Time-frequency analysis for non-stationary signal from mechanical measurement of bearing vibration

Zhao, Guifen; Wang, Zhi'an

doi:10.1504/ijmic.2011.041307

Cited by 4 publications

(1 citation statement)

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“…Monitoring variable speed machinery becomes difficult due to the interaction between angle, and time dependent characteristics (Abboud et al , 2015). Some current approaches to the monitoring of non-stationary machinery include signal processing techniques such as order tracking, time-frequency domain analysis and cyclo-non-stationary signal analysis (Uma Maheswari and Umamaheswari, 2017; Zhao and Wang, 2011; Law et al , 2012; Prudhom et al , 2017; Abboud et al , 2017) as well as machine learning algorithms that look for patterns in the data that are the result of changes in condition(Goreczka and Strackeljan, 2012; Wang and Kanneg, 2009). Solutions to monitoring non-stationary machinery typically rely on extensive training data sets that attempt to cover the entire machine operating window, as well as a priori information about the failure signature.…”

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confidence: 99%

Fault detection for parallel operating machines

Helm

Timusk

2019

JQME

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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.

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