Research on online monitoring and fault diagnosis system based on multivariate empirical mode decomposition

Abstract

Abstract Mechanical equipment is crucial to industrial production, so the monitoring and fault diagnosis of its vibration signals is important to ensure production safety. And this paper mainly researches on an online monitoring and fault diagnosis system. The system makes use of multiple sensors to collect vibration signals from different positions of machines for multivariate signals so that condition of the machines can be monitored and information of local faults can avoid loss. Besides, this paper applies a method for fault feature extraction of mechanical equipment based on multivariate empirical mode decomposition (MEMD), which can accurately extract frequency of fault features to realize fault diagnosis when the features are weak in the early period. And this paper puts forward a design to the overall framework of the system based on technical requirements of the system, then introduces the multi-sensor mechanical fault diagnosis method based on MEMD. On such basis, this paper employs LabVIEW and Python to develop the upper computer software of the system, and experiments are carried out to testify whether this system is viable or not. The research results show that the system developed by this paper has practical application value in condition monitoring and fault diagnosis of mechanical equipment.