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

Measurement ◽  
2020 ◽  
Vol 159 ◽  
pp. 107802 ◽  
Author(s):  
Shijie Hao ◽  
Feng-Xiang Ge ◽  
Yanmiao Li ◽  
Jiayu Jiang
Keyword(s):  
Fault Diagnosis ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
One Dimensional ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Long Short Term Memory

A new fault diagnosis model of rolling element bearing based on a recurrent neural network

2021 ◽  
pp. 095440892110644
Author(s):  
Xudong Song ◽  
Dajie Zhu ◽  
Shaocong Sun
Keyword(s):  
Fault Diagnosis ◽  
Short Term Memory ◽  
Rolling Element Bearing ◽  
Short Term ◽  
Term Memory ◽  
Bearing Fault ◽  
Rolling Element ◽  
Element Bearing ◽  
Long Short Term Memory ◽  
Gated Recurrent Unit

The rolling element bearing is an important part of mechanical equipment, it has various kinds of malfunctions, the location of the fault may occur in the inner ring, outer ring, or rolling element of the bearing. Therefore, traditional methods of classification are difficult to classify and identify effectively. To improve the accuracy of bearing fault diagnosis, the deep learning method is used to diagnose the fault of the rolling element bearing. In this paper, the long short-term memory and gated recurrent unit are combined to build a bearing fault diagnosis model. On the other hand, this paper adjusts the hidden layer structure and optimizes the network parameters to establish a better long short-term memory–gated recurrent unit–long short-term memory diagnostic model and classify the fault types of bearings with Softmax. The model proposed in this paper can effectively diagnose the bearing fault under the bearing data set of Case Western Reserve University and the University of Cincinnati. Compared with the traditional long short-term memory and the gated recurrent unit, the model proposed in this paper has high accuracy in fault diagnosis as well as certain reliability and generalization ability.

Real-time fault diagnosis using deep fusion of features extracted by parallel long short-term memory with peephole and convolutional neural network

2020 ◽  
pp. 095965182094829
Author(s):  
Funa Zhou ◽  
Zhiqiang Zhang ◽  
Danmin Chen
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Short Term Memory ◽  
Two Dimensional ◽  
Short Term ◽  
Term Memory ◽  
One Dimensional ◽  
Long Short Term Memory

Analysis of one-dimensional vibration signals is the most common method used for safety analysis and health monitoring of rotary machines. How to effectively extract features involved in one-dimensional sequence data is crucial for the accuracy of real-time fault diagnosis. This article aims to develop more effective means of extracting useful features potentially involved in one-dimensional vibration signals. First, an improved parallel long short-term memory called parallel long short-term memory with peephole is designed by adding a peephole connection before each forget gate to prevent useless information transferring in the cell. It can not only solve the memory bottleneck problem of traditional long short-term memory for long sequence but also can make full use of all possible information helpful for feature extraction. Second, a fusion network with new training mechanism is designed to fuse features extracted from parallel long short-term memory with peephole and convolutional neural network, respectively. The fusion network can incorporate two-dimensional screenshot image into comprehensive feature extraction. It can provide more accurate fault diagnosis result since two-dimensional screenshot image is another form of expression for one-dimensional vibration sequence involving additional trend and locality information. Finally, real-time two-dimensional screenshot image is fed into convolutional neural network to secure a real-time online diagnosis which is the primary requirement of the engineers in health monitoring. Validity of the proposed method is verified by fault diagnosis for rolling bearing and gearbox.

scholarly journals Fault Diagnosis for Bearing Based on 1DCNN and LSTM

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.

scholarly journals Intelligent Fault Diagnosis of Bearing Based on Convolutional Neural Network and Bidirectional Long Short-Term Memory

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Dazhang You ◽  
Linbo Chen ◽  
Fei Liu ◽  
YePeng Zhang ◽  
Wei Shang ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Vibration Signal ◽  
Short Term ◽  
Term Memory ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Accuracy ◽  
Long Short Term Memory

The traditional bearing fault diagnosis methods have complex operation processes and poor generalization ability, while the diagnosis accuracy of the existing intelligent diagnosis methods needs to be further improved. Therefore, a novel fault diagnosis approach named CNN-BLSTM for bearing is presented based on convolutional neural network (CNN) and bidirectional long short-term memory (BLSTM) in this paper. This method directly takes the collected one-dimensional raw vibration signal as input and adaptively extracts the feature information through CNN. Then, the BLSTM is used to fuse the extracted features to acquire the failure information sufficiently and prevent the model from overfitting. Finally, two different experimental datasets are used to verify the effectiveness of the method. The experimental results show that the proposed CNN-BLSTM model can accurately diagnose the fault category of bearings. It has the advantages of rapidity, stability, antinoise, and strong generalization.

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

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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