1. Introduction
Steam turbines are powerful pieces of mechanical equipment used for thermal power plants and nuclear power plants that play important roles in the power industry with their high efficiency and advanced performance [
1]. As a part of the electro-hydraulic (EH) oil control system of a steam turbine, high-pressure adjusted hydraulic servomotors are responsible for providing a power source for valve mechanisms (the valve and the device connected to the valve) and play an important role in ensuring the safe and stable operation of steam turbines [
2]. Due to the harsh working environment and strong non-linearity of the system, their faults are difficult to diagnose through traditional regular maintenance schemes and maintenance methods, thus seriously affecting the safe operation of steam turbines [
3]. Steam turbines’ fault statistics show that abnormal shutdown accidents of steam turbines caused by the failure of regulation systems account for one-third of failures. For example, the 200 MW heating unit of a power plant once tripped due to a jammed electro-hydraulic servo valve in the hydraulically adjusted servomotor [
4]. Therefore, it is of great significance to conduct effective fault detection for adjusted hydraulic servomotors.
Yu et al. successfully applied parameter detection, modeling, and simulation methods for the diagnosis of sticking faults in hydraulically adjusted servomotors’ slide valves [
5,
6]. By introducing an expert system, Li et al. proposed a detection method for various faults of slide valves and electro-hydraulic converter jamming faults [
7]. Wang et al. combined expert knowledge and fault information of a DEH system’s equipment and used fault trees to find the causes of the system’s degradation [
8]. Wang et al. combined a PSO (particle swarm optimization) algorithm with a BP (backpropagation) neural network to effectively diagnose faults in servo valves [
9]. Xu et al. verified that the GA (genetic algorithm) can be applied to diagnose faults via simulation [
10]. Feng et al. proposed a strategy for diagnosing jamming faults based on DEH data and achieved good results by verifying the actual data [
11]. Zhang et al. combined the system identification method and GA to realize the diagnosis of sticking faults in hydraulically adjusted servomotors [
12]. However, this research mainly focused on the two types of faults, namely piston rod sticking and the servo valve sticking, and the research method was mainly simulations, which have the disadvantages of studying a single type of fault and a limited research method. With the advent of Industry 4.0 and the era of intelligent manufacturing, diagnostic systems that rely on knowledge and simulation have been unable to meet the requirements of modern intelligent fault diagnoses [
13]. Yang et al. implemented a classification of seven states of hydraulically adjusted servomotor based on the pressure signal of the hydraulically adjusted servomotor using 1DCNN [
14]. Zhou et al. applied the support vector data domain description (SVDD) algorithm to the detection of faults in a hydraulically adjusted servomotor based on the pressure, displacement, and other signals during the operation of the equipment. However, as research has been carried out on only three types of faults (internal leakage of the valve core of the electro-hydraulic servo valve, internal leakage of the seal ring wear at the piston rod of the hydraulic cylinder, and internal leakage of the solenoid valve), the limited fault types easily leads to the problem of a high false-positive rate in the early-warning model [
15]. During operation, the hydraulically adjusted servomotor will cause vibrations due to various forces. When it is in an abnormal operating state, a series of vibration impacts and shock attenuation responses will occur. The resulting vibration signals contain rich information on the faults’ characteristics, which is crucial for the identification and localization of the faults [
16]. Compared with a pressure sensor, a vibration sensor is more convenient to install, has the advantages of being simple test equipment, and is more suitable for industrial scenarios.
