A Comprehensive 3-Steps Methodology for Vibration-based Fault Detection, Diagnosis and Localization in Rotating Machines

Abstract

In any industry, it is the requirement to know whether the machine is healthy or not to operate machine further. If the machine is not healthy then what is the fault in the machine and then finally its location. The paper is proposing a 3-steps methodology for the machine fault diagnosis to meet the industrial requirements to aid the maintenance activity.  The Step-1 identifies whether machine is healthy or faulty, then Step-2 detect the type of defect and finally its location in Step-3. This method is extended further from the earlier study on the 2-Steps method for the rotor defects only to the 3-Steps methodology to both rotor and bearing defects.  The method uses the optimised vibration parameters and a simple Artificial Neural Network (ANN)-based Machine Learning (ML) model from the earlier studies. The model is initially developed, tested and validated on an experimental rotating rig operating at a speed above 1^st critical speed. The proposed method and model are then further validated at 2 different operating speeds, one below 1^st critical speed and other above 2^nd critical speeds. The machine dynamics are  expected to be significantly different at these speeds. This highlights the robustness of the proposed 3-Steps method.

Conflict of Interest Statement

The authors declare no conflicts of interest.