1. Introduction
Aero engines are praised as the “flower of industry” and are the core components that provide power, while the bearing rotor system is praised as the “heart” of the engine [
1]. Condition monitoring is the key to their good function and a basic task of aero engine condition prediction. Accurately predicting the service condition of bearings can not only eliminate a potential safety hazard but also improve the economy during the use of an aero engine [
2]. Affected by engine high speeds, high temperatures, heavy loads, and other harsh working conditions, bearings under extreme working conditions for a long time can experience extreme fatigue and internal extreme wear failure, which will directly cause engine failure, aircraft loss of power, etc., endangering people’s lives and property [
3]. Therefore, it is of great significance to monitor the wear condition of aero engine bearings and provide warnings about their failure to support the stable operation of the engine.
The condition monitoring and wear prediction technologies of mechanical equipment can be mainly divided into physical model-driven methods [
4,
5], statistical model-driven methods [
6,
7], and machine learning-driven methods [
8,
9]. Among them, the methods based on physical models are limited by the complex and changeable operating conditions of modern industrial equipment systems, making it difficult to establish accurate models of complex dynamic systems [
10]. The methods based on statistical models rely on a large amount of historical data and are difficult to apply to different systems and failure modes. The nonlinear expression ability of the methods driven by machine learning is weak, and their prediction effect depends largely on the quality of the extracted features [
11]. With the continuous development of artificial intelligence and deep learning, data-driven bearing condition monitoring has become the mainstream direction of research [
12,
13].
A deep learning neural network provides an efficient and feasible solution for bearing condition monitoring and remaining service life prediction with excellent nonlinear expression ability. For example, Y. Zhao et al. [
14] proposed such a system to enhance the recognition performance of HAR by a 1DCNN recognition algorithm that utilizes a single triaxial accelerometer. Shuzhi Gao et al. [
15] proposed a rolling bearing fault diagnosis model based on adaptive modified complementary ensemble empirical mode decomposition and a one-dimensional convolutional neural network. Zhang, X et al. [
16] proposed a methodology for predicting the remaining usability of rolling bearings. Qin, S et al. [
17] proposed a new type of long short-term memory neural network with macroscopic–microscopic attention. Y. Qin et al. [
18] proposed a new kind of gated recurrent unit neural network with dual attention gates, namely, a gated dual attention unit. Although the above models based on LSTM architecture and their model variants have achieved good prediction accuracy, these models are prone to overfitting due to their many parameters and long training time. Peng, Huachao et al. [
19] proposed a novel multiscale temporal convolutional transformer to simultaneously extract long-term degradation features and local contextual associations directly from raw monitoring data. Cao, Yudong, et al. [
20] proposed a new deep learning framework, a temporal convolutional network with a residual self-attention mechanism, which can learn both time frequency and temporal information from signals. However, a TCN network uses a fully connected neural network to extract the output features, which will lead to parameter redundancy and an increase in computation, thus increasing the complexity and training difficulty of the model.
Vaswani et al. [
21] proposed the famous sequence model called Transformer, which raised the attention mechanism to a new level. Transformer, as an improved attention-based model, greatly exploited the potential of attention in processing serial data. For example, Liu Jie et al. proposed [
22] a feature fusion-based method for bearing RUL prediction. Wei Yupeng et al. [
23] proposed a Siamese LSTM network, which was first introduced to classify degradation stages before predicting the RUL of bearings. Chang Yuanhong et al. [
24] proposed an efficient end-to-end Temporal Flow Transformer for RUL prognostics of rolling bearings.
