0.95	0.91	0.82	0.86	0.99
B33	0.89	0.91	0.84	0.76	0.78	0.93	0.92	0.95	0.89	0.81	0.77	0.90
B41	0.88	0.85	0.98	0.92	0.91	0.78	0.77	0.83	1.00	0.94	0.9	0.85
B42	0.99	0.95	0.89	0.84	0.75	0.79	0.97	0.92	0.89	0.83	0.73	0.79
B43	0.98	0.95	0.91	0.85	0.77	0.73	0.89	0.86	0.9	0.91	0.97	0.94
B44	0.83	0.89	0.85	0.79	0.82	0.94	0.90	0.91	0.98	0.85	0.84	0.87

below. The scores obtained for each indicator range from 0 to 100 and the values are uniformly planned to be in the range of 0.01–1 for computational ease. The data from the counted calendar year are used as the raw data evaluation matrix.

②

Calculate weights

First, the data are normalized using excel in order to obtain the normalization matrix. The characteristic weight, information entropy, and coefficient of variation of each metric are then calculated. Finally, the weight of each index is calculated using formula (1), and the results are shown in

Table 9. Results of objective weight calculation by entropy weight method.

Level 1 Indicators	Secondary Indicators	Objective Weights
Lubrication effect	Equipment load	0.0888
	Operating temperature	0.0490
	Viscosity of the lubricant	0.0886
	Anti-wear properties of lubricants	0.0671
	Thickness of oil film	0.0399
Fault maintenance	Failure rate	0.0521
	MTBF	0.0772
	MTTR	0.0672
Energy saving and reduction of consumption	Lubricant replacement intervals	0.0810
	Lubricant consumption	0.0578
	Electricity consumption	0.0795
Economic cost	Selection of friction sub-materials	0.0700
	Selection of lubricants	0.0736
	Payback period	0.0462
	Profitability	0.0655

below.

$$W_{\mathrm{j}}=\frac{d_j}{\mathrm{k}-{\displaystyle \sum _{}^{}{E}_j}}(j=1,2,\dots n)$$

(1)

(3)

Calculate combined weights

The integrated weights of the metrics are calculated by using the formula for computing the integrated weights in Section 3, by substituting the subjective weights obtained from the AHP calculation and the objective weights obtained from the entropy weighting method, and the results are shown in

Table 10. Combined weights.

Name of the Indicator	Subjective Weights	Objective Weights	Combined Weights
Equipment load	0.0578	0.0888	0.0733
Operating temperature	0.2056	0.0490	0.1273
Viscosity of the lubricant	0.1188	0.0886	0.1037
Anti-wear properties of lubricants	0.0521	0.0671	0.0596
Thickness of oil film	0.1042	0.0399	0.0721
Failure rate	0.1397	0.0521	0.0959
MTBF	0.0533	0.0772	0.0653
MTTR	0.0396	0.0672	0.0534
Lubricant replacement intervals	0.0690	0.0810	0.0750
Lubricant consumption	0.0196	0.0578	0.0387
Electricity consumption	0.0133	0.0795	0.0464
Selection of friction sub-materials	0.0568	0.0700	0.0634
Selection of lubricants	0.0431	0.0736	0.0584
Payback period	0.0136	0.0462	0.0299
Profitability	0.0135	0.0655	0.0395

below.

4.2.3. Comprehensive Evaluation

(1)

