Article: Intelligent fault diagnosis framework of mechatronics systems on high resolution sensory data Journal: International Journal of Mechatronics and Manufacturing Systems (IJMMS) 2024 Vol.17 No.1 pp.69 - 83 Abstract: A stable electrical power supply relies on efficient fault diagnosis in power systems. The existing fault detection methods suffer from low accuracy and detection delay, hampering prompt rectification of power system faults. Overcoming these limitations requires precise early fault diagnosis, which is crucial for the evolution of power system fault diagnosis. This paper presents an advanced fault diagnosis method, utilising an enhanced convolutional neural network (CNN) with high-resolution digital fault recorder signals. The method targets subtle fault characteristics of power systems, achieving early fault detection, even before five recording cycles. The performance metrics for the method, including accuracy, precision, recall, and F1-score, are documented as 0.9550, 1.0000, 0.9495, and 0.9735, respectively. Additionally, the outcomes of the proposed work demonstrate enhanced performance compared to existing models. The developed automatic detection system accurately identifies faults, facilitating swift power supply restoration. This approach mitigates potential power system fault consequences, enhancing supply stability and economic benefits while reducing government spending. Inderscience Publishers - linking academia, business and industry through research

Title: Intelligent fault diagnosis framework of mechatronics systems on high resolution sensory data

Authors: Chengwei Chai; Zhuofu Deng; Jiaqiang Liu; Huaicheng Wu

Addresses: Software College, Northeastern University, Shenyang, 110169, China ' Software College, Northeastern University, Shenyang, 110169, China ' Super High Voltage Branch, State Grid Liaoning Electric Power Co., Ltd., Shenyang, 110000, China ' Super High Voltage Branch, State Grid Liaoning Electric Power Co., Ltd., Shenyang, 110000, China

Abstract: A stable electrical power supply relies on efficient fault diagnosis in power systems. The existing fault detection methods suffer from low accuracy and detection delay, hampering prompt rectification of power system faults. Overcoming these limitations requires precise early fault diagnosis, which is crucial for the evolution of power system fault diagnosis. This paper presents an advanced fault diagnosis method, utilising an enhanced convolutional neural network (CNN) with high-resolution digital fault recorder signals. The method targets subtle fault characteristics of power systems, achieving early fault detection, even before five recording cycles. The performance metrics for the method, including accuracy, precision, recall, and F1-score, are documented as 0.9550, 1.0000, 0.9495, and 0.9735, respectively. Additionally, the outcomes of the proposed work demonstrate enhanced performance compared to existing models. The developed automatic detection system accurately identifies faults, facilitating swift power supply restoration. This approach mitigates potential power system fault consequences, enhancing supply stability and economic benefits while reducing government spending.

Keywords: failure; fault diagnosis; mechatronics systems; power systems; deep learning.

DOI: 10.1504/IJMMS.2024.138135

International Journal of Mechatronics and Manufacturing Systems, 2024 Vol.17 No.1, pp.69 - 83

Received: 03 Dec 2023
Accepted: 23 Mar 2024
Published online: 29 Apr 2024 *

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Title: Intelligent fault diagnosis framework of mechatronics systems on high resolution sensory data

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