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Open AccessArticle

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

by Wenkai Liu 1,2, Ping Guo 1,2,* and Lian Ye 1,2
1
Chongqing Key Laboratory of Software Theory and Technology, Chongqing University, Chongqing 400044, China
2
College of Computer Science, Chongqing University, Chongqing 400044, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(14), 3109; https://doi.org/10.3390/s19143109
Received: 15 May 2019 / Revised: 18 June 2019 / Accepted: 5 July 2019 / Published: 14 July 2019
(This article belongs to the Special Issue Sensors for Fault Diagnosis)
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. View Full-Text
Keywords: recurrent neural network; data-driven fault diagnosis; lightweight network; deep learning; bearing faults recurrent neural network; data-driven fault diagnosis; lightweight network; deep learning; bearing faults
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Liu, W.; Guo, P.; Ye, L. A Low-Delay Lightweight Recurrent Neural Network (LLRNN) for Rotating Machinery Fault Diagnosis. Sensors 2019, 19, 3109.

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