A New, Non-Linear, Adaptive, Blind Source Separation Approach to Gear Tooth Failure Detection and Analysis

Roan, Michael J.; Erling, Josh G.; Sibul, Leon H.

doi:10.1006/mssp.2002.1504

Cited by 56 publications

(26 citation statements)

References 14 publications

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“…Past condition monitoring techniques of gearboxes have used many different signal processing approaches such as synchronous time series averaging 17 , amplitude and phase demodulation 18 , time-frequency distribution 19 and wavelet analysis 20 . The use of statistical signal processing approaches have also taken a hold in gear tooth failure detection 21 . Non-linear adaptive algorithms for independent component analysis (ICA) have been shown to separate unknown, statistically independent sources that have been mixed in dynamic systems.…”

Section: Time Reversalmentioning

confidence: 99%

Signal Processing in Physical and Engineering Acoustics

Chambers¹,

Anderson²,

Ferguson³

et al. 2011

Acou. Today

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Section: Time Reversalmentioning

confidence: 99%

Signal Processing in Physical and Engineering Acoustics

Chambers¹,

Anderson²,

Ferguson³

et al. 2011

Acou. Today

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“…The means, standard deviations, and thresholds are given in Table V. Note that analysis for the macro-pitting, multiple teeth failures only included 9 out of the 12 total sets for this failure mode (sets 4,5,7,8,9,12,16,20,23). This was due to difficulty in classifying the faulty data regimes for the excluded sets (sets 6, 11, 21).…”

Section: Quantitative Analysis Of Gear Fault Detectionmentioning

confidence: 99%

“…Dempsey, et al, presents a summary of current methods to identify gear health, with emphases to FAA and U.S. Army rotorcraft applications [5]. Recent refinements to vibration-based gear fault detection have been made [6][7][8] along with other methods such as vibro-acoustics [9], acoustic emission [10], and impact velocity modeling [11]. A common theme noticed is that experimental data verifying fault detection In a recent study on face gear endurance [12], a number of test sets were instrumented with a gear fault detection system and run until failure.…”

Section: Introduction mentioning

confidence: 99%

Gear Fault Detection Effectiveness as Applied to Tooth Surface Pitting Fatigue Damage

Lewicki

Dempsey²,

Heath

et al. 2009

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY) May 20092. REPORT TYPE ARL-RP-0247 SPONSOR/MONITOR'S ACRONYM(S) 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) SPONSOR/MONITOR'S REPORT NUMBER(S) DISTRIBUTION/AVAILABILITY STATEMENTApproved for public release; distribution unlimited. SUPPLEMENTARY NOTESPresented at the American Helicopter Society 65th Annual Forum, Grapevine, Texas, May 27-29, 2009. ABSTRACTA study was performed to evaluate fault detection effectiveness as applied to gear tooth pitting fatigue damage. Vibration and oil-debris monitoring (ODM) data were gathered from 24 sets of spur pinion and face gears run during a previous endurance evaluation study. Three common condition indicators (RMS, FM4, and NA4) were deduced from the time-averaged vibration data and used with the ODM to evaluate their performance for gear fault detection. The NA4 parameter showed to be a very good condition indicator for the detection of gear tooth surface pitting failures. The FM4 and RMS parameters performed average to below average in detection of gear tooth surface pitting failures. The ODM sensor was successful in detecting a significant amount of debris from all the gear tooth pitting fatigue failures. Excluding outliers, the average cumulative mass at the end of a test was 40 mg. SUBJECT TERMSGears, fault detection, vibration,; oil debris, receiver operating characteristic curve ABSTRACTA study was performed to evaluate fault detection effectiveness as applied to gear tooth pitting fatigue damage. Vibration and oil-debris monitoring (ODM) data were gathered from 24 sets of spur pinion and face gears run during a previous endurance evaluation study. Three common condition indicators (RMS, FM4, and NA4) were deduced from the timeaveraged vibration data and used with the ODM to evaluate their performance for gear fault detection. The NA4 parameter showed to be a very good condition indicator for the detection of gear tooth surface pitting failures. The FM4 and RMS parameters performed average to below average in detection of gear tooth surface pitting failures. The ODM sensor was successful in detecting a significant amount of debris fro...

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“…Roan, etc. [10] presented a non-linear BSS approach and applied it to failure detection of gear tooth. Shen and Yang [11] presented a novel BSS method based on Fractional Fourier Transform, which could be used to separate the mixed non-stationary signals successfully and was applied for fault diagnosis of rolling bearing in freight train successfully.…”

Section: Introductionmentioning

confidence: 99%

Two De-Noising Methods Based on Gabor Transform

Shen

Zhang

Yang

et al. 2011

AEF

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Abstract. Two de-noising methods, named as the averaging method in Gabor transform domain (AMGTD) and the adaptive filtering method in Gabor transform domain (AFMGTD), are presented in this paper. These two methods are established based on the correlativity of the source signals and the background noise in time domain and Gabor transform domain, that is to say, the uncorrelated source signals and background noise in time domain would still be uncorrelated in Gabor transform domain. The construction and computation scheme of these two methods are investigated. The de-noising performances are illustrated by some simulation signals, and the wavelet transform is used to compare with these two new de-noising methods. The results show that these two methods have better de-noising performance than the wavelet transform, and could reduce the background noise in the vibration signal more effectively.

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A New, Non-Linear, Adaptive, Blind Source Separation Approach to Gear Tooth Failure Detection and Analysis

Cited by 56 publications

References 14 publications

Signal Processing in Physical and Engineering Acoustics

Signal Processing in Physical and Engineering Acoustics

Gear Fault Detection Effectiveness as Applied to Tooth Surface Pitting Fatigue Damage

Two De-Noising Methods Based on Gabor Transform

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