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.

Detection of Rolling-Element Bearing Faults in Non-stationary Quasi-Parallel Machinery Using Residual Analysis Augmented by Neural Networks

“…Experimental feature residual analysis, first proposed in and further investigated in (Helm & Timusk, 2017;Helm & Timusk, 2019) is a method for detecting faults in connected parallel machinery by analyzing the residual or difference between vibration features in the parallel subsystems. When a fault is present the residual between the features of the vibrations from the parallel subsystem will increase.…”

“…Experimental feature residual analysis as defined in (Helm & Timusk, 2017;Helm & Timusk, 2019) is limited in possible applications; this work looks to expand the possible applications to quasi-parallel machinery. Quasi-parallel machinery, unlike connected parallel machinery does not require the individual subsystems to be identical, nor do they have to have identical operating conditions, the only requirement is that they share a common forcing function.…”

“…The main contribution of this work is that it presents a new fault detection architecture that extends the work in (Helm & Timusk, 2017;Helm & Timusk, 2019) to be able to include connected machinery that does not necessarily operate in a perfectly parallel manner (quasi-parallel). This is accomplished through the addition of an FFNN to the experimental feature residual analysis technique to allow the utilization of the real time information from a connected but not strictly identical component to reduce the sensitivity of the system to fluctuations in speed and load.…”