1. Introduction
As a critical power source, diesel engines are an irreplaceable part of heavy industry, agriculture, nuclear power, and other fields. It is common that diesel engines are subject to fault because of their complex internal structure and harsh operating environments [
1,
2,
3]. This may lead to failure of the entire system if the fault is not diagnosed in time, threatening the operator’s safety and causing great economic loss. For example, the valve train is a particularly important part of the diesel engine, which is mainly composed of intake valve, exhaust valve, rocker arm, pushrod, and tappet. A healthy valve train guarantees correct timing of the valve, that is, intake and exhaust at the time of default. Besides, a normal valve train clearance is also vital for thermal compensation. However, because of the impact of various vibrations sources, the valve clearance frequently trends to increase due to component wear after too many operating hours, reducing the efficiency of the diesel engine. It can even deteriorate into a valve fracture fault, reducing reliability [
4,
5,
6,
7]. Therefore, research on diesel engine health monitoring and fault diagnosis is valuable and urgently needed. Fortunately, related research work is extensively investigated and many algorithms are developed to diagnose fault based on thermal parameters, vibration signals, acoustic emission signals, or some other signals [
8,
9,
10].
The basic scheme of fault diagnosis generally consists of three successive stages: data acquisition, feature extraction, and decision making. Feature extraction, which is manually performed with the help of signal processing methods and statistic methods, plays a critical role in the performance of the diagnosis. It is a very common technical route to perform time domain, frequency domain, and time-frequency domain analysis based on vibration signals obtained by installing acceleration sensors. There are many existing methods based on the above, such as Fourier transform (FT), short-time Fourier transform (STFT), continuous wavelet transform (CWT), Wigner Ville distribution (WVD), Choi–Williams distribution (CWD), singular value decomposition (SVD), empirical mode decomposition (EMD), variational mode decomposition (VMD), and sparse time-frequency analysis (STFA). In [
11], STFT, WVD, and CWD were utilized, respectively, for detailed scrutiny of vibrations generated by an under-load engine, and the root mean square (RMS) and kurtosis of vibration signals were calculated to perform the fault diagnosis of injectors. In [
12], discriminative non-negative matrix factorization (DNMF) was carried out to capture the features of time-frequency image, which were then passed to k-nearest neighbors (KNN) to implement the diagnosis of the diesel valve fault. In [
13], STFT and CWT were developed to mine the features of the time domain signals, and the maximum, mean, and energy of the engine vibration were found increased significantly in the frequency band of 3–4.7 kHz when the piston has a scratching fault. In [
14], an improved VMD was proposed for signal decomposition, and then instantaneous energy distribution-permutation entropy was implemented for feature extraction. Unfortunately, in spite of many existing algorithms on feature extraction, these processing and feature extraction algorithms are difficult to absolutely adapt to specific signals. CWT has achieved satisfactory results in the time-frequency analysis of non-stationary signals, but the wavelet basis and decomposition scale need to be manually selected, which restricts the performance of self-adaptation. EMD can adaptively decompose signals into several intrinsic mode functions according to their characteristics. Nevertheless, it has the flaw of the mode mixing and the end effect. The number of modes in VMD plays a critical role in the decomposition result, but the parameter is selected based on prior-knowledge. In general, signal processing based on expertise or prior-knowledge will inevitably lead to information missing. The experience limitation of the fault mechanism can make the performance of these algorithms unsatisfactory.
Fortunately, with the deepening application of machine learning in the industrial field, some emerging intelligent fault diagnosis algorithms have demonstrated inspiring results [
15]. Among them, the emergence of the stacked autoencoder (SAE) has brought us a new method of feature extraction, that is, the hierarchical extraction of deep abstract features directly from the raw signal via unsupervised or semi-supervised training [
16]. Compared to traditional manually chosen features, the features extracted by SAE adaptively retain the most representative information based on the characteristics of a signal. In addition, a lot of signal processing work is simplified due to the end-to-end network structure. Therefore, SAE has been employed by many scholars to address machinery fault diagnosis, and the performance is remarkable. In [
17], the features of fault bearing under different crack sizes were obtained with the help of SAE, and not surprisingly, the fault diagnosis of bearing crack was effectively performed. In [
18], a SAE feature mining method was developed to diagnose gearbox fault from frequency-domain signals, and two gearbox datasets were collected to confirm the effectiveness of the proposed method. In [
19], a stacked denoising autoender (SDAE) was established to carry out layer-wise feature extraction from vibration signals in the frequency domain. Then, principal component analysis was used to verify the feature extraction ability. In [
20], a SDAE was introduced to automatically learn in-depth features from complex data. Combined with an improved regularization method, the model achieved an impressive result. Moreover, the regularization parameters changed depending on the layers of the SDAE. However, most of the above studies are the introduction and application of SAE, or the improvement of the structure in SAE, or the optimization of specific hyper-parameters. Because of the deep network structure of SAE, there are many hyper-parameters that can have a significant influence on the performance of fault diagnosis. The multi-parameter joint optimization in the SAE model is still an urgent task for current research. Besides, when SAE is utilized for reciprocating mechanical feature extraction, the test data that is quite different from the training data in the operating condition may lead to an insufficient performance. Therefore, the new network design of SAE still should be investigated to adjust to multiple operating conditions.
