Lightweight and intelligent model based on enhanced sparse filtering for rotating machine fault diagnosis

过度拟合 Softmax函数 计算机科学 人工智能 稳健性(进化) 分类器(UML) 断层(地质) 机器学习 模式识别(心理学) 人工神经网络 生物化学 化学 地震学 基因 地质学
作者
Yunhan Ling,Dianyu Fu,Peng Jiang,Yuwen Sun,Chao Yuan,Dali Huang,Jingfeng Lu,Siliang Lu
出处
期刊:Transactions of the Institute of Measurement and Control [SAGE Publishing]
标识
DOI:10.1177/01423312231185702
摘要

Rotating machine fault diagnosis plays a vital role in reducing maintenance costs and preventing accidents. Machine learning (ML) methods and Internet of things (IoT) technologies have been recently introduced into machine fault diagnosis and have generated inspiring results. An ML model with more trainable parameters can typically generate a higher fault diagnostic accuracy. However, the IoT nodes have limited computation and storage resources. How to design an ML model with high accuracy and computational efficiency is still a difficulty and challenge. This work develops an enhanced sparse filtering (ESF) method for mining and fusing the features of the machine signals for fault diagnosis. First, a dimension reduction algorithm is utilized for obtaining the principal components of the vibration signals that are hindered by noises. The distinct features of the principal components are then exploited by using sparse filtering (SF). To reduce the overfitting of the SF model, the L 1/2 norm is applied to regularize the objective function. Finally, the obtained features are combined as the inputs of a softmax classifier for machine fault pattern recognition. The effectiveness, superiority, and robustness of the proposed ESF method are validated by the simulated signals and the practical bearing and motor fault signals compared with the other conventional methods. The lightweight and intelligent ESF algorithm is also deployed onto an edge computing node to realize online motor fault diagnosis. The designed model and the proposed method show great potential in highly accurate and efficient rotation machine fault diagnosis.
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