scholarly journals A Novel Fault Diagnosis Approach for Chillers Based on 1-D Convolutional Neural Network and Gated Recurrent Unit

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2458 ◽  
Author(s):  
Zhuozheng Wang ◽  
Yingjie Dong ◽  
Wei Liu ◽  
Zhuo Ma
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Sequence Data ◽  
Experimental Results ◽  
Learning Technology ◽  
Global Features ◽  
Gated Recurrent Unit ◽  
Diagnosis Approach

The safety of an Internet Data Center (IDC) is directly determined by the reliability and stability of its chiller system. Thus, combined with deep learning technology, an innovative hybrid fault diagnosis approach (1D-CNN_GRU) based on the time-series sequences is proposed in this study for the chiller system using 1-Dimensional Convolutional Neural Network (1D-CNN) and Gated Recurrent Unit (GRU). Firstly, 1D-CNN is applied to automatically extract the local abstract features of the sensor sequence data. Secondly, GRU with long and short term memory characteristics is applied to capture the global features, as well as the dynamic information of the sequence. Moreover, batch normalization and dropout are introduced to accelerate network training and address the overfitting issue. The effectiveness and reliability of the proposed hybrid algorithm are assessed on the RP-1043 dataset; based on the experimental results, 1D-CNN_GRU displays the best performance compared with the other state-of-the-art algorithms. Further, the experimental results reveal that 1D-CNN_GRU has a superior identification rate for minor faults.

scholarly journals A CNN-ABiGRU method for Gearbox Fault Diagnosis

2022 ◽  
Vol 16 ◽  
pp. 440-446
Author(s):  
Xiaoyang Zheng ◽  
Zeyu Ye ◽  
Jinliang Wu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Prior Knowledge ◽  
Time Sequence ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Generalization Performance ◽  
Proposed Model ◽  
Gated Recurrent Unit

As a key part of modern industrial machinery, there has been a lot of fault diagnosis methods for gearbox. However, traditional fault diagnosis methods suffer from dependence on prior knowledge. This paper proposed an end-to-end method based on convolutional neural network (CNN), Bidirectional gated recurrent unit (BiGRU), and Attention Mechanism. Among them, the application of BiGRU not only made perfect use of the time sequence of signal, but also saved computing resources more than the same type of networks because of the low amount of calculation. In order to verify the effectiveness and generalization performance of the proposed method, experiments are carried out on two datasets, and the accuracy is calculated by the ten-fold crossvalidation. Compared with the existing fault diagnosis methods, the experimental results show that the proposed model has higher accuracy.

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.

scholarly journals A Novel Fault Diagnosis Algorithm for Rolling Bearings Based on One-Dimensional Convolutional Neural Network and INPSO-SVM

2020 ◽  
Vol 10 (12) ◽  
pp. 4303
Author(s):  
Yang Shao ◽  
Xianfeng Yuan ◽  
Chengjin Zhang ◽  
Yong Song ◽  
Qingyang Xu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Experimental Results ◽  
Learning Ability ◽  
Support Vector ◽  
Intelligent Fault Diagnosis ◽  
Rolling Bearings ◽  
One Dimensional ◽  
Hybrid Framework

Deep learning based intelligent fault diagnosis methods have become a research hotspot in the fields of fault diagnosis and the health management of rolling bearings in recent years. To effectively identify incipient faults in rotating machinery, this paper proposes a novel hybrid intelligent fault diagnosis framework based on a convolutional neural network and support vector machine (SVM). First, an improved one-dimensional convolutional neural network (1DCNN) was adopted to extract fault features, and the state information and intrinsic properties of the raw vibration signals were mined. Second, the extracted features were used to train the SVM, which was applied to classify the fault category. The proposed hybrid framework combined the excellent classification performance of the SVM for small samples and the strong feature-learning ability of CNN network. In order to tune the parameters of the SVM, an improved novel particle swarm optimization algorithm (INPSO) which combined the Tent map and Lévy flight strategy was proposed. Numerical experimental results indicated that the proposed PSO variant had a better performance in searching accuracy and convergence speed. At last, multiple groups of rolling bearing fault diagnosis experiments were carried out and experimental results showed that, with the proposed 1DCNN-INPSO-SVM model, the hybrid framework was capable of diagnosing with high precision for rolling bearings and superior to some traditional fault diagnosis methods.

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 Early Fault Detection Method of Rolling Bearing Based on MCNN and GRU Network with an Attention Mechanism

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xiaochen Zhang ◽  
Yiwen Cong ◽  
Zhe Yuan ◽  
Tian Zhang ◽  
Xiaotian Bai
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Convolutional Neural Network ◽  
Detection Method ◽  
Rolling Bearing ◽  
Attention Mechanism ◽  
Early Fault Detection ◽  
Gated Recurrent Unit ◽  
Unit Network

Aiming at the problem of early fault diagnosis of rolling bearing, an early fault detection method of rolling bearing based on a multiscale convolutional neural network and gated recurrent unit network with attention mechanism (MCNN-AGRU) is proposed. This method first inputs multiple time scales rolling bearing vibration signals into the convolutional neural network to train the model through multiscale data processing and then adds the gated recurrent unit network with an attention mechanism to make the model predictive. Finally, the reconstruction error between the actual value and the predicted value is used to detect the early fault. The training data of this method is only normal data. The early fault detection in the operating condition monitoring and performance degradation assessment of the rolling bearing is effectively solved. It uses a multiscale data processing method to make the features extracted by CNN more robust and uses a GRU network with an attention mechanism to make the predictive ability of this method not affected by the length of the data. Experimental results show that the MCNN-AGRU rolling bearing early fault diagnosis method proposed in this paper can effectively detect the early fault of the rolling bearing and can effectively identify the type of rolling bearing fault.

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 Bearing Fault Diagnosis Based on Multiscale Convolutional Neural Network Using Data Augmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Seungmin Han ◽  
Seokju Oh ◽  
Jongpil Jeong
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Feature Learning ◽  
Learning Technology ◽  
Financial Loss ◽  
Training Time ◽  
Imbalance Problem

Bearings are one of the most important parts of a rotating machine. Bearing failure can lead to mechanical failure, financial loss, and even personal injury. In recent years, various deep learning techniques have been used to diagnose bearing faults in rotating machines. However, deep learning technology has a data imbalance problem because it requires huge amounts of data. To solve this problem, we used data augmentation techniques. In addition, Convolutional Neural Network, one of the deep learning models, is a method capable of performing feature learning without prior knowledge. However, since conventional fault diagnosis based on CNN can only extract single-scale features, not only useful information may be lost but also domain shift problems may occur. In this paper, we proposed a Multiscale Convolutional Neural Network (MSCNN) to extract more powerful and differentiated features from raw signals. MSCNN can learn more powerful feature expression than conventional CNN through multiscale convolution operation and reduce the number of parameters and training time. The proposed model proved better results and validated the effectiveness of the model compared to 2D-CNN and 1D-CNN.

Sign in / Sign up

Export Citation Format

Share Document