Table of Contents
Journal of Industrial Mathematics
Volume 2014 (2014), Article ID 543056, 10 pages
http://dx.doi.org/10.1155/2014/543056
Research Article

Cross Correlation for Condition Monitoring of Variable Load and Speed Gearboxes

Bharti School of Engineering, Laurentian University, Sudbury, ON, Canada P3E 2C6

Received 31 July 2014; Accepted 22 November 2014; Published 22 December 2014

Academic Editor: Domenico Vitulano

Copyright © 2014 Jordan McBain and Markus Timusk. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The ability to identify incipient faults at an early stage in the operation of machinery has been demonstrated to provide substantial value to industry. These benefits for automated, in situ, and online monitoring of machinery, structures, and systems subject to varying operating conditions are difficult to achieve at present when they are run in operationally constrained environments that demand uninterrupted operation in this mode. This work focuses on developing a simple algorithm for this problem class; novelty detection is deployed on feature vectors generated from the cross correlation of vibration signals from sensors mounted on disparate locations in a power train. The behavior of these signals in a gearbox subject to varying load and speed is expected to remain in a commensurate state until a change in some physical aspect of the mechanical components, presumed to be indicative of gearbox failure. Cross correlation will be demonstrated to generate excellent classification results for a gearbox subject to independently changing load and speed. It eliminates the need to analyze the highly complex dynamics of this system; it generalizes well across untaught ranges of load and speed; it eliminates the need to identify and measure all predominant time-varying parameters; it is simple and computationally inexpensive.