Condition monitoring and fault diagnosis of rotating machinery based on feature extraction and expression of vibration signals

Issue title: Special Section: Intelligent Data Aggregation Inspired Paradigm and Approaches in IoT Applications

Guest editors: Xiaohui Yuan and Mohamed Elhoseny

Article type: Research Article

Authors: Haixia, Liu^{; *}

Affiliations: School of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an, China

Correspondence: [*] Corresponding author. Liu Haixia, School of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an, 710055, China. E-mail: [email protected].

Abstract:  To further explore the state monitoring of rotating machinery, the state monitoring and fault diagnosis functions have been presented by using vibration signal feature extraction and expression technology. After describing the development status of vibration signal extraction and expression, the state monitoring and fault diagnosis model of rotating machinery are constructed by using this technology. Based on the algorithm flow and evaluation model, an optimization and update scheme is proposed, and an adequate evaluation model is established by evaluating the fault detection status effectively. A genetic algorithm is added to the model to assist optimization to achieve timely analysis and processing of data. In the testing of mechanical fault detection model, the efficiency of data processing and the number of fault detection are tested for vibration signal feature extraction. The test results show that the vibration signal feature extraction technology speeds up the state detection effect and the processing results are more precise.

Keywords: Vibration signal, rotating machinery state, monitoring fault diagnosis, genetic algorithm

DOI: 10.3233/JIFS-179067

Journal: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 87-94, 2019