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Open AccessArticle

A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine

1
College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China
2
Huadian Electric Power Research Institute, Hangzhou 310030, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(2), 420; https://doi.org/10.3390/s20020420
Received: 6 December 2019 / Revised: 8 January 2020 / Accepted: 9 January 2020 / Published: 11 January 2020
Classification of faults in mechanical components using machine learning is a hot topic in the field of science and engineering. Generally, every real-world running mechanical system exhibits personalized vibration behaviors that can be measured with acceleration sensors. However, faulty samples of such systems are difficult to obtain. Therefore, machine learning methods, such as support vector machine (SVM), neural network (NNs), etc., fail to obtain agreeable fault detection results through smart sensors. A personalized diagnosis fault method is proposed to activate the smart sensor networks using finite element method (FEM) simulations. The method includes three steps. Firstly, the cosine similarity updated FEM models with faults are constructed to obtain simulation signals (fault samples). Secondly, every simulation signal is separated into sub-signals to solve the time-domain indexes to generate the faulty training samples. Finally, the measured signals of unknown samples (testing samples) are inserted into the trained SVM to classify faults. The personalized diagnosis method is applied to detect bearing faults of a public bearing dataset. The classification accuracy ratios of six types of faults are 90% and 92.5%, 87.5% and 87.5%, 85%, and 82.5%, respectively. It confirms that the present personalized diagnosis method is effectiveness to detect faults in the absence of fault samples. View Full-Text
Keywords: personalized fault diagnosis; bearings; finite element method; numerical simulation; support vector machines personalized fault diagnosis; bearings; finite element method; numerical simulation; support vector machines
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Liu, X.; Huang, H.; Xiang, J. A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine. Sensors 2020, 20, 420.

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