Cyclostationary analysis with logarithmic variance stabilisation

Borghesani, Pietro; Shahriar, Rifat

doi:10.1016/j.ymssp.2015.08.014

Cited by 45 publications

(22 citation statements)

References 11 publications

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“…However, the high level of separation for non-symptomatic cyclic frequencies (up until 70% for the highest spectral bands) indicates the exposure of this indicator to the risk of false alarms and masking by non-bearing related CS2 sources. This cyclic leakage has been discussed in [52], and proves the advantages of CLES. An additional test to practically demonstrate the masking risk has been conducted on the same test-rig adding a percussor on the disk of the input shaft.…”

Section: Separationsupporting

confidence: 57%

“…As for vibration bursts, which are usually only induced at the next stage (spalling), the bursts should occur at the bearing characteristic fault frequencies BPFO, BPFI, BSF and FTF, coherently with the cyclic loading of the cracked location. For instance, a crack on the outer-race of the bearing is expected to induce AE bursts at a cyclic frequency = BPFO, with resulting peaks at this cyclic frequency and its multiples on cyclic frequency indicators such as squared-envelope spectrum (SES) and cyclic modulation spectrum (CMS [52], is exacerbated by the AE narrow band sensor, which forces all CS2…”

Section: Acoustic Emissionmentioning

confidence: 99%

“…analysis and is a popular technique because of its simplicity in its application, the effectiveness and the low computational cost [45,52]. This method is widely-used for demodulation to extract the fault feature of a rolling element bearing since vibration signals from faulty roller bearings contain the feature of modulation [80].…”

Section: Envelope Analysismentioning

confidence: 99%

“…Considering this limitation of SES, log-envelope spectrum (LES) has recently been proposed to improve the performance under multiple CS2 components [89]. The computation of LES is very similar to SES.…”

Section: Envelope Analysismentioning

confidence: 99%

“…Cyclostationary analysis is another important tool that is used throughout this study in [52]. In the same study, a newly-proposed indicator called cyclic logarithmic envelope spectrum (CLES) was proposed as a solution to the problem.…”

Section: Cyclostationary Analysismentioning

confidence: 99%

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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: Separationsupporting

confidence: 57%

Section: Acoustic Emissionmentioning

confidence: 99%