Machining Tool and Bearing Failure Analysis Using the Orthogonal Hilbert-Huang Transform on Vibration and Motor Current Datasets

Abstract

Early fault detection is the primary goal of condition-based maintenance (CBM); to identify an impending fault before failure occurs and provide the necessary maintenance in a timely manner. Machining tools, such as drill bits and milling inserts, require a significant amount of maintenance during industrial machining operations to ensure that workpiece tolerances are met. Faulty tools tend to machine outside expected tolerances and run the risk of causing permanent damage to the workpiece, resulting in added production costs and unwanted delays. Similarly, ball bearing maintenance is crucial for keeping a machine in acceptable working condition, and has been the primary focus of many CBM studies due to nonlinear bearing degradation.

The orthogonal Hilbert-Huang transform (OHHT) is a recent improvement to the relatively new Hilbert-Huang transform (HHT) that is significantly more computationally efficient— compared to other improved HHT algorithms—and reduces the amount of energy leakage caused by non-orthogonal intrinsic mode functions. The HHT and its derivatives are being explored in CBM applications due to its adaptive nature in analyzing nonlinear and non-stationary phenomena, which is characteristic of machining induced vibrations. It has also shown promise in diagnosing machine tool wear from motor current signals compared to commonly used techniques such as wavelet analysis. This paper showcases the OHHT’s potential as a useful diagnostics tool for analyzing machine induced vibration and motor current signals.