Intelligent Diagnosis of Subway Traction Motor Bearing Fault Based on Improved Stacked Denoising Autoencoder

Xu, Yanwei; Li, Chen; Xie, Tancheng

doi:10.1155/2021/6656635

Cited by 6 publications

(3 citation statements)

References 19 publications

(17 reference statements)

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“…Fu-Lin et al [12] combined simulation and time-frequency analysis methods to study the application of autoencoders in bearing fault diagnosis, resulting in stable diagnostic outcomes. Xu et al [13]introduced an improved stacked denoising autoencoder method for fault diagnosis of metro traction motor bearings, effectively extracting deep features even under complex operating conditions. Lu et al [14] utilized deep autoencoders to learn and extract fault data features, followed by the use of a Softmax classifier for fault classification.…”

Section: Related Workmentioning

confidence: 99%

Novelty Detection and Fault Diagnosis Method for Bearing Faults Based on the Hybrid Deep Autoencoder Network

et al. 2023

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In the event of mechanical equipment failure, the fault may not belong to any known category, and existing deep learning methods often misclassify such faults into a known class, leading to erroneous fault diagnosis. In order to address the challenge of identifying new types of faults in mechanical equipment fault diagnosis, this paper proposes a novelty detection and fault diagnosis method for bearing faults based on a hybrid deep autoencoder network. Firstly, a hybrid deep autoencoder network with one input and two outputs was constructed. The original data were then fed into the network to obtain its low-dimensional representation and reconstructed data. By setting a threshold based on the reconstruction error, novel class faults can be detected, while known faults can be classified based on low-dimensional features. Experimental results demonstrate that the proposed method achieves a recognition accuracy of 98.59% (100%) for novel class identification (known fault classification) on the CWRU bearing dataset, 96.79% (98.53%) on the Paderborn dataset, and 84.34% (97.03%) on the MFPT dataset. Therefore, the hybrid deep autoencoder network not only accurately detects unknown types of faults but also effectively classifies known fault types, demonstrating excellent fault identification and classification capabilities.

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Section: Related Workmentioning

confidence: 99%

Novelty Detection and Fault Diagnosis Method for Bearing Faults Based on the Hybrid Deep Autoencoder Network

et al. 2023

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“…Rolling bearings are critical components of mechanical equipment, among which the traction motor bearings of metro vehicles operate in prolonged elevated temperatures, electrical corrosion, and alternating impacts from gear meshing and wheel-rail contact, which can lead to localized damage, resulting in abnormal vibration in traction motors and impacting the operation status of other vehicle components through the transmission system, and subsequently threatening the operation safety of metro vehicles [1][2][3]. However, there is limited data on fault bearings collected during the actual operation of metro vehicles within a restricted timeframe, and sometimes only normal bearing data is available.…”

Section: Introductionmentioning

confidence: 99%

Simulation data-driven fault diagnosis method for metro traction motor bearings under small samples and missing fault samples

Bi,

Liao,

et al. 2024

Meas. Sci. Technol.

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Traction motor bearings are crucial for guaranteeing the safe operation of metro vehicles. However, in the metro traction motor bearing fault diagnosis, there are usually problems of small samples and missing fault samples, leading to inaccurate results. Therefore, a novel bearing fault diagnosis method utilizing a track-vehicle-bearing coupled dynamic model and the IDCG-MAMCNN model is proposed in this paper. The IDCG-MAMCNN model combines an improved deep convolutional generative adversarial network (IDCGAN) with a multi-scale convolutional neural network with mixed attention (MA-MCNN). Specifically, simulation data is first provided by the coupled dynamic model to supplement missing fault samples. Secondly, the IDCGAN, along with a training method that involves pre-training models with simulation samples and fine-tuning models with experimental samples, is introduced to generate high-quality samples and augment experimental samples under small samples. Lastly, the MA-MCNN serves as the classification model, trained with the augmented dataset comprising experimental, simulation, and generated samples. The fault diagnosis performance of the proposed method is evaluated on the experimental samples of two bearing datasets under small samples and various conditions of missing fault samples. It has been demonstrated by the experimental results that the proposed method exhibits robust fault diagnosis performance and generates high-quality samples under small samples and missing fault samples. Furthermore, the proposed method showcases its adaptability to different operation speeds.

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“…Deep learning models (DLMs) have been used widely in the areas of finance, natural language processing, and image processing [30][31][32][33]. For condition monitoring of a rotating machine, there exist a variety of DLMs based fault diagnosis frameworks, such as stacked denoising autoencoder [34], recurrent neural network [35], long short term memory (LSTM) networks [36], gated recurrent unit network [37], and convolutional neural network (CNN) [37,38]. One of the deep learning models, CNN, is a famous model because of its visual understanding [39].…”

Section: Introductionmentioning

confidence: 99%

Gearbox Fault Identification Framework Based on Novel Localized Adaptive Denoising Technique, Wavelet-Based Vibration Imaging, and Deep Convolutional Neural Network

2021

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This paper proposes an accurate and stable gearbox fault diagnosis scheme that combines a localized adaptive denoising technique with a wavelet-based vibration imaging approach and a deep convolution neural network model. Vibration signatures of a gearbox contain important fault-related information. However, this useful fault-related information is often overwhelmed by random interference noises. Furthermore, the varying speed of gearboxes makes it difficult to distinguish the fault-related frequencies from the interference noises. To obtain a noise-free signal for extraction of fault-related information under variable speed conditions, first, a new localized adaptive denoising technique (LADT) is applied to the vibration signal. The new localized adaptive denoising technique results in optimized vibration sub-bands with negligible background noise. To obtain fault-related information, the wavelet-based vibration imaging approach (WVI) is applied to the denoised vibration signal. The wavelet-based vibration imaging approach decomposes the vibration signal into different time–frequency scales, these scales are reflected by a two-dimensional image called a scalogram. The scalograms obtained from the wavelet-based vibration imaging approach are provided as an input to the deep convolutional neural network architecture (DCNA) for extraction of discriminant features and classification of multi-degree tooth faults (MDTFs) in a gearbox under variable speed conditions. The proposed scheme outperforms the already existing state-of-the-art gearbox fault diagnosis methods with the highest classification accuracy of 100%.

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Intelligent Diagnosis of Subway Traction Motor Bearing Fault Based on Improved Stacked Denoising Autoencoder

Cited by 6 publications

References 19 publications

Novelty Detection and Fault Diagnosis Method for Bearing Faults Based on the Hybrid Deep Autoencoder Network

Novelty Detection and Fault Diagnosis Method for Bearing Faults Based on the Hybrid Deep Autoencoder Network

Simulation data-driven fault diagnosis method for metro traction motor bearings under small samples and missing fault samples

Gearbox Fault Identification Framework Based on Novel Localized Adaptive Denoising Technique, Wavelet-Based Vibration Imaging, and Deep Convolutional Neural Network

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