Fault diagnosis and life prediction of mechanical equipment based on artificial intelligence

Issue title: Special Section: Big data analysis techniques for intelligent systems

Guest editors: Ahmed Farouk and Dou Zhen

Article type: Research Article

Authors: Heda, Zhang^{; *}

Affiliations: School of Mechanical Engineering, Beijing Institute of Technology, China

Correspondence: [*] Corresponding author. Zhang heda, School of Mechanical Engineering, Beijing Institute of Technology, China. E-mail: [email protected].

Abstract: Machinery and equipment are widely used in modern large-scale production, while industrial large-scale production and the progress of science and technology make machinery and equipment more complex and large-scale. Traditional mechanical diagnosis technology cannot meet the actual diagnosis requirements. The residual life of the whole equipment is predicted. This is of great significance for improving the efficiency of equipment, enhancing reliability, reducing maintenance costs and prolonging service life. The advantages of artificial intelligence in solving the problems of remote control, fault diagnosis and non-linearity point out the direction of the development of mechanical fault diagnosis technology. The research shows that the fault prediction and maintenance process based on the operating state of the device is summarized into three steps: data acquisition, data processing and equipment remaining life prediction. The comprehensive detection algorithm is used for diagnosis, and the diagnosis method is comprehensively analyzed. The research shows that after optimizing the network parameters through human intelligence, the network convergence speed is obviously accelerated, which can be used as a performance-based pattern recognition system for fault diagnosis of mechanical equipment.

Keywords: Artificial intelligence, machinery and equipment, fault diagnosis

DOI: 10.3233/JIFS-179157

Journal: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 3535-3544, 2019