Time-Frequency Analysis of Gearbox Faults

Oehlmann, Harald; Brie, David; Tomczak, Marc; Richard, Alain

doi:10.1016/s1474-6670(17)42475-x

Cited by 3 publications

(4 citation statements)

References 3 publications

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“…Sparsity measures are based on some commonly used statistical parameters. Currently, the application of these techniques to an angle-time cyclostationary framework offers exciting possibilities for demodulation analysis to enhance the detection of smaller transients [22].…”

Section: Introductionmentioning

confidence: 99%

Synchronous median instantaneous power spectrum-gram (SM-IPSgram): A filter banks decomposition for identifying informative frequency band and a weighting function

Sibanda

2024

Mechanical Systems and Signal Processing

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Section: Introductionmentioning

confidence: 99%

Synchronous median instantaneous power spectrum-gram (SM-IPSgram): A filter banks decomposition for identifying informative frequency band and a weighting function

Sibanda

2024

Mechanical Systems and Signal Processing

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“…As explained in IV.C.2, this fault does not lead to characteristic frequencies [5]. This is the reason why methods scanning the entire spectrum are required (cepstrum, correlation method, time-frequency representation).…”

Section: ) Gearfault Detectionmentioning

confidence: 98%

“…When a tooth breaks down, the torque is applied on the following tooth and the shape is modified. Some authors [5] suggest to develop the torque in Fourier series. In this case, the amplitude of every frequency component in the spectrum should be characterized both in the healthy and the faulty cases [6], which would be much more difficult.…”

Section: B Pwm Inverter Modelmentioning

confidence: 99%

Signal processing tools for monitoring induction drive

Raison

Rostaing²,

Rognon³

IECON'99. Conference Proceedings. 25th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.99CH37029)

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This paper consists of three parts. In the first one, the faults under study are listed. The simulation models used to characterize the faults occurring in the electrical drive and to validate the fault detection and isolation methods are presented. In the second part, a spectral characterization is performed for each considered fault on the observable signals appropriate for each component of the drive (voltage, current or speed). Signal based detection methods are presented in the third part and are validated in simulation.

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Section: ) the Gear And The Transmissionmentioning

confidence: 99%

Induction drive speed monitoring by neural networks

Raison

Francois²,

Rostaing³

et al.

2000 26th Annual Conference of the IEEE Industrial Electronics Society. IECON 2000. 2000 IEEE International Conference on Indus

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This paper presents a fault detection and isolation (fdi) scheme with neural networks dedicated to the faults in the mechanical part of the drive. This method is designed to be suitable for a variable speed drive fed by an electronic device.After a presentation of the models of the drive components, faults characterization is reminded and the fdi scheme is introduced, trained and tested for different speeds.

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Time-Frequency Analysis of Gearbox Faults

Cited by 3 publications

References 3 publications

Synchronous median instantaneous power spectrum-gram (SM-IPSgram): A filter banks decomposition for identifying informative frequency band and a weighting function

Synchronous median instantaneous power spectrum-gram (SM-IPSgram): A filter banks decomposition for identifying informative frequency band and a weighting function

Signal processing tools for monitoring induction drive

Induction drive speed monitoring by neural networks

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