An Improved Method of EWT and Its Application in Rolling Bearings Fault Diagnosis

The feature extraction of wheelset-bearing fault is important for the safety service of high-speed train. In recent years, sparse representation is gradually applied to the fault diagnosis of wheelset-bearing. However, it is difficult for traditional sparse representation to extract fault features ideally when some strong interference components are imposed on the signal. Therefore, this paper proposes a novel feature extraction method of wheelset-bearing fault based on the wavelet sparse representation with adaptive local iterative filtering. In this method, the adaptive local iterative filtering reduces the impact of interference components effectively and contributes to the extraction of sparse impulses. The wavelet sparse representation, which adopts L1-regularized optimization for a globally optimal solution in sparse coding, extracts intrinsic features of fault in the wavelet domain. To validate the effectiveness of this proposed method, both simulated signals and experimental signals are analyzed. The results show that the fault features of wheelset-bearing are sufficiently extracted by the proposed method.

“…e derivation process of simplification from (7) to (8) and ( 9) is presented briefly in this part. According to the principle of SB [28], (7) is solved by…”

Section: Appendixmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Feature Extraction Method of Wheelset-Bearing Fault Based on Wavelet Sparse Representation with Adaptive Local Iterative Filtering

Zhan

Lin

Huang

et al. 2020

“…PSO-BP neural network fault diagnosis is mainly divided into three parts: the determination of the neural network structure, the PSO-BP algorithm training network model, and the diagnosis process of the test sample [12], as shown in Figure 3.…”

Section: Pso-bp Neural Network Fault Diagnosismentioning

confidence: 99%

Fault Diagnosis of High-Power Tractor Engine Based on Competitive Multiswarm Cooperative Particle Swarm Optimizer Algorithm

Xiao

Wang

et al. 2020

With the rapid development of high-power tractor, the fault diagnosis of high-power tractor has become more and more important for ensuring the operating safety and efficiency. PSO is an iterative optimization evolutionary algorithm, which can iterate through different particles to find the optimal solution. However, there is only one population in the standard PSO algorithm, and the information exchange between the populations is relatively single, which can easily lead to the stagnation of the development of the population. In this paper, due to high-power tractor diesel engine fault complexity, fault correlation, and multifault concurrency, a multigroup coevolution particle swarm optimization BP neural network for diesel engine fault diagnosis method was proposed. First, the USB-CAN device was used to collect data of 8 items of the diesel engine under five different working conditions, and the data was parsed through the SAE J1939 protocol; then, the BP neural network was reconstructed, and a competitive multiswarm cooperative particle swarm optimizer algorithm (COM-MCPSO) was used to optimize its structure and weights. Finally, the data of optimized neural network under five different fault conditions show that, compared with BP neural network and PSO optimized BP neural network, the fault diagnosis of COM-MCPSO optimized BP neural network not only improves the network training speed, but also enhances generalization ability and improves recognition accuracy.

“…Vibration signal analysis is a commonly used method in mechanical fault diagnosis to extract valuable features that provide internal machine information from collected chaotic signals [2]. For example, Ahmed et al [3] used discrete wavelet and wavelet packet transforms to filter the acquired signals, processed wavelet coefficients in the time and frequency domains, extracted various features, and classified the surface roughness of mechanical faults in cutting tests.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Method Based on Multidomain Heterogeneous Information Entropy Fusion and Model Self-Optimisation

Song

Bai

Zhang

et al. 2022

Incomplete diagnostic information, inadequate multisource sensor information, weak diagnosis models, and subjective experience result in difficulty in predicting rotating machinery faults. To overcome these limitations, we proposed a multiple domain and heterogeneous information entropy fusion model based on an optimisation of bearing fault diagnosis. The spatiotemporal approach uses a multiscene domain fusion strategy based on heterogeneous sensors (HSMSF) to extract feature fusion strategies and analyses the characteristics of the bearing fault features by multichannel processes with convolutional neural networks to vibration signals. After the mapping of multiple quality characteristics, the high-quality features are combined with each other, and the adaptive entropy weighted fusion method is used to analyse and make decisions on sensor information from different detection points. Nineteen key model parameters that were required for HSMSF construction were selected by adaptive optimisation using the chaos elitist modified sparrow search algorithm (CEI-SSA), and a self-learning diagnostic model that is suitable for multiple detection points was constructed. The validity and feasibility of the proposed fault diagnosis method were verified experimentally on two common reference-bearing datasets, CWRU and IMS, and compared with other fault diagnosis methods.