Knowledge Transfer based Evolutionary Deep Neural Network for Intelligent Fault Diagnosis

• URL: http://arxiv.org/abs/2109.13479v5
• Date: Fri, 21 Mar 2025 11:54:41 GMT
• Title: Knowledge Transfer based Evolutionary Deep Neural Network for Intelligent Fault Diagnosis
• Authors: Arun K. Sharma, Nishchal K. Verma,
• Abstract summary: We propose an evolutionary Net2Net transformation (EvoN2N) that finds the best suitable DNN architecture with limited availability of labeled data samples.<n>The proposed framework can obtain the best model for intelligent fault diagnosis without a long and time-consuming search process.<n>The best models obtained are capable of demonstrating an excellent diagnostic performance and classification accuracy of almost up to 100% for most of the operating conditions.
• Score: 6.167830237917662
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A faster response with commendable accuracy in intelligent systems is essential for the reliability and smooth operations of industrial machines. Two main challenges affect the design of such intelligent systems: (i) the selection of a suitable model and (ii) domain adaptation if there is a continuous change in operating conditions. Therefore, we propose an evolutionary Net2Net transformation (EvoN2N) that finds the best suitable DNN architecture with limited availability of labeled data samples. Net2Net transformation-based quick learning algorithm has been used in the evolutionary framework of Non-dominated sorting genetic algorithm II to obtain the best DNN architecture. Net2Net transformation-based quick learning algorithm uses the concept of knowledge transfer from one generation to the next for faster fitness evaluation. The proposed framework can obtain the best model for intelligent fault diagnosis without a long and time-consuming search process. The proposed framework has been validated on the Case Western Reserve University dataset, the Paderborn University dataset, and the gearbox fault detection dataset under different operating conditions. The best models obtained are capable of demonstrating an excellent diagnostic performance and classification accuracy of almost up to 100% for most of the operating conditions.

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