In recent years, with improvements in computing power, deep learning, especially CNNs, has become a research hotspot in the field of fault diagnosis [
17]. Compared with the process of traditional machine learning diagnosis, in which each part is conducted independently, CNNs can automatically extract the features, and the feature extraction process is directly oriented to the classification of faults. This end-to-end joint optimization is conducive to improving the generalizability of the model. Since Krizhevsky et al. used a CNN to obtain the best classification in the ImageNet Large-Scale Visual Recognition Challenge in 2012 [
18], CNNs have been widely used in the field of image recognition. After He Kaming proposed the landmark residual network (ResNet) and became the champion of the ILSVRC 2015 Challenge [
19], many scholars have applied ResNet to intelligent fault diagnosis. Wang et al. combined ResNet with a convolutional block attention module and integrated it with a graph convolutional network to propose a novel method of fault diagnosis for rotating machinery using unbalanced datasets [
20]. However, the state signal during a machine’s operation is usually a one-dimensional vector, and converting the original signal into a two-dimensional picture may cause a certain degree of distortion. Therefore, the one-dimensional residual network (1D_ResNet) is directly used to process the original one-dimensional time series signal, which not only ensures the authenticity of the input but also simplifies the network’s structure and reduces the number of parameters, which is conducive to the application of the model in real-time diagnoses of equipment. Liu et al. improved the predictive accuracy of the problem of burring in the process of manufacturing aluminum alloy wheels under conditions of limited samples using 1D-ResNet combined with the migration learning technique [
21]. Tan et al. proposed an improved one-dimensional Inception-ResNet (1D-Inception-ResNet) neural network that enhances the generalization and migration of the model across devices [
22]. However, since the one-dimensional signal may contain various fault features with low to high frequencies, the traditional single-scale 1D_ResNet cannot capture this information at different frequency scales at the same time, which give it a weak ability to extract features. Therefore, by combining the multi-scale method with 1D_ResNet, the model can understand the signal’s characteristics more comprehensively. Liu et al. proposed a multi-scale kernel residual convolutional neural network (MK-ResCNN) to improve the accuracy of diagnoses of faults in electric motors to 94.67% under non-stationary conditions [
23]. However, since MK-ResCNN is connected to the multi-scale nuclear channel only after the convolution operation of the original one-dimensional signal, and the information loss caused by the convolution operation cannot be compensated for by the subsequent multi-scale convolution kernel, this study made improvements based on these points. A multi-scale one-dimensional ResNet (M1D_ResNet) network was designed, which can directly extract the features from the original one-dimensional signals at different scales, thus avoiding the problem of information loss before the information flows into multi-scale channels.
The main task of early warnings of faults in mechanical equipment is that the model should identify the abnormal behavior of the equipment using real-time data. However, the equipment in the industrial field is in a normal working state most of the time, but the faulty state is very random, so compared with data on the normal state of the equipment, which can easily be obtained, the collection of data on the abnormal state is quite difficult. Moreover, even if small amounts of faulty data are obtained, it is difficult to fully describe all faulty states [
24,
25]. In the face of this kind of data or dataset imbalance, the one-class classification (OCC) algorithm has become the key to solving this problem. The term “single classification” was first proposed by C. M. Bishop [
26] and is generally synonymous with anomaly detection (AD) or novelty detection [
27]. In the training stage, the AD model only needs to pay attention to normal data. Compared with a classification model that can only diagnose known faults, it also has great advantages in detecting unknown faults [
28]. As a classical algorithm in the field of detecting anomalies, support vector data description (SVDD) was proposed in 1999 by David M. Tax et al. [
29]. It has been widely used in biochemistry, cloud computing, fault diagnosis, and other fields [
30,
31,
32]. Since the performance of SVDD is greatly affected by the selection of hyperparameters, people have begun to use various meta-heuristic methods combined with SVDD. Zhang et al. adopted the PSO algorithm to optimize SVDD hyperparameters and realized the detection of hidden dangers in rolling bearings [
33]. Xu et al. achieved the detection of unknown faults in substations by optimizing SVDD hyperparameters with the PSO algorithm [
34]. Luo et al. applied PSO-SVDD to diagnose the faults in the fan of a root blower [
35]. However, the PSO algorithm is prone to falling into local optimal solutions in the optimization process [
36], while GA effectively maintains the population’s diversity through crossover and mutation operations, which helps the algorithm avoid premature convergence and improves its global search ability. For example, Guo et al. used GA to dynamically adjust the direction of pruning, that is, to determine how to cut off unwanted parts of the SVDD’s boundary to better distinguish targets and outliers [
37].
