LabVIEW Based Implementation of Fuzzy Logic for Vibration Analysis to Identify Machinery Faults

Rotating machine is device that supports an important category of manufacturing industry. Machine fault detection is very advantageous for diagnosis of faults before it occurs and to protect the machinery from catastrophic damages. Vibration monitoring is important for running rotating equipment evenly for years. There are diverse reasons that causes vibrations in the rotating machinery like rotor unbalance, machine looseness, permanent bow (bend shaft or warped shaft ), bearing damage, misalignment, eccentricity, oil whirl, cracked tooth, etc. There are several methods used to detect machinery faults such as, model based technique and signal based techniques which includes Hilbert-Huang Transform (HET), Wavelet Transform (WT), Artificial Neural Networks (ANN), Support Vector Machine (SVM), Shock Pulse Monitoring (SPM), Fuzzy Logic. The proposed work shows a novel approach of classification using fuzzy logic for decision making about the machine condition. The experimental results shows that, algorithm implemented identifies severity of faults to appropriate condition.