Open Access

ARTICLE

Two-Stage Edge-Side Fault Diagnosis Method Based on Double Knowledge Distillation

Yang Yang¹, Yuhan Long¹, Yijing Lin², Zhipeng Gao¹, Lanlan Rui¹, Peng Yu^1,3,*

1 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China
2 School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore
3 Science and Technology on Communication Networks Laboratory, The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang, 050051, China

* Corresponding Author: Peng Yu. Email:

Computers, Materials & Continua 2023, 76(3), 3623-3651. https://doi.org/10.32604/cmc.2023.040250

Received 10 March 2023; Accepted 28 June 2023; Issue published 08 October 2023

Abstract

With the rapid development of the Internet of Things (IoT), the automation of edge-side equipment has emerged as a significant trend. The existing fault diagnosis methods have the characteristics of heavy computing and storage load, and most of them have computational redundancy, which is not suitable for deployment on edge devices with limited resources and capabilities. This paper proposes a novel two-stage edge-side fault diagnosis method based on double knowledge distillation. First, we offer a clustering-based self-knowledge distillation approach (Cluster KD), which takes the mean value of the sample diagnosis results, clusters them, and takes the clustering results as the terms of the loss function. It utilizes the correlations between faults of the same type to improve the accuracy of the teacher model, especially for fault categories with high similarity. Then, the double knowledge distillation framework uses ordinary knowledge distillation to build a lightweight model for edge-side deployment. We propose a two-stage edge-side fault diagnosis method (TSM) that separates fault detection and fault diagnosis into different stages: in the first stage, a fault detection model based on a denoising auto-encoder (DAE) is adopted to achieve fast fault responses; in the second stage, a diverse convolution model with variance weighting (DCMVW) is used to diagnose faults in detail, extracting features from micro and macro perspectives. Through comparison experiments conducted on two fault datasets, it is proven that the proposed method has high accuracy, low delays, and small computation, which is suitable for intelligent edge-side fault diagnosis. In addition, experiments show that our approach has a smooth training process and good balance.

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Keywords

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