Fault detection for parallel operating machines

Abstract

Purpose

The purpose of this paper is to demonstrate that by utilizing the relationship between redundant hardware components, inherent in parallel machinery, vibration-based fault detection methods can be made more robust to changes in operational conditions. This work reports on a study of fault detection on bearings operating in two parallel subsystems that experience identical changes in speed and load.

Design/methodology/approach

This study was carried out using two identical subsystems that operate on the same duty cycle. The systems were run with both healthy and a variety of common bearing faults. The faults were detected by analyzing the residual between the features of the two vibration signatures from the two subsystems.

Findings

This work found that by utilizing this relationship in parallel operating machinery the fault detection process can be improved. The study looked at several different types of feature vector and found that, in this case, features based on envelope analysis or autoregressive model work the best, whereas basic statistical features did not work as well.

Originality/value

The proposed method can be a computationally efficient and simple solution to monitoring non-stationary machinery where there is hardware redundancy present. This method is shown to have some advantages over non-parallel approaches.

Citation

Helm, D. and Timusk, M. (2020), "Fault detection for parallel operating machines", Journal of Quality in Maintenance Engineering, Vol. 26 No. 2, pp. 335-348. https://doi.org/10.1108/JQME-10-2018-0087