Application of Wavelet Transform for Fault Diagnosis in Rotating Machinery

Bendjama, Hocine; Bouhouche, Salah; Boucherit, Mohamed Seghir

doi:10.7763/ijmlc.2012.v2.93

Cited by 62 publications

(36 citation statements)

References 19 publications

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“…Many signal analysis methods are able to extract useful information from vibration data. Currently, most of these methods use spectral analysis based on Fourier Transform (FT) [4]. Investigation of effect of vibrations at the bearings due to simulated faults like parallel misalignments, angular misalignment, combined parallel and angular misalignments and unbalances was done on a rotor rig.…”

Section: Literature Surveymentioning

confidence: 99%

Condition Monitoring of Air Compressor in Steel Industry using ANFIS

B¹,

Tjprc²

2019

IJMPERD

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As large rotating machines are increasingly employed in continuous operations at high speeds and with heavy loads, vibration behavior of rotating systems is emerging as more complex phenomenon. Monitoring vibration behaviour of large rotating machinery is an effective way to reduce losses and enhance safety, reliability, availability and durability in manufacturing processes. This research focuses on condition monitoring of one of the vital and the most critical machine Air Compressor of steel industry. It considers vibration levels of the machinery based on ISO limit of vibration severity using Adoptive neuro fuzzy inference system (ANFIS). Two different data schemes were formulated based on preliminary experimentation on Sugeno type 3 inputs (v Hm , v Vm & v Am) and 1 output (i. e., Condition) ANFIS model. The performance criterion of the ANFIS classifier was evaluated using confusion matrix. The total classification accuracy of 95% obtained proves the validation of the Air Compressor model. ANFIS can also be extended to condition monitoring of various rotating machinery.

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Section: Literature Surveymentioning

confidence: 99%

Condition Monitoring of Air Compressor in Steel Industry using ANFIS

B¹,

Tjprc²

2019

IJMPERD

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“…However, localized gear defects typically introduce non-stationary components into the signal [10], which cannot be studied by conventional frequency analysis techniques. In this scenario, the adoption of advanced techniques based on the time-frequency domain such as the Continuous Wavelet Transform (CWT) [10,19,20] DIAGNOSTYKA, Vol. 21, No.…”

Section: Introductionmentioning

confidence: 99%

Condition monitoring indicators for pitting detection in planetary gear units

Berlato¹,

D’Elia²,

Battarra³

et al. 2020

Diagnostyka

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In industrial field, there is an increasing demand for monitoring systems enabling predictive maintenance programs. In this context, the present work concerns the monitoring of distributed wear (pitting) in planetary gearboxes. For this purpose, some metrics of the synchronous average of the vibration signal, based on the statistical moment of the fourth order, are present in literature; in this paper, a new indicator is proposed, namely NA4mod. The effectiveness of this metric in identifying the early stage of pitting has been evaluated by conducting an accelerated life test of about 700 hours on a test bench using a back-to-back configuration. The paper introduces the proposed metric, describes the test, presents and dis-cusses the results. Metric NA4mod exhibits satisfactory capability to detect pitting with better reliability than other metrics in literature. In addition, the metric is shown to be sensitive to both early stage damage and pitting severity in the final stage. Results are verified by means of wavelet-transform analysis.

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“…Transform and wavelet transform [3,4,5]. Then, to localize sound sources using acoustic signals, some methods also have been developed.…”

Section: Icose Conference Proceedingsmentioning

confidence: 99%

Application of Short Time Fourier Transform and Wavelet Transform for Sound Source Localization Using Single Moving Microphone in Machine Condition Monitoring

Rusli¹

2016

KEG

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The paper discusses means to predict sound source position emitted by fault machine components based on a single microphone moving in a linear track with constant speed. The position of sound source that consists of some frequency spectrum is detected by time-frequency distribution of the sound signal through Short Time Fourier Transform (STFT) and Continues Wavelet Transform (CWT). As the amplitude of sound pressure increases when the microphone moves closer, the source position and frequency are predicted from the peaks of time-frequency contour map. Firstly, numerical simulation is conducted using two sound sources that generate four different frequencies of sound. The second case is experimental analysis using rotating machine being monitored with unbalanced, misalignment and bearing defect. The result shows that application of both STFT and CWT are able to detect multiple sound sources position with multiple frequency peaks caused by machine fault. The STFT can indicate the frequency very clearly, but not for the peak position. On the other hand, the CWT is able to predict the position of sound at low frequency very clearly. However, it is failed to detect the exact frequency because of overlapping.

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Application of Wavelet Transform for Fault Diagnosis in Rotating Machinery

Cited by 62 publications

References 19 publications

Condition Monitoring of Air Compressor in Steel Industry using ANFIS

Condition Monitoring of Air Compressor in Steel Industry using ANFIS

Condition monitoring indicators for pitting detection in planetary gear units

Application of Short Time Fourier Transform and Wavelet Transform for Sound Source Localization Using Single Moving Microphone in Machine Condition Monitoring

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