In this paper, a variational stacked autoencoder with harmony search optimizer (HSO–VSAE) method is carried out to handle the diagnosis of a diesel engine under multiple operating conditions. The proposed algorithm is verified by the case of a 12-cylinder diesel engine data that consists of a normal intake valve train fault and an exhaust valve train fault under 12 operating conditions. The main contributions of this paper can be summed up as follows: (1) In order to overcome the dependence of prior knowledge in traditional feature extraction, a novel variational stacked autoencoder (VSAE) model is proposed to mine high-level features adaptively from angular domain signals considering multiple operating conditions. (2) The dropout technique and the batch normalization technique are introduced to get over the flaw of over-fitting and the internal covariate shift problem in the deep layers of SAE. (3) In order to achieve the multi-parameter joint optimization in the proposed model, the harmony search optimizer (HSO) algorithm is adopted to adjust the network structure for a good match with the given data set.
The rest organization of the paper is as follows. In
Section 2, the theoretical background of VSAE and HSO is introduced.
Section 3 presents the proposed HSO–VSAE method for the fault diagnosis of diesel engine.
Section 4 describes the diesel engine test rig and the data obtained.
Section 5 details the experimental results of valve train diagnosis based the proposed method. The conclusions are presented in
Section 6.
3. The Proposed HSO–VSAE Method
The deep network structure of VSAE may introduce some flaws while improving the performance of feature extraction, such as the over-fitting problem and the “internal covariate shift” problem. Therefore, the dropout technique [
27] and the batch normalization (BN) technique are introduced in this study to improve the generalization ability and convergence rate of the VSAE model. With the help of these techniques, the VSAE can converge quickly after fine-tuning, extract features hierarchically, and classify faults. However, there is still a concern that needs to be emphasized: some hyper-parameters in this model have a significant influence on the feature extraction effect, and inappropriate parameter values will degrade the performance of the VSAE model. On the other hand, fixed hyper-parameter settings are obviously not reasonable enough for different data sets. By introducing the HSO algorithm into the VSAE, the network structure can be adaptively adjusted according to different data sets, further improving the high-level feature extraction performance of the VSAE. The HSO–VSAE fault diagnosis model is shown in
Figure 5.
In this HSO–VSAE fault diagnosis frame, a VSAE model is built first as in
Figure 3. Then, the decoder part of the VSAE is discarded when the pre-training is completed, and a fully connected layer and a softmax layer are sequentially added to classify the faults. After adding the dropout layers—the BN layers—fine-tuning is performed to complete the training of the VSAE model. For the node drop ratios of the first two layers and the number of hidden layer nodes, a total of five important hyper-parameters in the VSAE, namely,
Dropout1,
Dropout2,
m,
l, and
k. The initial range of values is determined based on the experiment or existing knowledge. Then, the solution space and harmony memory are established according to the range of each parameter. Finally, five hyper-parameters are adaptively chosen by the HSO algorithm in accordance with the distribution characteristics of data set. Therefore, the proposed HSO–VSAE method can achieve both satisfactory fault diagnosis performance and generalization capability even under varying operating conditions.
4. Test Rig and Data Description
The TBD234 test rig is a 12-cylinder piston diesel engine designed by Henan Diesel Engine Industry Co. Ltd., (Luoyang, China). The key parameters of the diesel engine are shown in
Table 1. A BH5011 acceleration sensor, designed by Beijing Bohua Technology Co. Ltd., (Beijing, China), is installed in the vertical direction of the cylinder head of each cylinder to obtain a cylinder vibration signal. To obtain an instantaneous speed signal and a key phase signal, a Bently 3300 XL eddy current sensor is respectively installed in the radial direction and the axial direction of the flywheel, which is directly connected to the crankshaft. Test data is collected by the BH5000E monitoring system designed by Beijing Bohua Technology Co. Ltd., (Beijing, China). The sampling is performed in the time domain at a sampling frequency of 51.2 kHz. The main components of the test rig are shown in
Figure 6.
As a kind of motion mechanism, the valve train of the diesel engine often has an abnormal increase in the valve clearance due to wear, resulting in economic loss. Thence, the abnormality of the intake and exhaust valve clearance is the target fault of this experiment. In the healthy state, the normal intake valve clearance and exhaust valve clearance should be 0.3 mm and 0.5 mm, respectively. In this experiment, the clearances are adjusted by the feeler gauge to simulate different degrees of valve fault. The experiment simulates seven states of valve clearance, that is, normal state (NS), serious intake valve fault (SIF), serious exhaust valve fault (SEF), serious intake and exhaust valves fault (SIE), minor intake valve fault (MIF), minor exhaust valve fault (MEF), and minor intake and exhaust valves fault (MIE). The specific fault valve train clearance setting is summarized in
Table 2. In this experiment, the vibration data of 12 operating conditions are collected along with 80 data files for each operating condition. Therefore, 960 data files are obtained for each kind of fault. The detailed operating conditions are shown in
Table 3.
For the TBD234 diesel engine, one operating cycle consists of 720 degrees in angular domain no matter how the conditions change. For time domain signal, the length of each data file in each cycle is different under different operating conditions, which does not meet the needs of the model input. Therefore, the instantaneous speeds of engine are first obtained with the help of an eddy current sensor; then, the angular domain signals are obtained by resampling the time domain signal to the phase of 0~720° by linear interpolation. For each operating condition, the length of each data file is fixed at 3600. The envelopes are extracted in the time domain by the second order extremum method and then also resampled to the angular domain, as shown by the red lines in
Figure 7. The frequency domain signals are obtained by a Fourier transform of the time domain signal.
Figure 7 shows the angular domain signals, the frequency domain signals, and the envelopes in seven fault categories at 1500 rpm and 1300 N·m.