1. Introduction
As regular power machinery, the diesel engine has superior output torque and fuel economy, which secures its irreplaceable role in the industry, agriculture, and so on. Under worsening energy and environmental crises, many countries are creating stringent legislation for diesel engines [
1]. This presents a challenge for researchers; on the other hand, many technologies are developing rapidly as a result of this opportunity [
2]. The application of new technologies improves the performance of engines, but it also leads to higher complexity, which results in more frequent failure [
3].
Engine fault detection has developed from breakdown maintenance to regular maintenance and is gradually developing into predictive maintenance [
4]. Traditional disassembly fault diagnosis technology is evolving to non-disassembly fault diagnosis. The non-disassembly predictive maintenance depends on collecting and analyzing state information [
5]. The vibration signal is a common choice because of its rich information, high stability, and low cost. For example, Barszcz et al. [
6] used vibration signals to detect the bearing and gear faults of an engine. Benkedjouh et al. [
7] designed a rotating machinery fault prediction and health management method by vibration signals. Furthermore, vibration analysis does not invade the engine block and can detect multiple kinds of faults, so it is currently considered to be one of the strongest potential methods [
8]. The engine has many excitation sources; hence, sensors are usually placed on the block and cylinder head cover to collect vibration signals synthetically. However, the engine vibration signal usually has strong nonlinearity and randomness, and the background noise easily covers the weak features caused by early faults. Therefore, appropriate algorithms should be researched to recognize faults accurately [
9].
The most frequently used method is to begin by processing vibration signals by decomposition, denoising, feature extraction, and feature selection, and then recognizing engine faults by a simple classifier. For example, Wang et al. [
10] decomposed vibration signals by variational mode decomposition (VMD) and selected the intrinsic mode function (IMF) with the highest kurtosis as the sensitive component to extract features; finally, the faults were detected by an extreme learning machine (ELM). The characteristic of this step-by-step method is the low requirement for the classifier. In contrast, the algorithm in every step should be controlled manually. Moreover, the recognition rate highly depends on extracted features. Still, the features usually have strong pertinence, leading to their needing to be adjusted according to specific applications with several attempts and having inferior generalization.
End-to-end fault detection is another attractive method because of its high efficiency and generalization capability. On the other hand, the high-performance classifier is required to analyze complex vibration signals, in which deep belief networks (DBNs) [
11], convolutional neural networks (CNNs) [
12], and recurrent neural networks (RNNs) [
13] are frequently used artificial neural networks (ANNs). The DBN is stacked by several restricted Boltzmann machines (RBMs) and can detect the correlation of high-order data in visual layers by hidden layers. Ma et al. [
14] used the DBN to detect signals compressed by compressive sensing to realize end-to-end fault recognition. Jiang et al. [
15] proposed a DBN model optimized by the locality preserving projection (LPP), which could diagnose bearing faults without manual feature extraction. The CNN could gradually extract local features by convolutional layers, making it more suitable for high-dimensional data. Azamfar et al. [
16] proposed a 2-dimensional CNN model based on signals fused from several sensors to detect gearbox faults end-to-end. The CNN could obtain better results in complex conditions with the pretreated vibration signals; for example, Hasan et al. [
17] employed the CNN to analyze the vibration signals pretreated by the bispectrum for bearing fault detection in various working conditions. The bispectrum is a higher-order spectrum, the discrete Fourier transform of the higher-order cumulant. It has no definite physical significance but could magnify abnormal impact components for the pattern recognition model. As representative RNNs, the long short-term memory (LSTM) and gated recurrent unit (GRU) have advantages in time series analysis. Alrifaey et al. [
18] combined the LSTM with a stacked autoencoder (SAE) to analyze time-dependent vibration signals for electrical gas generator fault detection. Yu et al. [
19] used a stacked denoising autoencoder (SDAE) and the GRU to detect planetary gear faults. However, the gradient descent algorithm used in the DBN, the CNN, and the RNN may easily lead to low convergence speed and local minima. They are also prone to over-fitting with small-size training data. Moreover, the DBN and RNN cannot deal with spatial information because they generally require one-dimensional data. The CNN is usually combined with them, such as the DBN stacked by convolutional RBMs [
20], convolutional LSTM [
21], and multiscale CNN-GRU with attention mechanism (MCNN-AGRU) [
22], whereas their efficiency and accuracy still have room for further improvement.