Bearing wear state monitoring is an effective means to achieve a working state assessment of a machine. Vibration, sound, and temperature are mainly used to monitor the working conditions of rolling bearings [
25]. The change in the key analysis index of a vibration signal indicates a change in the working state of mechanical equipment, but the vibration information is often interfered with by the noise of the mechanical equipment, so it cannot be used to effectively evaluate the bearing wear state in real time [
26]. In addition, because bearing temperature changes are not noticeable when bearing wear faults occur, bearing wear faults cannot be monitored and judged in many cases [
27]. Other methods, such as acoustic or acoustic emission monitoring, can also reflect bearing wear and working conditions, but there are specific limitations to engineering feasibility. In recent years, the field of aero engines began to use many chip sensors for online monitoring of bearing wear, which has the unique advantage of occurring in real time and being fast, comprehensive, and so on. However, due to weak signal fault characteristics and intense background noise of aero engine bearing wear fault, the current popular bearing wear monitoring methods have certain limitations, which are mainly manifested in the following aspects. ① In the high-thrust aero engine bearing rotor system, the bearing installation skew causes the system to appear unbalanced or misaligned failure, resulting in bearing wear failure quickly, especially in harsh environments, such as high load and high speed; bearing wear is apt to evolve into a serious fault and then affect the rotor support system and the whole machine safety and reliability. ② In engineering, the earlier the bearing wear state is found, the possibility of rotor support system failure and equipment shutdown will be significantly reduced. Compared with aero engine fault diagnosis and RUL prediction, there are relatively few studies on condition monitoring. ③ Considering that bearing performance changes are often accompanied by specific nonlinear and non-stationarity characteristics, the secondary redundant feature information in the data will interfere with the feature extraction of the TCN network. It cannot show the ability to make the actual final prediction. In addition, the current deep neural network state monitoring model has the inherent “black box” characteristic, and the mapping relationship between input and output is difficult to accurately describe by the existing model, and there are technical challenges of poor interpretability. Therefore, to solve the above problems, the major novelty points of this study are concluded as follows.
(1) In this paper, a new framework of the residual spatiotemporal feature fusion feature focusing module, named RTCA-Net, is developed in a new way, which focuses on solving the critical problem that it is difficult to accurately monitor: the wear state of aero engine intermediate bearings under special working conditions in practical engineering. This research significantly broadens the application of deep learning models to aircraft-bearing condition monitoring tasks.
(2) Considering the strong time variability of metal scrap particle data of the bearing wear state in a high-thrust aero engine bearing rotor system, a feature extraction module of the residual spatiotemporal structure was innovatively designed and introduced into the proposed RTCA-Net framework, thus improving the framework’s ability to mine the characteristics of the intermediate bearing wear state.
(3) Considering that the wear state data of aero engine intermediate bearings are highly nonlinear, unstable, and susceptible to interference from redundant information, a feature-focusing module is designed in this paper. By adjusting the weight coefficient changes in the training process, the RTCA-Net framework can focus on the more helpful information for monitoring the wear state of inter-shaft bearings from a wealth of information. Thus, the negative influence of redundant information on network learning can be reduced.
(4) Comparative analysis was conducted based on the measured dataset of the same metal debris particles, and the results showed that the proposed RTCA-Net framework achieved good results regarding the RMSE, MAE, and score. Among them, compared with the RTC-Net model, the proposed RTCA-Net framework has improved by 53% and 66% in two indicators, the RMSE MAE. At the same time, compared with the RTC-Net model, the proposed RTCA-Net framework has improved by 59%, 39%, 48%, and 67%, respectively, in four indicators, the eMAE, eRMSE, eNMAE, and eMAPE. Compared with the TCA-Net model, the proposed RTCA-Net framework has improved by 46%, 41%, 59%, and 52%, respectively, regarding the eMAE, eRMSE, eNMAE, and eMAPE. The proposed RTCA-Net framework has good generalization, superiority, and credibility.
