Early fault detection in gearboxes based on support vector machines and multilayer perceptron with a continuous wavelet transform

Jedliński, Łukasz; Jonak, Józef

doi:10.1016/j.asoc.2015.02.015

Cited by 113 publications

(50 citation statements)

References 14 publications

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“…As observed from the figures,signalsassociated with healthy and broken gearboxes can be differentiated based on their patterns. However, the constant time-frequency window used in the analysis usually makes STFTnot suitablefor analyzing complex vibrations signals that require the multi scale signal analysis [5]. In other words, once the length of the window is chosen, the entire analysis will be based onfixed time and frequency bandwidths.…”

Section: Resultsmentioning

confidence: 99%

“…The CWT as one of the most prominent tools in detecting transient signals offers a better compromise for time-frequency signal analysis. It has a fine localization of frequency content in the low frequency region and a fine localization of time contentat the high frequency region, which makes it as an excellent tool to diagnose gearbox fault [5]. As the impulsive feature of vibration signal exhibits a short time duration but a wide frequency bandwidth, CWT is one of the best candidates to be used to detect the pattern of vibration signal from the faulty gear.…”

Section: Resultsmentioning

confidence: 99%

“…However, the main problem with the method concerns about the constant resolution for all frequencies because it uses the same window for the analysis of the entire signal. On the other hand, the CWT has a multi-scale property,alower scale wavelet hasa high time resolution and a lower frequency resolution, but a higher scale wavelet has a high frequency resolution and a low time resolution [5]. Compared to STFT, CWT has the flexibility in adjusting its time-frequency resolutionwhich means thatit is more suitable to analyzethe impulsive signals associated with a faulty gearbox.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bevel Gearbox Fault Diagnosis using Vibration Measurements

Hartono

Halim

Widodo

et al. 2016

MATEC Web of Conferences

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Abstract.The use of vibration measurementanalysis has been proven to be effective for gearbox fault diagnosis. However, the complexity of vibration signals observed from a gearbox makes it difficult to accurately detectfaults in the gearbox. This work is based on a comparative studyof several time-frequency signal processing methods that can be used to extract information from transient vibration signals containing useful diagnostic information. Experiments were performed on a bevel gearbox test rig using vibration measurements obtained from accelerometers. Initially, thediscrete wavelet transform was implementedfor vibration signal analysis to extract the frequency content of signal from the relevant frequency region. Several time-frequency signal processing methods werethen incorporated to extract the fault features of vibration signals and their diagnostic performances were compared. It was shown thatthe Short Time Fourier Transform (STFT) could not offer a good time resolution to detect the periodicity of the faulty gear tooth due the difficulty in choosing an appropriate window length to capture the impulse signal. The Continuous Wavelet Transform (CWT), on the other hand, was suitable to detection of vibration transients generated by localized fault from a gearbox due to its multi-scale property. However, both methods still require a thorough visual inspection. In contrast, it was shown from the experiments that the diagnostic method using the Cepstrumanalysis could provide a direct indication of the faulty tooth without the need of a thorough visual inspection as required by CWT and STFT.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bevel Gearbox Fault Diagnosis using Vibration Measurements

Hartono

Halim

Widodo

et al. 2016

MATEC Web of Conferences

View full text Add to dashboard Cite

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“…Once it is chosen, it will be used in the entire analysis since STFT has a constant time-frequency resolution in the entire time-frequency plot. Therefore it is not suitable for analyzing gearbox vibration signals that contain multi-scale components [13]. …”

Section: Figure 6 Tsa Vibration Signal From a Healthy Gearboxmentioning

confidence: 99%

Vibration-based Fault Diagnostic of a Spur Gearbox

et al. 2016

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Abstract. This paper presents comparative studies of Fast Fourier Transform (FFT), Short Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) as several advanced time-frequency analysis methods for diagnosing an early stage of spur gear tooth failure. An incipient fault of a chipped tooth was investigated in this work using vibration measurements from a spur gearbox test rig. Time Synchronous Averaging was implemented for the analysis to enhance the clarity of fault feature from the gear of interest. Based on the experimental results and analysis, it was shown that FFT method could identify the location of the faulty gear with sufficient accuracy. On the other hand, Short Time Fourier Transform method could not provide the angular location information of the faulty gear. It was found that the Continuous Wavelet Transform method offered the best representation of angle-frequency representation. It was not only able to distinguish the difference between the normal and faulty gearboxes from the joint angle-frequency results but could also provide an accurate angular location of the faulty gear tooth in the gearbox.

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“…Reliable condition monitoring techniques are essential when performing condition-based maintenance on expensive rotating machine assets [1,2]. Advanced signal processing [3][4][5][6][7][8][9][10][11][12][13] and sophisticated supervised machine learning techniques [14][15][16][17][18][19][20][21][22][23] are actively investigated to improve the condition monitoring task. Deep learning techniques have also recently been used to not only infer the condition of the machine, but also to extract features from the raw dataset i.e.…”

Section: Introductionmentioning

confidence: 99%