A novel bearing intelligent fault diagnosis framework under time-varying working conditions using recurrent neural network

An, Zenghui; Li, Shunming; Wang, Jinrui; Jiang, Xingxing

doi:10.1016/j.isatra.2019.11.010

Cited by 159 publications

(64 citation statements)

References 36 publications

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“…• Prone to noise RNN [35] • Parameter sharing is consistent throughout the program • Stacking into deep models is not possible • Long data cannot be interpreted accurately AE [36] • Resultant based on data and not on pre-defined filters • Less complex…”

Section: Architecturementioning

confidence: 99%

Deep Learning for Fault Diagnostics in Bearings, Insulators, PV Panels, Power Lines, and Electric Vehicle Applications—The State-of-the-Art Approaches

et al. 2021

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Deep learning (DL) is an exciting field of interest for many researchers and business. Due to a massive leap in DL based research, many domains like Business, science and government sectors make use of DL for various applications. This work puts forward the importance of DL and its application in a few critical electrical segments. Initially, an introduction to Artificial Intelligence (AI) and Machine Learning (ML) is presented. Then the need for DL and the popular architectures, algorithms and frameworks used are presented. A summary of different techniques used in DL is outlined, and finally, a review on the application of deep learning techniques in some popular electrical applications is presented. Five critical electrical applications, namely identification of bearing faults, hot spots on the surface of PV panels, insulator faults, an inspection of power lines and Electric vehicles have been considered for review in this work. The primary aim of this work is to present chronologically, a survey of different areas in which it applies DL along with their architectures, frameworks and techniques to provide a deeper understanding of DL for widespread use in real-time applications.

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Section: Architecturementioning

confidence: 99%

Deep Learning for Fault Diagnostics in Bearings, Insulators, PV Panels, Power Lines, and Electric Vehicle Applications—The State-of-the-Art Approaches

et al. 2021

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“…We compared the proposed MLKDCE-PBiLSTM with five advanced methods. They are a DCAE network with five-layer convolutional network [ 15 ], BiLSTM network [ 24 ], LSTM with multiple CNN [ 23 ], MSCNN [ 40 ], LeNet-5 with a new convolutional neural network proposed by Wen [ 41 ]. The six methods adopt the same training strategies in the overall experiments.…”

Section: Performance Verificationmentioning

confidence: 99%

“…The structure of BiLSTM can simultaneously utilize the information of past moments and future moments, which makes the final prediction more accurate than LSTM. An et al [ 23 ] utilized CNN-based LSTM for fault feature extraction of the bearing under time-varying working conditions. Rao et al [ 24 ] utilized convolutional BiLSTM to accurately realize fault diagnosis of rotating machinery.…”

Section: Introductionmentioning

confidence: 99%

Multilocation and Multiscale Learning Framework with Skip Connection for Fault Diagnosis of Bearing under Complex Working Conditions

Ban

Wang

et al. 2021

Sensors

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Considering various fault states under severe working conditions, the comprehensive feature extraction from the raw vibration signal is still a challenge for the diagnosis task of rolling bearing. To deal with strong coupling and high nonlinearity of the vibration signal, this article proposes a novel multilocation and multikernel scale learning network based on deep convolution encoder (DCE) and bidirectional short-term memory network (BiLSTM). The former multifeature learning network of this article proposed combines the skip connection and the DCE network. The network can automatically extract and fuse global and local features from different network depth and time scales of the raw vibration signal. Then, the former network as the input of the latter network is fed into the feature protection layer for further mining sensitive and complementary features. Consequently, the proposed network scheme can perform well in generalization capability. The performance of the proposed method is verified on the two kinds of bearing datasets. The diagnostic results demonstrate that the proposed method can diagnose multiple fault types more accurately. Also, the method performs better in load and speed adaptation compared with other intelligent fault classification methods.

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“…However, the fly in the ointment is that the signal processing method requires a lot of prior knowledge and manpower [6]. With the rise of deep learning, it is gradually applied to solve the problem of online intelligent fault diagnosis because it can intelligently extract fault features and classify them [7].…”

Section: Introductionmentioning

confidence: 99%

Siamese Distinguishing Features Attentional Enhancement Transfer Fault Diagnosis Method for Variable Rotational Speed

Xu¹,

et al. 2024

IEEE Access

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Transfer learning is widely used in artificial intelligence fault diagnosis field because it can solve the problem of label missing in rotating parts at varying speeds. However, the domain adaptive method in transfer learning is not suitable for real transfer fault diagnosis scenarios, and the adaptive enhancement of fault characteristics is not realized in the transfer process. To solve these thorny problems, a novel method called Siamese Distinguishing features Attentional Enhancement Transfer fault diagnosis (SDAET) has been proposed. The body of SDAET model adopts the dual-branch convolutional neural network architecture with shared weights. It mainly uses the contrast loss function in the siamese feature contrast network to extract the domain invariant features at two rotational speeds, and then applies it to the transfer diagnosis at other rotational speeds, so as to meet the more real transfer diagnosis scenarios. The distinguishing features attentional enhancement network is designed to adaptively enhance the differentiated domain invariant features at different rotational speeds. Furthermore, various feature visualization techniques are used to further explain the features learned from the black box neural network. The diagnostic results on two kinds of test datasets show that the proposed model has higher diagnostic accuracy. The technique of siamese distinguishing features attentional enhancement provides a new and better way to solve the transfer diagnosis problem.

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A novel bearing intelligent fault diagnosis framework under time-varying working conditions using recurrent neural network

Cited by 159 publications

References 36 publications

Deep Learning for Fault Diagnostics in Bearings, Insulators, PV Panels, Power Lines, and Electric Vehicle Applications—The State-of-the-Art Approaches

Deep Learning for Fault Diagnostics in Bearings, Insulators, PV Panels, Power Lines, and Electric Vehicle Applications—The State-of-the-Art Approaches

Multilocation and Multiscale Learning Framework with Skip Connection for Fault Diagnosis of Bearing under Complex Working Conditions

Siamese Distinguishing Features Attentional Enhancement Transfer Fault Diagnosis Method for Variable Rotational Speed

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