Determination of classical and nodal domains

In order to make an evaluation of the advantages and disadvantages of the performance of the intelligent lubrication system of off-highway mining mechanically driven dump trucks, three evaluation levels are set up, namely qualified, good, and excellent, and both the knuckle domain and the evaluation model set up have 15 features which are C₁, C₂, C₃, … C₁₅. For the classical domain of each index and the knuckle domain, we have:

$$\begin{gathered} R_1=\left(\begin{array}{ccc}{N}_1& C_1& (\mathrm{0.0.8})\\ & C_2& (0,0.8)\\ & ⋮& ⋮\\ & C_i& (\mathrm{0.0.8})\\ & ⋮& ⋮\\ & C_{15}& (\mathrm{0.0.8})\end{array}\right)R_2=\left(\begin{array}{ccc}{N}_2& C_1& (0.8,0.9)\\ & C_2& (0.8,0.9)\\ & ⋮& ⋮\\ & C_{\mathrm{i}}& (0.8,0.9)\\ & ⋮& ⋮\\ & C_{15}& (0.8,0.9)\end{array}\right) \\ {R}_3=\left(\begin{array}{ccc}{N}_3& C_1& 0.9,1\\ & C_2& 0.9,1\\ & ⋮& ⋮\\ & C_{\mathrm{i}}& 0.9,1\\ & ⋮& ⋮\\ & C_{15}& 0.9,1\end{array}\right)R_{\mathrm{p}}=\left(\begin{array}{ccc}{N}_{\mathrm{p}}& C_1& (0,1)\\ & C_2& (0,1)\\ & ⋮& ⋮\\ & C_i& (0,1)\\ & ⋮& ⋮\\ & C_{15}& (0,1)\end{array}\right) \end{gathered}$$

(2)

Determine the material element to be evaluated R_q

The data for each indicator are based on the data in Table 8, and the data for 2021 are used as a proxy to create the matrix of elements to be evaluated, R_q.

$$R_{\mathrm{q}}=\left[\begin{array}{ccc}\mathrm{q}& C_1& 0.91\\ & C_2& 0.89\\ & C_3& 0.93\\ & C_4& 0.98\\ & C_5& 0.90\\ & C_6& 0.83\\ & C_7& 0.79\\ & C_8& 0.80\\ & C_9& 0.81\\ & C_{10}& 0.99\\ & C_{11}& 0.90\\ & C_{12}& 0.85\\ & C_{13}& 0.79\\ & C_{14}& 0.94\\ & C_{15}& 0.87\end{array}\right]$$

(3)

Calculating relevance and determining evaluation ratings

When calculating the degree of correlation, it is necessary to continually calculate the distance between the values of each indicator and each grade in order to determine the value of the correlation function that determines the evaluation grade. The computations are large and cumbersome; thus, MATLAB and EXCEL interactive localization methods are used to compute the correlation degree, correlation function, and evaluation grade. The correlation and integrated correlation and evaluation score results are shown in

Table 11. Correlation of each evaluation indicator with each level.

Name of the Indicator	N1	N2	N3	Evaluation Rating of Indicators
Equipment load	−0.5500	−0.0100	0.1000	excellent
Operating temperature	−0.4500	0.0100	−0.0833	good
Viscosity of the lubricant	−0.6500	−0.3000	0.3000	excellent
Anti-wear properties of lubricants	−0.9000	−0.8000	0.2000	excellent
Thickness of oil film	−0.5000	0	0	good
Failure rate	−0.1500	0.3000	−0.2917	good
MTBF	0.0125	−0.0455	−0.3437	pass
MTTR	0	0	−0.3333	pass
Lubricant replacement intervals	−0.0500	0.1000	−0.3214	good
Lubricant consumption	−0.9500	−0.9000	0.1000	excellent
Electricity consumption	−0.5000	0	0	good
Selection of friction sub-materials	−0.2500	0.5000	−0.2500	good
Selection of lubricants	0.0125	−0.0455	−0.3437	pass
Payback period	−0.7000	−0.4000	0.4000	excellent
Profitability	−0.3500	0.3000	−0.1875	good
Comprehensive correlation	−0.3819	−0.0460	−0.0801

below.

