Vibration‐based wind turbine planetary gearbox fault diagnosis using spectral averaging

Yoon, Jae; He, David; Hecke, Brandon Van; Nostrand, Thomas J.; Zhu, Junda; Bechhoefer, Eric

doi:10.1002/we.1940

Cited by 30 publications

(17 citation statements)

References 24 publications

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“…However, before the filtering process, the raw vibration signal is time synchronous averaged. The time synchronous averaging (TSA) of the raw vibration is required to extract the periodic waveforms (frequencies related to the specific shaft rotational speed) and remove the additive, background and non‐stationary noise component . For removing/filtering out the GMF and harmonic frequency components, first, the TSA signal was converted into the frequency/order domain from the time domain.…”

Section: Resultsmentioning

confidence: 99%

Gear pitting severity level identification using binary segmentation methodology

Kundu

Darpe

Kulkarni

2019

Struct Control Health Monit

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With growth of a defect on the gear tooth surface, vibration response of the geared rotor system changes, which can be quantified using a health indicator. Based on the health indicator, identification of an accurate health stage or categorization is crucial so that the pitting severity classification can be done precisely. In all the prior reported works, the fault severity classification approaches are applied to the seeded pitting fault, and hence, exact state change point is known in advance. However, in the real life, the gear tooth surface is subjected to a natural pitting progression, and exact state change point of the gear is not known a priori. This study presents a binary segmentation methodology for identification of multiple degradation stages such as initial pitting, medium pitting, and severe pitting in a spur gear subjected to natural pitting progression. The gear damage progression is represented by a correlation coefficient-based degradation parameter extracted from the residual vibration signal. Based on the change in distributional properties such as mean and variance of the degradation parameter with time progression, three degradation stages of the gear are identified. A flexible miniature inspection camera is used to access and assess the actual physical damage on the gear tooth surface. Based on the visual inspection pictures, actual damage area at the identified degradation stages is quantified. Performance of the binary segmentation methodology is validated through six run-to-failure accelerated gear pitting experiments.

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Section: Resultsmentioning

confidence: 99%

Gear pitting severity level identification using binary segmentation methodology

Kundu

Darpe

Kulkarni

2019

Struct Control Health Monit

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“…It is basic knowledge currently, so we will skip the mathematical foundation of this. Spectral analysis (SA) is one of the most popular techniques for vibration data processing because the analysis of frequency content allows finding a physical link to the rotating parts of the machine [ 17 ], and it has found many applications [ 11 , 24 , 53 , 54 ]. One of the most critical limitations of SA is the requirement regarding the stationarity of the signal.…”

Section: Methodsmentioning

confidence: 99%

“…Spectral analysis is one of the most frequently used in engineering practice. An explanation of the feature extraction phase will be minimized, and we will refer to [ 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

Decision Tree-Based Classification for Planetary Gearboxes’ Condition Monitoring with the Use of Vibration Data in Multidimensional Symptom Space

Lipiński

Brzychczy

Zimroz

2020

Sensors

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Monitoring the condition of rotating machinery, especially planetary gearboxes, is a challenging problem. In most of the available approaches, diagnostic procedures are related to advanced signal pre-processing/feature extraction methods or advanced data (features) analysis by using artificial intelligence. In this paper, the second approach is explored, so an application of decision trees for the classification of spectral-based 15D vectors of diagnostic data is proposed. The novelty of this paper is that by a combination of spectral analysis and the application of decision trees to a set of spectral features, we are able to take advantage of the multidimensionality of diagnostic data and classify/recognize the gearbox condition almost faultlessly even in non-stationary operating conditions. The diagnostics of time-varying systems are a complicated issue due to time-varying probability densities estimated for features. Using multidimensional data instead of an aggregated 1D feature, it is possible to improve the efficiency of diagnostics. It can be underlined that in comparison to previous work related to the same data, where the aggregated 1D variable was used, the efficiency of the proposed approach is around 99% (ca. 19% better). We tested several algorithms: classification and regression trees with the Gini index and entropy, as well as the random tree. We compare the obtained results with the K-nearest neighbors classification algorithm and meta-classifiers, namely: random forest and AdaBoost. As a result, we created the decision tree model with 99.74% classification accuracy on the test dataset.

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“…Lei et al [8] proposed an adaptive stochastic resonance method to suppress noise interference and enhance fault signature and applied it to weak fault feature extraction of planetary gearboxes. Yoon et al [9] developed spectral averaging via enveloping and Welch's spectral averaging for planetary gearbox fault diagnosis. These works have enriched our understanding on planetary gearbox fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Planetary Gearbox Fault Diagnosis via Torsional Vibration Signal Analysis in Resonance Region

Feng

Liang

2017

Shock and Vibration

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Planetary gearbox torsional vibration signals are free from the extra amplitude modulation effect due to time-varying transmission paths and have simpler frequency structure than translational ones. Gear faults result in modulation on the torsional resonance vibration and are manifested by the modulation feature. These merits are exploited for planetary gearbox fault diagnosis in this paper. Gear fault induced torsional vibrations in resonance region are modelled as amplitude modulation and frequency modulation (AM-FM) processes, the explicit equation of Fourier spectrum is derived, and the sideband characteristics are summarized. To avoid complex sideband analysis, amplitude and frequency demodulation analysis methods are exploited. The equations of amplitude and frequency demodulated spectra are derived in closed form, and their frequency structures are revealed. For fault diagnosis based on above theoretical derivations, a resonance frequency identification approach is proposed through time-frequency analysis of torsional vibrations during variable speed processes, according to the independence nature of resonance frequency on running conditions. The theoretical derivations and proposed approach are illustrated by numerical simulated signal analysis and are further validated through dynamics modelling and lab experimental tests. Localized faults on the sun, planet, and ring gears are successfully diagnosed.

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Vibration‐based wind turbine planetary gearbox fault diagnosis using spectral averaging

Cited by 30 publications

References 24 publications

Gear pitting severity level identification using binary segmentation methodology

Gear pitting severity level identification using binary segmentation methodology

Decision Tree-Based Classification for Planetary Gearboxes’ Condition Monitoring with the Use of Vibration Data in Multidimensional Symptom Space

Planetary Gearbox Fault Diagnosis via Torsional Vibration Signal Analysis in Resonance Region

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