Multisensor bearing fault diagnosis based on one-dimensional convolutional long short-term memory networks

Hao, Shijie; Ge, Feng-Xiang; Li, Yanmiao; Jiang, Jiayu

doi:10.1016/j.measurement.2020.107802

Cited by 155 publications

(57 citation statements)

References 29 publications

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“…Failure diagnosis was performed using a sliding-window-technique-based LSTM. For rolling-bearing failure monitoring, Hao et al [25] proposed a multisensor diagnostic framework using 1D-CNN-LSTM, 1D-CNN for feature extraction and LSTM for classification. The effectiveness of this approach was compared to that of support vector machines (SVMs), k-nearest neighbors (KNN), backpropagation neural networks (BPNNs), and CNNs.…”

Section: Introductionmentioning

confidence: 99%

Gearbox Failure Diagnosis Using a Multisensor Data-Fusion Machine-Learning-Based Approach

Habbouche

Benkedjouh

Benbouzid

2021

Entropy

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Failure detection and diagnosis are of crucial importance for the reliable and safe operation of industrial equipment and systems, while gearbox failures are one of the main factors leading to long-term downtime. Condition-based maintenance addresses this issue using several expert systems for early failure diagnosis to avoid unplanned shutdowns. In this context, this paper provides a comparative study of two machine-learning-based approaches for gearbox failure diagnosis. The first uses linear predictive coefficients for signal processing and long short-term memory for learning, while the second is based on mel-frequency cepstral coefficients for signal processing, a convolutional neural network for feature extraction, and long short-term memory for classification. This comparative study proposes an improved predictive method using the early fusion technique of multisource sensing data. Using an experimental dataset, the proposals were tested, and their effectiveness was evaluated considering predictions based on statistical metrics.

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Section: Introductionmentioning

confidence: 99%

Gearbox Failure Diagnosis Using a Multisensor Data-Fusion Machine-Learning-Based Approach

Habbouche

Benkedjouh

Benbouzid

2021

Entropy

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“…The equipment vibration signals under negative conditions and the signals under normal operating conditions often have different characteristics. Therefore, vibration signals analysis for power equipment can distinguish different operating conditions of equipment, thereby helping people to diagnose faults and find the cause [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Different types of sound signals are closely related to the characteristics of different space or objects.…”

Section: Introductionmentioning

confidence: 99%

Signals classification based on IA-optimal CNN

Zhang

et al. 2021

Neural Comput & Applic

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The versatility of the existing A-optimal-based CNN for solving multiple types of signals classification problems has not been verified by different signals datasets. Moreover, the existing A-optimal-based CNN uses a simplified approximate function as the optimization objective function instead of precise analytical function, which affects the signals classification accuracy to a certain extent. In this paper, a classification method called IA-optimal CNN is proposed. To improve the stability of the classifier, the trace of the covariance matrix of the weights of the fully connected layer is used as the optimization objective function, and the parameter optimization model is established without any simplification of the optimization objective function. In addition, to avoid the difficulty of not being able to obtain the analytical expression formula of the partial derivative of the inverse matrix with regard to the networks parameters, a novel dual function is introduced to transform the optimization problem into an equivalent binary function optimization problem. Furthermore, based on the above analytical solution results, the parameters are updated using the alternate iterative optimization method and the accurate weight update formula is deduced in detail. Five signals datasets are used to test the universality of the IAoptimal CNN in signals classification fields. The performance of IA-optimal CNN is showed, and the experimental results are compared with the existing A-optimal-based classification algorithm. Lastly, the following conclusion is proved theoretically: For the A-optimal-based CNN, the trace of the covariance matrix will continue to decrease and approach a convergence value in the iterative process, but it is impossible for the networks to strictly reach the A-optimal state.

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“…In recent years, deep learning methods have been successfully applied to the health monitoring of mechanical equipment [ 17 , 18 , 19 , 20 , 21 ]. Hao et al Proposed an one-dimensional convolutional long short-term memory (LSTM) networks, where both the spatial and temporal features of multisensor measured vibration signals are extracted and then jointed for better bearing fault diagnosis [ 22 ]. Xue et al proposed a fault diagnosis method based on a deep convolution neural network (DCNN) and SVM, it achieved good accuracy [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearings Based on a Residual Dilated Pyramid Network and Full Convolutional Denoising Autoencoder

Shi

Chen

et al. 2020

Sensors

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Intelligent fault diagnosis algorithm for rolling bearings has received increasing attention. However, in actual industrial environments, most rolling bearings work under severe working conditions of variable speed and strong noise, which makes the performance of many intelligent fault diagnosis methods deteriorate sharply. In this regard, this paper proposes a new intelligent diagnosis algorithm for rolling bearing faults based on a residual dilated pyramid network and full convolutional denoising autoencoder (RDPN-FCDAE). First, a continuous wavelet transform (CWT) is used to convert original vibration signals into time-frequency images. Secondly, a deep two-stage RDPN-FCDAE model is constructed, which is divided into three parts: encoding network, decoding network and classification network. In order to obtain efficient expression of data denoising feature of encoding network, time-frequency images are first input into the encoding-decoding network for unsupervised pre-training. Then pre-trained coding network and classification network are combined into residual dilated pyramid full convolutional network (RDPFCN) for parameter fine-tuning and testing. The proposed method is applied to bearing vibration datasets of test rig with different speeds and noise modes. Compared with representative machine learning and deep learning method, the results show that the algorithm proposed is superior to other methods in diagnostic accuracy, noise robustness and feature segmentation ability.

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Multisensor bearing fault diagnosis based on one-dimensional convolutional long short-term memory networks

Cited by 155 publications

References 29 publications

Gearbox Failure Diagnosis Using a Multisensor Data-Fusion Machine-Learning-Based Approach

Gearbox Failure Diagnosis Using a Multisensor Data-Fusion Machine-Learning-Based Approach

Signals classification based on IA-optimal CNN

Fault Diagnosis of Rolling Bearings Based on a Residual Dilated Pyramid Network and Full Convolutional Denoising Autoencoder

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