4. Experiment
4.1. Bearing Wear Failure Test
In the actual industrial environment, the change in working conditions is complex and challenging to predict, especially in the bearing. In the rotor system of the high-thrust aero engine, the bearing wear of the intermediate bearing can quickly evolve into a severe fault in a harsh environment, such as high loads and high speeds, which affects the safety and reliability of the rotor support system and the whole machine. However, in the actual working environment, due to various conditions, the time series data of bearing condition monitoring will have insufficient information, especially since the intermediary bearing is a vital component of the aero engine rotor system. It has been in harsh environments, such as high temperatures, high speeds, and significant load changes, for a long time. The time series data of intermediate bearing monitoring will have low availability and value density. Specifically, on the one hand, because the intermediary bearing is in the fault state for less time, there are more samples in the healthy state in the monitoring data, and the time from showing damage characteristics to failure is short, resulting in sample imbalance. On the other hand, due to some unique spatial structures, sensor installation difficulties, test costs, and other objective factors, obtaining the timing signal acquisition of intermediary bearings is complicated, and thus, the time series data of aero engine bearing health monitoring will be limited.
To monitor the wear failure of cylindrical roller bearing with pivot intermediate support in a high-pressure rotor support system with a high-thrust aero engine during installation deflection, a simulation test bench for an aero engine cylindrical roller bearing and a high-pressure rotor system were developed for verification and analysis. This test system is mainly composed of five parts: a drive system, a control system, a rotor support system, a data acquisition system, and a lubrication system. The physical diagram of the cylindrical roller bearing high-pressure rotor system simulation test bench is shown in
Figure 5.
(1) Rotor support system: The bearing seat is fixed on the horizontal platform through a bolt connection, a sliding guide rail is installed on both sides of the bearing seat, and the guide rail is fixed on the platform to restrict the axial freedom of the bearing. The central part of the rotor in the rolling bearing high-pressure rotor system is constrained by two bearing seats at both ends of the high-pressure rotor shaft, and then the rolling bearing high-pressure rotor system is supported.
(2) Drive system: The frequency conversion control system is used to drive the motor to provide power for the rolling bearing high-pressure rotor system, and diaphragm coupling is connected with the high-pressure rotor to make the high-pressure rotor and the rolling bearing rotate together and then provide power for the rolling bearing high-pressure rotor system.
(3) Dust signal acquisition: The dust sensor is installed in the lubrication system of the aero engine bearing and the high-pressure rotor system, and the metal dust sensor is used to obtain the dust data in the oil sample for the bearing wear failure state, and then the correlation between the bearing wear state and the dust particle data is analyzed, which can effectively monitor the lubricating oil dust data in real time. The metal chip sensor of the ZXMS model was used in the test, and its physical picture is shown in
Figure 6. The ZXMS series electromagnetic oil wear particle online monitoring sensor can measure the number, size, and properties of metal wear particles in the oil (ferromagnetic and non-ferromagnetic); ferromagnetic particle values are intermediate. By connecting it in series to the oil circuit in the lubrication system, the particle data from the metal chips in the hydraulic and lubrication system can be displayed online in real time. The cylindrical roller bearing mounted on the right side of the rolling bearing high-pressure rotor system is tested. The basic structural parameters of cylindrical roller bearings under test are shown in
Table 2.
(4) Lubrication system: The lubrication system in the rolling bearing high-pressure rotor system simulation test bench mainly includes five parts: a supply oil pump, a return oil pump, an oil tank, a flow-regulating valve, and an oil pipe. In order to facilitate better measurement of the number of metal particles of rolling bearings in lubricating oil, it can be clearly found in
Figure 6 that forced lubrication is adopted in the test process so that the lubrication system in the simulation test bench of the rolling bearing high-pressure rotor system has the characteristics of forced oil intake and oil return.
(5) The setting of test conditions: To better simulate the wear failure of cylindrical roller bearings and obtain the wear failure data of cylindrical roller bearings in a short time, the high-pressure rotor system is set to two different states of unbalanced and unbalanced on the simulation test bench of the aero engine rolling bearing high-pressure rotor system. Among them, the bearing far away from the drive end is the test bearing, and the unbalance of 40 g·mm is added to the unbalance disc of the high-pressure rotor. The left bearing seat is raised by 0.75 mm, and the bearing installation tilt angle is 35°. This chapter selects the cylindrical roller bearing, with the right end as the test bearing. The test conditions are shown in
Table 3 below.
