Vibration analysis of electrical rotating machines using FFT: A method of predictive maintenance

Abstract

Present day requirements for enhanced reliability of rotating equipments are most critical than ever before, and demands continue to grow constantly. Detection of faults play important role in the quest for highly reliable operations. Reducing maintenance and production cost, improving uptime, product quality, advance safety and reducing risks are some of the essential drivers for deploying vibration analysis. These serve as goals of any plant or corporation. Vibration analysis for predictive maintenance is an important ingredient in all these goals. Vibration analysis can be used as part of root cause analysis efforts within any plant. The present work offers a course of action for analyzing the vibration signals of electrical rotating machines and diagnoses the health of machine for predictive maintenance requirements using Fast Fourier Transform (FFT). The Vibration analysis of electrical rotating machines lies on the fact that all rotating machines in good condition have fairly stable vibration pattern. Under any abnormal condition in working of machines, the vibration pattern gets changed. The amount of variation can be detected and the nature of abnormalities can be analyzed with LabVIEW to get an idea about the fault in the machine. Based on the type of defect and its slope of progression, predictive maintenance schedule can be proposed. This work also aims at overcoming the limitations of traditional vibration analysis techniques.