Novel Instantaneous Wavelet Bicoherence for Vibration Fault Detection in Gear Systems
Higher order spectra exhibit a powerful detection capability of low-energy fault-related signal components, buried in background random noise. This paper investigates the powerful nonlinear non-stationary instantaneous wavelet bicoherence for local gear fault detection. The new methodology of select...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-10-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/14/20/6811 |
| _version_ | 1797514629908267008 |
|---|---|
| author | Len Gelman Krzysztof Soliński Andrew Ball |
| author_facet | Len Gelman Krzysztof Soliński Andrew Ball |
| author_sort | Len Gelman |
| collection | DOAJ |
| description | Higher order spectra exhibit a powerful detection capability of low-energy fault-related signal components, buried in background random noise. This paper investigates the powerful nonlinear non-stationary instantaneous wavelet bicoherence for local gear fault detection. The new methodology of selecting frequency bands that are relevant for wavelet bicoherence fault detection is proposed and investigated. The capabilities of wavelet bicoherence are proven for early-stage fault detection in a gear pinion, in which natural pitting has developed in multiple pinion teeth in the course of endurance gearbox tests. The results of the WB-based fault detection are compared with a stereo optical fault evaluation. The reliability of WB-based fault detection is quantified based on the complete probability of correct identification. This paper is the first attempt to investigate instantaneous wavelet bicoherence technology for the detection of multiple natural early-stage local gear faults, based on comprehensive statistical evaluation of the industrially relevant detection effectiveness estimate—the complete probability of correct fault detection. |
| first_indexed | 2024-03-10T06:34:19Z |
| format | Article |
| id | doaj.art-0727cd3131774f2782dc52223cb37d3c |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T06:34:19Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-0727cd3131774f2782dc52223cb37d3c2023-11-22T18:09:10ZengMDPI AGEnergies1996-10732021-10-011420681110.3390/en14206811Novel Instantaneous Wavelet Bicoherence for Vibration Fault Detection in Gear SystemsLen Gelman0Krzysztof Soliński1Andrew Ball2Department of Engineering and Technology, School of Computing and Engineering, The University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UKMeggitt Sensing Systems, Rte de Moncor 4, 1701 Fribourg, SwitzerlandDepartment of Engineering and Technology, School of Computing and Engineering, The University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UKHigher order spectra exhibit a powerful detection capability of low-energy fault-related signal components, buried in background random noise. This paper investigates the powerful nonlinear non-stationary instantaneous wavelet bicoherence for local gear fault detection. The new methodology of selecting frequency bands that are relevant for wavelet bicoherence fault detection is proposed and investigated. The capabilities of wavelet bicoherence are proven for early-stage fault detection in a gear pinion, in which natural pitting has developed in multiple pinion teeth in the course of endurance gearbox tests. The results of the WB-based fault detection are compared with a stereo optical fault evaluation. The reliability of WB-based fault detection is quantified based on the complete probability of correct identification. This paper is the first attempt to investigate instantaneous wavelet bicoherence technology for the detection of multiple natural early-stage local gear faults, based on comprehensive statistical evaluation of the industrially relevant detection effectiveness estimate—the complete probability of correct fault detection.https://www.mdpi.com/1996-1073/14/20/6811condition monitoringfault detectionvibration analysis |
| spellingShingle | Len Gelman Krzysztof Soliński Andrew Ball Novel Instantaneous Wavelet Bicoherence for Vibration Fault Detection in Gear Systems Energies condition monitoring fault detection vibration analysis |
| title | Novel Instantaneous Wavelet Bicoherence for Vibration Fault Detection in Gear Systems |
| title_full | Novel Instantaneous Wavelet Bicoherence for Vibration Fault Detection in Gear Systems |
| title_fullStr | Novel Instantaneous Wavelet Bicoherence for Vibration Fault Detection in Gear Systems |
| title_full_unstemmed | Novel Instantaneous Wavelet Bicoherence for Vibration Fault Detection in Gear Systems |
| title_short | Novel Instantaneous Wavelet Bicoherence for Vibration Fault Detection in Gear Systems |
| title_sort | novel instantaneous wavelet bicoherence for vibration fault detection in gear systems |
| topic | condition monitoring fault detection vibration analysis |
| url | https://www.mdpi.com/1996-1073/14/20/6811 |
| work_keys_str_mv | AT lengelman novelinstantaneouswaveletbicoherenceforvibrationfaultdetectioningearsystems AT krzysztofsolinski novelinstantaneouswaveletbicoherenceforvibrationfaultdetectioningearsystems AT andrewball novelinstantaneouswaveletbicoherenceforvibrationfaultdetectioningearsystems |