As a key part of the EH control of the oil system of turbines, it is of great significance to detect anomalies in the high-pressure hydraulically adjusted servomotor. Deep learning has gradually become a mainstream method in the field of feature extraction for detecting anomalies by virtue of its powerful automatic feature extraction capability. However, there are still some challenges and problems in the current method of feature extraction based on deep learning in detecting the anomalies in hydraulic servomotors:
(1) The existing studies have mainly focused on specific types of faults in hydraulic servomotors, such as jamming of the piston rod or servo valve. The homogeneity of fault types limits the applicability of these methods to a wider range of types of faults in hydraulically adjusted servomotors.
(2) The existing research has relied heavily on simulation data, and this reliance makes it difficult to generalize these methods to industrial sites.
(3) Conventional 1DCNN deep learning models suffer from insufficient capability for feature extraction in capturing multi-band fault features in one-dimensional signals.
(4) The SVDD hyperparameters in the existing SVDD-based studies on detecting anomalies in hydraulically adjusted servomotors were manually selected, which had a certain degree of blindness and could not guarantee the optimal performance of the trained SVDD anomaly detection model.
Therefore, the main contributions of this study are as follows:
(1) In this study, the vibration signal of the hydraulic servomotor was utilized as the data source to realize lossless signal acquisition, which has the advantages of more convenient installation and being more applicable to industrial scenarios.
(2) In this study, an improved M1D_ResNet model was proposed, which can directly process the raw vibration data, eliminating the complex and time-consuming steps of manual feature extraction and signal preprocessing. In addition, it can capture the multi-scale features in the original signal, which prepares high-quality feature data for the subsequent training of the SVDD model.
(3) In this study, GA was used to optimize the SVDD parameters, which overcame the limitations of the traditional SVDD method in the selection of hyperparameters, and finally realized the high-precision detection of anomalies in the hydraulically adjusted servomotor.
The content of the rest of this article is as follows.
Section 2 describes the basic principles of M1D-ResNet and SVDD, the methodological flow of this study, the model’s structure, and the parameter settings.
Section 3 contains the analyses of the experimental data collection in detail and the experimental results. Finally, the conclusions drawn from this study and future research directions are summarized in
Section 4.
4. Conclusions
This study introduced M1D_ResNet for extracting the features of the operational states of high-pressure hydraulically adjusted servomotors, addressing the issue of the traditional 1D_ResNet failing to adequately extract features at different scales. Subsequently, features extracted from the vibration signals of the hydraulically adjusted servomotor during normal operation using M1D_ResNet were used to construct a model for detecting anomalies in a hydraulically adjusted servomotor using SVDD, meeting the needs of practical engineering applications to build such models on the basis of normal data alone. Additionally, the GA was used to automatically optimize the hyperparameters of SVDD, addressing the issue of needing manual intervention for adjusting the hyperparameters. The conclusions are as follows:
(1) Compared with the traditional 1D_ResNet and three other single-scale one-dimensional residual networks, M1D_ResNet achieved the highest classification accuracy. The feature learning effects of each network were analyzed visually using t-SNE, revealing that M1D_ResNet’s feature learning was superior to that of the other networks.
(2) The iterative paths of combinations of hyperparameters during the GA optimization process were visually analyzed, demonstrating that GA could effectively optimize the hyperparameters of SVDD.
(3) Experimental validation was conducted on the SVDD-based detection of anomalies for hydraulically adjusted servomotors, and the decision boundaries were visualized, proving the effectiveness of the SVDD hypersphere model in detecting anomalies in hydraulically adjusted servomotors in different operational states. This lays a solid foundation for constructing models for detecting anomalies in hydraulically adjusted servomotors in practical engineering applications based solely on normal data.
In the future, in-depth research on methods of fusing multi-source information based on vibration and pressure signals can be conducted to improve the accuracy of detecting anomalies in adjusted hydraulic servomotors. In addition, research on reducing the weight of the M1D_ResNet network could be conducted to reduce the size of the model and improve its operational efficiency.