Intelligent Fault Diagnosis of Rotating Machinery Based on Deep Recurrent Neural Network

2018 ◽  
Author(s):  
Xingqiu Li ◽  
Hongkai Jiang ◽  
Yanan Hu ◽  
Xiong Xiong
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Recurrent Neural Network ◽  
Rotating Machinery ◽  
Intelligent Fault Diagnosis ◽  
Deep Recurrent Neural Network

scholarly journals A Low-Delay Lightweight Recurrent Neural Network (LLRNN) for Rotating Machinery Fault Diagnosis

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3109 ◽  
Author(s):  
Wenkai Liu ◽  
Ping Guo ◽  
Lian Ye
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Cell Structure ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Memory Space ◽  
Low Delay ◽  
Long Short Term Memory

Fault diagnosis is critical to ensuring the safety and reliable operation of rotating machinery systems. Long short-term memory networks (LSTM) have received a great deal of attention in this field. Most of the LSTM-based fault diagnosis methods have too many parameters and calculation, resulting in large memory occupancy and high calculation delay. Thus, this paper proposes a low-delay lightweight recurrent neural network (LLRNN) model for mechanical fault diagnosis, based on a special LSTM cell structure with a forget gate. The input vibration signal is segmented into several shorter sub-signals in order to shorten the length of the time sequence. Then, these sub-signals are sent into the network directly and converted into the final diagnostic results without any manual participation. Compared with some existing methods, our experiments illustrate that the proposed method has less memory space occupancy and lower computational delay while maintaining the same level of accuracy.

A Deep Recurrent Neural Network-Based Method for Automated Building System Fault Diagnosis

2021 ◽  
pp. 613-624
Author(s):  
Yichen Liu ◽  
Xinghua Wang ◽  
Cheng Fan ◽  
Bufu Huang ◽  
Jiayuan Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Recurrent Neural Network ◽  
Deep Recurrent Neural Network ◽  
Building System

scholarly journals Smart Fault Diagnostics using Convolutional Neural Network and Adam Stochastic Optimization

2021 ◽  
Vol 3 (1) ◽  
pp. 38-46
Author(s):  
Subarna Shakya
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Nonlinear Problems ◽  
Basic Structure ◽  
Rotating Machinery ◽  
Intelligent Fault Diagnosis ◽  
Performance Diagnosis ◽  
Large Scope ◽  
And Performance

Navigation, aviation and several other fields of engineering extensively make use of rotating machinery. The stability and safety of the equipment as well as the personnel are affected by this machinery. Use of deep learning as the basis of intelligent fault diagnosis schemes has and investigation of other relevant fault diagnosis schemes has a large scope for development. Thorough exploration needs to be performed in deep neural network (DNN) based schemes as shallow layer network structure based fault diagnosis schemes that are currently available has several considerable limitations. The nonlinear problems may be processed during intelligent fault diagnosis using deep convolutional neural network, which is a special structure DNN. The convolutional neural network (CNN) based scheme is emphasized in this paper. The principle and basic structure of the model are introduced. In rotating machinery, the fault diagnosis schemes using CNN are analyzed and summarized. Various CNN schemes, the potential mechanisms and performance diagnosis are analyzed. A novel smart fault diagnosis strategy is proposed while highlighting the potential aspects of existing schemes and reviewing the challenges.

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