Bearing Diagnostics Based on Pattern Recognition of Statistical Parameters

Xi, Fengfeng; Sun, Qiao; Krishnappa, G.

doi:10.1177/107754630000600303

Cited by 42 publications

(6 citation statements)

References 2 publications

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“…Also, the FMECA method classifies the FMs using risk priority numbers (RPN) that are calculated with three failure mode parameters: occurrence (Occ), detection (Det) and severity (Sev). So, the FMECA [11] allows the definition of the appropriate detection method and measures to be used in the diagnostics as well as in the prognostics of the failure modes.…”

Section: Prognostics Processmentioning

confidence: 99%

A TSK-Type Recurrent Neuro-Fuzzy Systems for Fault Prognosis

Mahdaoui¹,

Mouss²

2012

JSEA

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As a result from the demanding of process safety, reliability and environmental constraints, a called of fault detection and diagnosis system become more and more important. In this article some basic aspects of TSK (Takigi Sugeno Kang) neuro-fuzzy techniques for the prognosis and diagnosis of manufacturing systems are presented. In particular, a neuro-fuzzy model that can be used for the identification and the simulation of faults prognosis models is described. The presented model is motivated by a cooperative neuro-fuzzy approach based on a vectorized recurrent neural network architecture. The neuro-fuzzy architecture maps the residuals into two classes: a one of fixed direction residuals and another one of faults belonging to rotary kiln

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Section: Prognostics Processmentioning

confidence: 99%

A TSK-Type Recurrent Neuro-Fuzzy Systems for Fault Prognosis

Mahdaoui¹,

Mouss²

2012

JSEA

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“…previous research work has shown that each feature is only effective for certain defect at certain stage. For example, spikiness of the acoustic emission signals indicated by Kurtosis implies incipient defects, whereas the high energy level given by the value of RMS indicates severe defects (Xi F and Krishnappa, 2000). A good performance assessment method should take advantage of mutual information from multiple features for system degradation (hazard) assessment.…”

Section: Background Of Gearbox Prognosticsmentioning

confidence: 99%

Robust Prognostics Concept for Gearbox with Artificially Induced Gear Crack Utilizing Acoustic Emission

Abouel-Seoud¹,

Elmorsy²,

Dyab³

2011

EER

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Prognostic is a rapidly developing field and seeks to build on current diagnostic equipment capabilities for predicting the system state in advance. In machine condition prognostics, the current and past observations are used to predict the upcoming states of the machine. Signal-de-noising and extraction of the weak acoustic signature are crucial to gearbox prognostics since the inherent deficiency of the measuring mechanism often introduces a great amount of noise to the signal. In addition, the signature of a defective gearbox is spread across a wide frequency band and hence can easily become masked by noise and low frequency effects. As a result, robust concepts are needed to provide more evident information for gearbox performance assessment and prognostics. This paper introduces enhanced and robust prognostic concepts for gear tooth based on an optimal wavelet filter method for fault identification and a statistical method for performance degradation assessment. The experimental results demonstrate that the gear tooth defect can be detected and evaluated at an early stage of development when both the optimal wavelet filter and statistical analysis technique are used.

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“…This inherently implies a need to consider the statistical distribution of the residual series. In fact, when the distribution is studied in detail, it alone offers clues about the state of damage of the system [5,6,14,15]. For that reason, this section is dedicated to observations made with regards to characteristics of the distribution of the residuals and direct comparison to the normal distribution.…”

Section: Normality Assumptionmentioning

confidence: 99%

Damage detection and localization based on outlying residuals

Mattson

Pandit

2006

Smart Mater. Struct.

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A method based on outlier identification in model prediction errors is introduced for locating damage in space and time. The technique eliminates the need for explicit information about the response of a healthy system. It only requires that the healthy system be described by a predictive model. This technique can be combined with a previously published method based solely on moment statistics of the prediction errors. In combination, these techniques provide extremely reliable damage identification and localization without explicit baseline information. The power of the method lies in the ability to incorporate virtually any predictive model to adequately characterize the measured signals. Once this adequate model is available, and prediction errors are calculated, outlier tests can be used to pinpoint the location in space and time where that damage signature occurs. The technique is illustrated using benchmark data from Los Alamos National Laboratory available through the internet.

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Bearing Diagnostics Based on Pattern Recognition of Statistical Parameters

Cited by 42 publications

References 2 publications

A TSK-Type Recurrent Neuro-Fuzzy Systems for Fault Prognosis

A TSK-Type Recurrent Neuro-Fuzzy Systems for Fault Prognosis

Robust Prognostics Concept for Gearbox with Artificially Induced Gear Crack Utilizing Acoustic Emission

Damage detection and localization based on outlying residuals

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