Incipient fault diagnosis on active disturbance rejection control

Huang, Darong; Hua, Xueming; Mi, Bo; Liu, Yang; Zhang, Zhenyuan

doi:10.1007/s11432-020-3154-5

Cited by 10 publications

(1 citation statement)

References 3 publications

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“…Accordingly, machine fault diagnosis is crucial to prevent economic losses and severe industrial accidents by monitoring the running status of the mechanical components (Shao et al, 2022). Machine fault diagnosis can be typically achieved with the following successive procedures, namely, signal acquisition, feature extraction, and fault pattern recognition (Darong et al, 2022; Zhang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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

Ling

Jiang

et al. 2023

Transactions of the Institute of Measurement and Control

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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 L1/2 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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Section: Introductionmentioning

confidence: 99%