Diagnosis of mechanical unbalance for double cage induction motor load in time-varying conditions based on motor vibration signature analysis

Abstract

This paper investigates the detectability of mechanical unbalance in double cage induction motor load using motor vibration signature analysis technique. Rotor imbalances induce specific harmonic components in electrical, electromagnetical, and mechanical quantities. Harmonic components characteristic of this category of rotor faults, issued from vibration signals analysis, are closely related to rotating speed of the rotor, which complicates its detection under non-stationary operating conditions of the motor. Firstly, experimental results were performed first under healthy and mechanical load unbalance cases, for different load levels under steady-state operating conditions to evaluate the sensitivity of motor axial vibration signature analysis (MAVSA) and motor radial vibration signature analysis (MRVSA) techniques. Secondly, and in order to overcome the limitations of the FFT analysis in time-varying conditions, a simple and effective method based on advanced use of wavelet analysis is proposed, that allows the diagnosis of mechanical load unbalance for a double cage induction machine operating under non-stationary conditions. Experimental tests were conducted for these purposes showing the effectiveness of the presented technique under time-varying operating conditions

A Survey of Broken Rotor Bar Fault Diagnostic Methods of Induction Motor

“…In addition, detection of faults at the early stages gives a lot of advantages like reliability of operation, increased motor life and economic benefits [1]. When motor starts becoming faulty it tends to change some of its parameters, such as mechanical vibrations [4], [5], electromagnetic field distributions [6], [7], temperature [8], stator's current [9]- [11]. Since induction motors are proportionally among the biggest energy consumers worldwide, their proper maintenance and early fault diagnostics will increase efficiency as well.…”

Fast–ICA for Mechanical Fault Detection and Identification in Electromechanical Systems for Wind Turbine Applications

“…These techniques include: Analysis of acoustic emissions [7], [8], oil analysis [9], [11] and specifically vibratory analysis. In particular, the investigation of vibratory signals has been proposed in different works, using different approaches: statistical analysis [10]- [11], temporal and/or frequency domain [7], [12], [13].…”