Domain Adaptation-based Edge Computing for Cross-Conditions Fault Diagnosis

• URL: http://arxiv.org/abs/2411.10340v1
• Date: Fri, 15 Nov 2024 16:40:43 GMT
• Title: Domain Adaptation-based Edge Computing for Cross-Conditions Fault Diagnosis
• Authors: Yanzhi Wang, Chu Wang, Jinhong Wu, Ziyang Yu, Qi Zhou,
• Abstract summary: This paper proposes a domain adaptation-based lightweight fault diagnosis framework for edge computing scenarios. The acquired fault diagnosis expertise from the cloud-model is transferred to the lightweight edge-model using adaptation knowledge transfer methods. In terms of diagnostic performance, the proposed method significantly improved diagnostic accuracy, with average increases of 34.44% and 17.33%, respectively.
• Score: 31.06621257486631
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fault diagnosis technology supports the healthy operation of mechanical equipment. However, the variations conditions during the operation of mechanical equipment lead to significant disparities in data distribution, posing challenges to fault diagnosis. Furthermore, when deploying applications, traditional methods often encounter issues such as latency and data security. Therefore, conducting fault diagnosis and deploying application methods under cross-operating conditions holds significant value. This paper proposes a domain adaptation-based lightweight fault diagnosis framework for edge computing scenarios. Incorporating the local maximum mean discrepancy into knowledge transfer aligns the feature distributions of different domains in a high-dimensional feature space, to discover a common feature space across domains. The acquired fault diagnosis expertise from the cloud-model is transferred to the lightweight edge-model using adaptation knowledge transfer methods. While ensuring real-time diagnostic capabilities, accurate fault diagnosis is achieved across working conditions. We conducted validation experiments on the NVIDIA Jetson Xavier NX kit. In terms of diagnostic performance, the proposed method significantly improved diagnostic accuracy, with average increases of 34.44% and 17.33% compared to the comparison method, respectively. Regarding lightweight effectiveness, proposed method achieved an average inference speed increase of 80.47%. Additionally, compared to the cloud-model, the parameter count of the edge-model decreased by 96.37%, while the Flops decreased by 83.08%.

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