V-belt fault detection using Extended Park Vector Approach (EPVA) in centrifugal fan driven by an induction motor

Abstract

Monitoring is a crucial part of an overall process of controlling and supervising systems, as it aims to detect, locate and diagnose faults that may affect their operational safety, performance, and reliability. One of our primary objectives is to develop an effective monitoring tool for transmission systems that use electrical measurements, particularly stator currents, which are already available in machine control devices. In this paper, we propose a dynamic model for a V-belt transmission system to demonstrate the impact of faults on the driving induction motor. Subsequently, we discuss two diagnostic techniques based on spectral analysis of the stator currents: Motor Current Signature Analysis (MCSA), and the Extended Park Vector Approach (EPVA). To evaluate the effectiveness of these techniques, we introduce a side-cut-off fault by 5 cm on the belt. The results of both theoretical and experimental studies revealed that the EPVA exhibits a greater richness in characteristic frequencies associated with belt faults compared to the MCSA method. In addition, EPVA demonstrates higher sensitivity to V-belt defects as evidenced by a large difference in frequency amplitude between the normal and the defect states.