First, let the rotor run continuously at a speed of 2000 r/min for no less than 15 min to avoid the impact of low speed and large current on the motor and ensure that the oil and gas lubrication supply is smooth and stable during operation. The chip sensor can work and collect data. Then, the bearing wear failure and evolution monitoring test was carried out, and the bearing was run for a long time at 3900 r/min. After stopping, wait until the speed is 0, adjust the speed to 4500 r/min, and then run for a long time; wait until the speed is 0 after stopping. The specific test process is as follows:
- (a)
Turn on the power supply and turn on the oil mist lubrication system;
- (b)
Start the motor at 2000 r/min and run stably for 15 min;
- (c)
Increase the rotational speed to the test speed of 3900 r/min, keep the operation for 5 min, and check whether the data of the vibration, temperature, and metal particle dust data collection system are normal.
- (d)
Set the sampling frequency of the vibration acquisition system to 5120 Hz for real-time data acquisition, analysis, and monitoring;
- (e)
Increase the speed to 4500 r/min and continue to collect;
- (f)
Turn off the motor, stop the system, and export the test data.
4.2. Bearing Wear Fault State
According to the test plan, when all the test tests of the aero engine bearing high-pressure rotor system are completed, the aero engine rolling bearing high-pressure rotor system is disassembled, and the surface wear state of the components of the cylindrical roller bearing is shown in
Figure 7.
It can be found in
Figure 7 that, in part of the roller of cylindrical roller bearing, there is an apparent bright band on the surface of the roller of cylindrical roller bearing, and there is also a clear, bright band on the surface of the outer ring of cylindrical roller bearing; the wear marks on the surface of the rolling body are relatively straightforward. At the same time, the inner surface of the outer ring of cylindrical roller bearing has undergone tremendous changes. Small spalling pits were found, indicating that the cylindrical roller bearing had suffered apparent wear failure during the test.
4.3. Bearing Metal Chip Data
The data collection method of metal dust particles is once every 10 s, and the collection time is 5 s. The collected data were constructed into a dataset of 3000 samples and divided into a training set and a test set according to a ratio of 8:2. That is, the first 2400 samples and the remaining 600 samples were, respectively, composed of the training set and the test set of the prediction model. The degradation dataset of the bearing wear state under two special working conditions of an aero engine bearing high-pressure rotor system is shown in
Figure 8.
As can be seen in
Figure 8, after high-speed operation of the aero engine bearing high-pressure rotor system, the accumulation of dust particles will increase linearly with the increase in bearing service time and the continuous change in the bearing wear state. In the two special working conditions of the bearing rotor system, the number of metal particles in the lubricating oil changes obviously, and the number of metal particles increase sharply, which verifies that the status monitoring of lubricating oil particles is in real time. At the same time, it can be found that although the degradation trend of the bearing high-pressure rotor is manifested as a fluctuating state of up and down oscillation, it shows a monotonically increasing trend on the whole and has a certain monotonicity, which can be used as a fundamental feature of bearing health monitoring for trend prediction.
4.4. Metal Scrap Data Training
The metal scrap particle data of the bearing wear state in the aero engine bearing high-pressure rotor system were input into (a) the proposed RTCA-Net framework and (b) the Residual Temporal Convolutional Network (RTC-Net), (c) the Temporal Convolutional Network-Self-Attention Mechanism (TCA-Net), (d) the temporal convolutional network (TCN-Net), (e) the One-dimensional Convolutional Neural Network–Gated Recurrent Unit (1DCNN-GRU-Net), (f) the Bi-directional Gated Recurrent Unit (Bi-GRU-Net), (g) Support Vector Regression (SVR-Net), and (h) Gaussian Process Regression (GPR-Net) and were trained.