Jaeger et al. [
23] proposed echo state networks (ESNs), whose basic principle is to take a randomly generated reservoir, instead of hidden layer neurons, as the basic processing unit to transform computing into linear regression. It shows excellent potential in pattern recognition. For example, Long et al. [
24] used the ESN to analyze 3-dimensional printer faults, and Wootton et al. [
25] designed a model by optimized ESN for static pattern recognition. However, the ESN lacks the capability for mining deep spatial information from time and frequency domains. Additionally, the hyper-parameters of the ESN are not clear in the mechanism and must be selected according to prior knowledge [
26]. Zhang et al. [
27] proposed a deep fuzzy ESN by combining fuzzy clustering, and Sun et al. [
28] designed a deep belief ESN model based on the DBN to extract deep features. Unfortunately, they have shortcomings in dealing with high-dimensional samples. Although Ma et al. [
29] proposed a convolutional multi-timescale ESN inspired by the CNN, it still needs larger size training data. The particle swarm optimization (PSO) [
30] and the binary grey wolf optimizer (GWO) [
31] are popular evolutionary metaheuristic algorithms to search for the best result, but they are prone to the local optimum. Inspired by theories of the black hole, the white hole, and the wormhole, Mirjalili et al. [
32] proposed a multi-verse optimizer (MVO). It has been widely used to optimize ANN hyper-parameters because of its better global optimization capability and stability. Faris et al. [
33] employed the MVO to optimize the multi-layer perceptron (MLP) model and obtained the best result compared with several traditional algorithms. Yang et al. [
34] used the MVO for the probability neural network (PNN) optimization to improve the recognition rate of power transformer faults. However, the MVO pours significant computing resources into the global search to avoid the local optimum, which results in insufficient local search and low convergence speed. It is worth improving further for the global and local searches in the MVO to optimize the ESN.
In this paper, a deep ESN model for engine faults end-to-end detection is proposed, and an improved MVO is researched to optimize hyper-parameters of the deep ESN. The main contributions are as follows:
- (1)
A sparse input weight matrix is designed for the ESN. Optimized by fixed convolution kernels and the autoencoder (AE), a deep ESN is proposed.
- (2)
A novel traveling distance rate (TDR) and universe collapse mechanism are proposed for the MVO to improve the local search and speed it up.
- (3)
The bispectrum is employed to transform the one-dimensional time-dependent vibration signal into a two-dimensional matrix with more impact features. An engine fault end-to-end detection model is then built based on the deep ESN, the improved MVO, and the bispectrum.
This paper is organized as follows: the research background and significance are introduced in
Section 1. Fundamental theories of the ESN and the MVO are introduced in
Section 2. The deep ESN model and the improved MVO are proposed in
Section 3. The diesel engine bench test and data collection system are described in
Section 4.
Section 5 introduces the analytical method of vibration signal and the complete framework of engine fault end-to-end detection model. The proposed method is also verified by experimental data in this section. The conclusion and outlook are presented in
Section 6.
4. Experiment
A turbocharged inline 6-cylinder diesel engine designed for heavy-duty vehicles is tested for a high reference value. The main technical parameters of the tested engine are listed in
Table 2.
The diesel engine bench test is shown in
Figure 5a. The support system is composed of a horizontal platform and four air springs, whose natural frequency is below 2 Hz. The engine is connected rigidly with the platform by steel braces. An electrical dynamometer connects with the engine by a carbon-fiber driveshaft outside of the test bench room. Sensors are ICP 621B40 piezoelectric accelerometers produced by PCB Piezotronics, and the data acquisition system is SCM05 LMS Testlab produced by Siemens. An SPSR-115 photoelectric rotating-speed sensor produced by Monarch Electric Co. is placed near the connecting shaft of the dynamometer to synchronize engine speeds and vibration signals.
According to [
3], fuel injection and valve systems are the most frequent failure parts (accounting for about 40%). The injection system fault is divided into three types: abnormal fuel injection timing is selected for simulating the control system failure, abnormal injection quantity for the injector failure, and abnormal rail pressure for the high-pressure common rail failure. The valve clearance faults are selected to simulate wear and carbon deposits in the valve system. In application, the faults should be detected at an early stage. Several early faults are designed, whose details are listed in
Table 3, where “+” and “−” represent increasing and decreasing parameters from the normal condition, respectively, and CA is the crankshaft angle. Testing speeds include 700 r/min, 1300 r/min, 1600 r/min, 2000 r/min, and 2300 r/min, and testing loads include 100%, 75%, and 50%. Data in 15 s are collected under various working conditions, respectively.
Considering that cylinder pressure acts on block and cylinder head directly, and that, moreover, intake and exhaust valves are near cylinder head, six sensors are placed on the Y-direction (the horizontal direction perpendicular to the crankshaft) of the cylinder head cover near the 1st–6th cylinders. Five additional sensors are also placed on the Z-direction (vertical direction) as reference and comparison. The eleven sensor placements are shown in
Figure 5b. The sampling frequency is set as 25.6 kHz based on the fault feature frequency of the testing engine and the Nyquist theorem.