The results of the calculations lead to the following conclusions:

Looking at the evaluation ratings as a whole, the maximum correlation for 2021 is −0.0460, with a rating of good. This indicates that the smart lubrication system operated well in 2021 and performance was not affected. From the evaluation scores of the metrics in Table 11, the performance of the smart lubrication system in 2021 was good overall, specifically, mostly excellent and good, but there are individual metrics that qualify. For example, the average trouble-free time, lubricant selection, etc., indicate that the performance of intelligent lubrication systems needs to be further improved. Judging by the weights of each metric in Table 10, the main factors that affect the performance of intelligent lubrication systems are the working environment temperature, lubricant viscosity, failure rate, lubricant replacement cycle, and equipment load. High temperatures can lead to increased wear due to the high lubricant viscosity. Too low or too high lubricant viscosity can affect the normal operation of the machine. In addition, the failure rate and length of the lubricant replacement cycle will also directly impact the service life and maintenance costs of intelligent lubrication systems. Therefore, when designing and applying an intelligent lubrication system, special attention should be paid to the impact of these factors on system performance. For example, grease sensors with higher sensitivity can be used to better monitor the grease state and reduce the impact of high or low lubricant viscosity on mechanical devices. The use of high-precision measurement devices for real-time monitoring and recording of wear and tear, in order to detect and respond to problems in a timely manner, allows the maintenance of components to be extended from passive to predictive maintenance. When choosing a lubricant, it is necessary to take into account the working environment of the construction machinery, the lubrication components and the mechanical maintenance cycle, so as to ensure the regular operation of the lubrication system of the construction machinery, extend the life of the machinery, and increase the efficiency of the work.

5. Summary and Outlook

Construction machinery often operates under high loads and harsh environments, which place high demands on lubrication systems. Construction machinery requires regular maintenance and service, and lubrication is an important part of this; however, frequent maintenance and service work can increase operating costs and time costs. Moreover, the construction industry is increasingly demanding improved energy efficiency and environmental protection, and lubrication systems need to be continuously optimized in order to reduce energy consumption and environmental impact. Intelligent lubrication systems can accurately apply lubricants according to the working load and working conditions of the device, avoiding excessive or insufficient lubrication and achieving energy-efficient optimization. This can also enable remote monitoring and management of the lubrication system of construction machinery through Internet technology to improve maintenance efficiency and operation management. In addition, it can collect, store, and analyze data on lubrication work, provide predictive maintenance based on data analysis, and reduce unplanned downtime and maintenance costs. By evaluating the performance of intelligent lubrication systems, it is possible to continuously improve and optimize the lubrication system to increase the performance and competitiveness of the device. The following conclusions can be obtained by combining the findings of the whole paper:

• By analyzing intelligent lubrication systems of construction machinery and screening the indicators, we have constructed a performance evaluation system applicable to smart lubrication systems of construction machinery. The system comprehensively describes the characteristics of an intelligent lubrication system for construction machinery while reducing the interference of irrelevant indicators, which can be applied effectively in practice.
• Performance evaluation of intelligent lubrication systems for construction machinery using the AHP–entropy weight method and topological matter–element model. We have developed a model suitable for the performance evaluation of intelligent lubrication systems for construction machinery. This model combines subjective and objective aspects and can obtain more accurate evaluation results, which is promising for applications.
• Based on the results obtained from the evaluation model, we analyze and conclude that the operating environment temperature, lubricant viscosity and failure rate have a large impact on the performance of the intelligent lubrication system for off-highway mining dump trucks, and suggest improvements. The model was reasonable, scientific, and adequate through example verification.

There are some differences in the working environment, structure, size, and operation of different construction machinery. In this paper, we have developed a performance evaluation system for intelligent lubrication systems for construction machinery, but how to select suitable evaluation metrics or modify the evaluation metrics for different construction machinery is a problem that needs to be considered. In addition, the established evaluation model is only applicable to construction machinery and not to other types of machinery, which brings certain limitations. The working environment of construction machinery is generally harsh, and the data required for the evaluation using the model are susceptible to objective conditions; thus, how to ensure the correctness of the data is also a consideration.