The training cycles of the eight models were all set to 1150 cycles, and the loss value transformation curves of the eight model training processes are shown in
Figure 9.
As seen in
Figure 9, the loss value of the proposed RTCA-Net framework tends to converge after the first few iterations and is flat and 0 after 30 iterations. After 1150 training rounds, the training loss value stabilized at about 0.5. It shows that the proposed RTCA-Net framework has a good training effect, and the training time of the model is short. After several iterations, the time sequence characteristic information of the metal scrap particle data can be learned. At the same time, it can be seen that the loss values of the seven models, including RTC-Net, all show a trend of convergence during the training process, but the loss values of the seven models fluctuate considerably. After 1000 training rounds, the loss values of the seven models are not in a state of convergence, and the loss value of TCA-Net is the smallest, which can reach about two values. This shows that the seven models cannot learn the characteristic information of the metal scrap particle data well.
The datasets of metal scrap particles based on the same bearing wear state were input into the eight models, such as the proposed RTCA-Net framework for training. The RMSE value curves of the eight kinds of model training processes are shown in
Figure 10.
As shown in
Figure 10, with the increase in the iteration cycle, the RMSE value of the proposed RTCA-Net framework presents a rapid decline trend. After 50 cycles, the RMSE value gradually tends to 0; after 1150 cycles, the RMSE value stabilizes below one value. Compared with RTC-Net and the other seven models, the proposed RTCA-Net framework has the fastest convergence rate on the verification set. The main reason is that the RTCA-Net framework can extract both the time and frequency domain information faster, so it can converge faster. When the seven models, such as RTC-Net, were trained on the dataset of bearing wear, the RMSE value showed a downward trend as a whole. However, after 1150 cycles, the RMSE value of RTC-Net was relatively low and stable at about 2.5. In summary, compared with the seven models, such as the RTC-Net, the proposed RTCA-Net framework has a faster convergence speed, good model stability, and strong generalization ability. This shows that the proposed RTCA-Net frame used in this paper is more consistent with the actual results and can better solve the problem of bearing wear state evaluation.
7. Conclusions
The intermediate bearings in high-thrust aircraft engines are prone to wear and tear faults, and the current deep neural network models make it difficult to monitor the wear status of bearings accurately. This paper takes a unique approach and develops a new framework for a residual spatiotemporal feature fusion feature focusing module named RTCA-Net. It provides theoretical and technical support for fault monitoring and health management of aircraft engine rotors and bearings. The main conclusions are as follows.
(1) Considering that aviation intermediate bearings operate in harsh environments, such as high loads and high speeds, especially in special operating conditions where the rotor system is unbalanced or misaligned, the proposed RTCA-Net framework achieves accurate prediction of the wear status of intermediate bearings. This study significantly expands the application of deep learning models in aviation-bearing condition monitoring tasks.
(2) Considering the solid time-varying nature of metal debris particle data in the bearing rotor system of high-thrust aircraft engines, an innovative feature extraction module for a residual spatiotemporal structure is designed. The residual idea is introduced into the proposed RTCA-Net framework, which improved the framework’s ability to mine intermediate bearing wear state features and reduced computational resource consumption.
(3) Considering the strong nonlinearity, instability, and susceptibility to redundant information in the wear status data of intermediate bearings in aircraft engines, this paper designs a feature-focusing module that can enhance essential features in the debris particle data of intermediate bearings and suppress the influence of non-important features. The accuracy and generalization performance of the proposed RTCA-Net framework is further improved by weighing the spatiotemporal information in the intermediate bearing data.
(4) Based on comparative analysis of the same metal chip particle dataset measured, the proposed RTCA-Net framework exhibits superior performance compared to previous studies, with high superiority, generalization, and robustness. By introducing the interpretable RTCA-Net framework, the reliability and credibility of mining the wear state characteristics of intermediate bearings in aircraft engines have been achieved.