Gear Fault Detection With the Energy Operator and its Variants

Vibration analysis is currently the most efficient, non-invasive way to monitor the condition of the gears. Faults in gears can be of two distinct types, distributed or local. Many fault detection methods are effective for one type of fault or the other but not both. Also, many methods have the inconvenience that they are not simple and/or require initial information about the state of gear. In this paper, several methods are proposed with the objective of finding a filter-free, simple and efficient method for the detection of both types of faults. The calculus enhanced energy operator (CEEO), previously designed for fault detection in bearings, is proposed here for the first time on gears. Two new methods, the EO123 and EO23, based on the original energy operator are also proposed and evaluated. All the proposed methods are filter free, computationally simple and can handle a certain level of noise and interference. With the exception of low rotational frequencies of the gears, it is demonstrated via simulated and experimentally obtained signals that the CEEO method can handle noise better than the other proposed methods and that the EO23 method can handle interference better than the others. Different conditions determine the effectiveness of the methods.