Combination bidirectional long short-term memory and capsule network for rotating machinery fault diagnosis

Deep learning (DL) plays a very important role in the fault diagnosis of rotating machinery. To enhance the self-learning capacity and improve the intelligent diagnosis accuracy of DL for rotating machinery, a novel hybrid deep learning method (NHDLM) based on Extended Deep Convolutional Neural Networks with Wide First-layer Kernels (EWDCNN) and long short-term memory (LSTM) is proposed for complex environments. First, the EWDCNN method is presented by extending the convolution layer of WDCNN, which can further improve automatic feature extraction. The LSTM then changes the geometric architecture of the EWDCNN to produce a novel hybrid method (NHDLM), which further improves the performance for feature classification. Compared with CNN, WDCNN, and EWDCNN, the proposed NHDLM method has the greatest performance and identification accuracy for the fault diagnosis of rotating machinery.

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Fault Diagnosis for Bearing Based on 1DCNN and LSTM

Shock and Vibration ◽

10.1155/2021/1221462 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Haibin Sun ◽

Shichao Zhao

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Short Term Memory ◽

Rotating Machinery ◽

Fault Identification ◽

Short Term ◽

Term Memory ◽

One Dimensional ◽

Memory Network ◽

Long Short Term Memory

Condition monitoring and fault diagnosis of the bearing are essential for the smooth operation of rotating machinery. In this paper, an end-to-end intelligent fault diagnosis method for bearing combining one-dimensional convolutional neural network with long short-term memory network (1DCNN-LSTM) is proposed for the deficiencies of existing fault diagnosis methods. First, the proposed method takes one-dimensional fault data directly as input. Second, one-dimensional convolutional neural network (1DCNN) is used for self-adaptively extracting robust features from the original bearing signal, and more features are extracted while ensuring the validity and saliency of the extracted features by combining maximum pooling and average pooling layers to downsample features. Then, long short-term memory network (LSTM) is used to learn the temporal dependencies among features. At last, fault identification is achieved. 1DCNN-LSTM does not require any manual feature extraction, and the errors caused by reliance on expert experience and incomplete information in traditional feature extraction methods are avoided. The results show that the proposed classifier with good generalization performance not only diagnoses the category of fault quickly and accurately under different load conditions but also achieves an average fault identification accuracy of 99.95%. For its powerful learning abilities, this method can also be applied to the bearing fault diagnosis of rotating machinery in many fields.

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Fault Diagnosis of Motor Bearings Based on a Convolutional Long Short-Term Memory Network of Bayesian Optimization

IEEE Access ◽

10.1109/access.2021.3093363 ◽

2021 ◽

pp. 1-1

Author(s):

Zhen Li ◽

Yang Wang ◽

Jianeng Ma

Keyword(s):

Fault Diagnosis ◽

Short Term Memory ◽

Bayesian Optimization ◽

Short Term ◽

Term Memory ◽

Memory Network ◽

Long Short Term Memory

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Gearbox Fault Diagnosis Classification with Empirical Mode Decomposition Based on Improved Long Short-Term Memory

2021 IEEE 6th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) ◽

10.1109/icccbda51879.2021.9442505 ◽

2021 ◽

Author(s):

Sheng-Nan Chen ◽

Feng Liu ◽

Chang-Xia Gao ◽

Jinyang Li

Keyword(s):

Fault Diagnosis ◽

Empirical Mode Decomposition ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Mode Decomposition ◽

Long Short Term Memory ◽

Diagnosis Classification

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Extraction of local and global features by a convolutional neural network–long short-term memory network for diagnosing bearing faults

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/09544062211016505 ◽

2021 ◽

pp. 095440622110165

Author(s):

Zhang Chao ◽

Wang Wei-zhi ◽

Zhang Chen ◽

Fan Bin ◽

Wang Jian-guo ◽

...

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Condition Monitoring ◽

Short Term Memory ◽

Vibration Signal ◽

Short Term ◽

Global Features ◽

Term Memory ◽

Long Short Term Memory ◽

Lstm Network

Accurate and reliable fault diagnosis is one of the key and difficult issues in mechanical condition monitoring. In recent years, Convolutional Neural Network (CNN) has been widely used in mechanical condition monitoring, which is also a great breakthrough in the field of bearing fault diagnosis. However, CNN can only extract local features of signals. The model accuracy and generalization of the original vibration signals are very low in the process of vibration signal processing only by CNN. Based on the above problems, this paper improves the traditional convolution layer of CNN, and builds the learning module (local feature learning block, LFLB) of the local characteristics. At the same time, the Long Short-Term Memory (LSTM) is introduced into the network, which is used to extract the global features. This paper proposes the new neural network—improved CNN-LSTM network. The extracted deep feature is used for fault classification. The improved CNN-LSTM network is applied to the processing of the vibration signal of the faulty bearing collected by the bearing failure laboratory of Inner Mongolia University of science and technology. The results show that the accuracy of the improved CNN-LSTM network on the same batch test set is 98.75%, which is about 24% higher than that of the traditional CNN. The proposed network is applied to the bearing data collection of Western Reserve University under the condition that the network parameters remain unchanged. The experiment shows that the improved CNN-LSTM network has better generalization than the traditional CNN.

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