Gear fault diagnosis using vibration signals based on decision tree assisted intelligent controllers

Gears are one of the most widely used elements in rotary machines for transmitting power and torque. The system is subjected to variable speed and torque which leads to faults in gears. This paper presents two different online condition monitoring systems using fuzzy and artificial neural network (ANN) controller for the fault diagnosis of spur gear. This work is conceived as pattern recognition problem and it consists of four main phases: viz. feature extraction, feature selection using C4.5 algorithm, training of fuzzy and ANN controllers with the selected features. Under feature extraction, statistical features like skewness, standard deviation, variance, root mean square (RMS) value, kurtosis, range, minimum value, maximum value, sum, median and crest factor are considered as features of the signal in the fault diagnostics. These features are extracted from vibration signals of time domain obtained from the experimental setup through a piezoelectric sensor. The vibration signals from the sensor are captured for normal tooth, wear tooth, broken tooth and broken tooth under loading conditions. The controllers are built and tested with representative data and the performance is also discussed