Toward Helicopter Gearbox Diagnostics From a Small Number of Examples

Zacksenhouse, Miriam; Braun, Simon; Feldman, Mark A.; Sidahmed, M.

doi:10.1006/mssp.2000.1297

Cited by 39 publications

(30 citation statements)

References 10 publications

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“…Conventional statistical pattern recognition methods and artificial neural networks (ANNs) classifiers are studied based on the premise that the sufficient samples are available, which is not always true in practice [19]. Support vector machines (SVMs, as defined in Section 3) based on statistical learning theory that are of specialties for a smaller sample number have better generalization than ANNs and guarantee the local and global optimal solution are exactly the same [20].…”

Section: Introductionmentioning

confidence: 99%

A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM

2007

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Section: Introductionmentioning

confidence: 99%

A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM

2007

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“…In most cases, the modulation waveforms are also sinusoids with lower shaft orders, i.e., 1× and/or 2× the shaft frequency. In cases where the shaft has multiple gears, the signal will be more complex due to the cross gear modulation interaction [2]. When a localized tooth defect such as a tooth crack is present, the engagement of the cracked tooth will induce an impulsive change with comparatively low energy to the gear mesh signal.…”

Section: Introductionmentioning

confidence: 99%

<title>Toward dynamic model-based prognostics for transmission gears</title>

Wang

2002

SPIE Proceedings

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This paper presents a novel methodology for the diagnosis and prognosis of crucial gear faults, such as gear tooth fatigue cracking. Currently, an effective detection of tooth cracking can be achieved by using the autoregressive (AR) modeling approach, where the gear vibration signal is modeled by an AR model and gear tooth cracking is detected by identifying the sudden changes in the model's error signal. The model parameters can be estimated under the criteria of minimum power or maximum kurtosis of model errors. However, these model parameters possess no physical meaning about the monitored gear system. It is proposed that the AR model be replaced by a gear dynamics model (GDM) that contains physically meaningful parameters, such as mass, damping and stiffness. By identifying and tracking the changes in the parameters, it is possible to make diagnosis and prognosis of gear faults. For example, a reduction in mesh stiffness may indicate cracking of a gear tooth. Towards physical model-based prognosis, an adaptive (or optimization) strategy has been developed for approximating a gear signal using a simplified gear signal model. Preliminary results show that this strategy provides a feasible adaptive process for updating model parameters based on measured gear signal.

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“…Comparing two outstanding and application effective techniques in diagnosing rotating machineries, i.e., the artificial neural networks (ANNs) and the support vector machines (SVMs), the latter proves to attain higher accuracy and a better generalization power for a smaller number of training samples when used in non-linear pattern recognition tasks [8], [9]. This paper describes a multi-model approach for monitoring and diagnosing the health of bearings under different operation regimes such as speed, torque, stress and/or load.…”

Section: Introductionmentioning

confidence: 99%

A multi-model approach for anomaly detection and diagnosis using vibration signals

Balanica

Liao

Claussen

et al. 2013

2013 IEEE Conference on Prognostics and Health Management (PHM)

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Continuous vibration monitoring of mechanical roller bearing parts potentially reduces machine downtime through timely prediction and diagnosis of abnormal events. Despite the progress made in the literature, challenges remain in how to assess performance related information for maintenance decision-making from large data streams. Furthermore, since roller bearings operate under various regimes (e.g., speed and load), it is not trivial to consider the effect of regime changes in the modeling in order to reduce false alarms. The paper describes a multi-model approach to monitor the condition of roller bearings under different operating regimes. Two modeling approaches for anomaly and degradation monitoring are proposed to automatically retrieve information from the data. A self-organizing map (SOM) and a support vector machines (SVM) are used comparatively for the evaluation of a bearing degradation in time (i.e., a dynamic health indicator) and for the determination of changes in the tracked features. The proposed method is validated using data from multiple bearings of the same type.

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Toward Helicopter Gearbox Diagnostics From a Small Number of Examples

Cited by 39 publications

References 10 publications

A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM

A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM

<title>Toward dynamic model-based prognostics for transmission gears</title>

A multi-model approach for anomaly detection and diagnosis using vibration signals

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