Gear Fault Detection Using Recurrence Quantification Analysis and Support Vector Machine

Nazari

2019

J. Vib. Eng. Technol.

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Background In general, diagnostics can be defined as the procedure of mapping the information obtained in the measurement space to the presence and magnitude of faults in the fault space. These measurements, and especially their nonlinear features, have the potential to be exploited to detect changes in dynamics due to the faults. Purpose We have been developing some interesting techniques for fault diagnostics with gratifying results. Methods These techniques are fundamentally based on extracting appropriate features of nonlinear dynamical behavior of dynamic systems. In particular, this paper provides an overview of a technique we have developed called Phase Space Topology (PST), which has so far displayed remarkable effectiveness in unearthing faults in machinery. Applications to bearing, gear and crack diagnostics are briefly discussed.

Section: Gear Fault Diagnosticsmentioning

confidence: 99%

A Review of Phase Space Topology Methods for Vibration-Based Fault Diagnostics in Nonlinear Systems

Nazari

2019

J. Vib. Eng. Technol.

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“…Moreover, these methods do not consider the fundamental nonlinear physics of the system, which could have valuable insights, as was shown in our work Kwuimy et al (2018) and Maraini and Nataraj (2018). The present study is a continuation of our development of a family of methods based on phase space characterizations (Mohamad and Nataraj, 2017;Mohamad et al, 2018aMohamad et al, , 2018bMohamad et al, , 2018cMohamad et al, , 2019Samadani et al, 2013Samadani et al, , 2016.…”

Section: Introductionmentioning

confidence: 79%

“…Various metrics can be calculated from the confusion matrix such as precision, recall, and overall accuracy (Mohamad et al, 2018b) to define the performance of the classifier. Precision measures the rate of correct predictions from all predictions for each class and indicates how often the classifier's prediction is correct.…”

Section: Fault Identificationmentioning

confidence: 99%

Fault identification and severity analysis of rolling element bearings using phase space topology

Journal of Vibration and Control

2020

This article presents the application of phase space topology and time-domain statistical features for rolling element bearing diagnostics in rotating machines under variable operating conditions. The results indicate very promising performance in identifying various faults with virtually perfect accuracy, recall, and precision. A comparison with the envelope analysis method is performed to show the superior performance of the proposed approach. In addition, the results demonstrate an outstanding prediction rate for the fault diameter of bearing defects.

“…In [20,21], we presented the application of recurrence plots (RPs) and recurrence quantification analysis (RQA) in the diagnostics of various faults in a gear-train system. It also apply mutual information to rank the extracted features in order to obtain an optimal feature set.…”

Section: Gear-train Setupmentioning

confidence: 99%

An overview of PST for vibration based fault diagnostics in rotating machinery

2018

MATEC Web Conf.

Self Cite

In general, diagnostics can be defined as the procedure of mapping the information obtained in the measurement space to the presence and magnitude of faults in the fault space. These measurements, and especially their nonlinear features, have the potential to be exploited to detect changes in dynamics due to the faults. We have been developing some interesting techniques for fault diagnostics with gratifying results. These techniques are fundamentally based on extracting appropriate features of nonlinear dynamical behavior of dynamic systems. In particular, this paper provides an overview of a technique we have developed called Phase Space Topology (PST), which has so far displayed remarkable effectiveness in unearthing faults in machinery. Applications to bearing, gear and crack diagnostics are briefly discussed.