Intelligent Method for Faults Diagnosis of Rolling Bearings via Chaos Optimized  Support Vector Machine

Qin, Hongling; Zhou, Xincong; Tian, Hongtao; Lu, Xiao

doi:10.19026/rjaset.5.4875

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…It is used in machine fault diagnosis, e.g. studies in [102][103][104] focusing on bearing fault detections and diagnosis, and machine learning, e.g. [105], since it shows outstanding performance in modelling and generalizing when compared with other methods such as neural network.…”

Section: Support Vector Machinementioning

confidence: 99%

Multi-Sensor Condition Monitoring of Bearings Using Support Vector Machines

Kan¹

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Keywords AbstractBearings are one of the most commonly-used and crucial components in the majority of rotating machines in industrial applications. Failure of this component is common and can lead to machine breakdown. If it is not detected in time, bearing faults can even lead to catastrophic failures. An effective diagnostic system provides early warning and fault identification such that any machine breakdown can be avoided. Condition monitoring can benefit plant operators resulting in shorter downtimes, higher operation reliability, reduced operations and maintenance cost, and more effective maintenance and logistics planning.However, machines and associated systems today are increasingly complex, while diagnostic techniques have been mainly applied to relatively simple rotating machines. Therefore, there is a need for research on condition monitoring techniques that can be applied to complex systems in variable operating conditions. This thesis presents a study on bearing condition monitoring under variable operating condition using Support Vector Machines. Data collected from multiple sensors including accelerometers, acoustic emission sensors and tachometers are used for the studies presented in this thesis. Support Vector Machines are an efficient method for classification and prediction, and shows outstanding performance in modelling and generalization.The study begins with collecting bearing condition monitoring data. Vibration analysis is a well-established technique, while acoustic emission is gaining an increasing amount of attention especially on its capability to detect bearing incipient fault such as subsurface cracks. Existing studies so far have not demonstrated AE's diagnostic capabilities using naturally-damaged bearing, and only a very few studies adopt envelope and cyclostationary analysis on bearing fault diagnosis with this sensor technology. Due to the second-order cyclostationary nature of bearing signals and the success of envelope analysis in analysing bearing signals, the diagnostic and prognostic studies presented in this thesis make use of both envelope analysis and cyclostationary analysis to analyse vibration and acoustic emission bearing signals that will be collected from bearing diagnostics and run-to-failure prognostic experiments through a bearing diagnostic test-rig and a bearing prognostic test-rig.Using the collected data, the effectiveness and diagnostic capability of cyclostationary analysis will first be assessed using feature analysis with support vector machine. A diagnostic study is then conducted, investigating the diagnostic effectiveness of acoustic emission and its superiority to vibration, especially in the detection of bearing subsurface cracks. Through comparison of AE with vibration signals, this study confirms AE's superiority in detecting subsurface cracks in bearings and the absence of vibration symptoms of subsurface cracks.This diagnostic study has also shown the crucial ability of the cyclic logarithmic envelope iii spectrum in separating different cyc...

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Section: Support Vector Machinementioning

confidence: 99%

Multi-Sensor Condition Monitoring of Bearings Using Support Vector Machines

Kan¹

View full text Add to dashboard Cite

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Intelligent Method for Faults Diagnosis of Rolling Bearings via Chaos Optimized Support Vector Machine

Cited by 1 publication

References 7 publications

Multi-Sensor Condition Monitoring of Bearings Using Support Vector Machines

Multi-Sensor Condition Monitoring of Bearings Using Support Vector Machines

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