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

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 2; p. 420
• Main Authors: Liu, Xiaoyang, Huang, Haizhou, Xiang, Jiawei
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 11-01-2020; MDPI
• Subjects: Artificial intelligence; Bearings; Boundary conditions; Classification; Computer simulation; Customization; Experiments; Fault detection; Fault diagnosis; finite element method; Lagrange multiplier; Machine learning; Mechanical components; Mechanical systems; Neural networks; numerical simulation; Optimization; Parameter identification; personalized fault diagnosis; Simulation; Smart sensors; Standard deviation; Support vector machines; Vibration measurement; personalized fault diagnosis; bearings; finite element method; support vector machines; numerical simulation
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20020420

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.