Stochastic Prognostics for Rolling Element Bearings

Li, Y.; Kurfess, Thomas R.; Liang, Steven Y.

doi:10.1006/mssp.2000.1301

Cited by 181 publications

(98 citation statements)

References 11 publications

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“…Ray and Tangirala [104] used a non-linear stochastic model of fatigue crack dynamics for real-time computation of the time-dependent damage rate and accumulation in mechanical structures. Li et al [105,106] introduced two defect propagation models via mechanistic modeling for RUL estimation of bearings. Oppenheiner and Loparo [107] applied a physical model for predicting the machine condition in combination with fault strengths to life model based on crack growth law to estimate RUL.…”

Section: Model-based Approachesmentioning

confidence: 99%

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Machine Fault Diagnosis and Prognosis: The State of The Art

Tran¹,

Yang²

2009

International Journal of Fluid Machinery and Systems

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Machine fault diagnostic and prognostic techniques have been the considerable subjects of condition-based maintenance system in the recent time due to the potential advantages that could be gained from reducing downtime, decreasing maintenance costs, and increasing machine availability. For the past few years, research on machine fault diagnosis and prognosis has been developing rapidly. These publications covered in the wide range of statistical approaches to model-based approaches. With the aim of synthesizing and providing the information of these researches for researcher's community, this paper attempts to summarize and classify the recent published techniques in diagnosis and prognosis of rotating machinery. Furthermore, it also discusses the opportunities as well as the challenges for conducting advance research in the field of machine prognosis.

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Section: Model-based Approachesmentioning

confidence: 99%

“…Model-based approaches [104][105][106][107][108][109][110][111][112][113][114] • Can be highly accurate • Require less data then data-driven approaches…”

Section: Approachesmentioning

confidence: 99%

Machine Fault Diagnosis and Prognosis: The State of The Art

Tran¹,

Yang²

2009

International Journal of Fluid Machinery and Systems

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“…These methods could be very accurate when a correct model can be built up. For example, several models have been developed to characterize the crack growth [4,5]. However, due to the limited knowledge of underlying mechanisms and physics, it becomes very difficult, or even impossible, to fully understand the evolution of defects and faults of complex engineered systems [1,2].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis and Health Assessment for Rotating Machinery Based on Kernel Density Estimation and Kullback-Leibler Divergence

Yu¹,

Yan²,

Zhang³

2017

Fault Diagnosis and Detection

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To avoid severe damages and unexpected shutdowns, fault diagnosis and health assessment of rotating machinery have received considerable attention in recent years. On the other hand, as a great amount of data become acquirable and accessible in industry, data-driven tools have become an emerging research area, acting as a complement to the model-based (or physics-based) fault diagnosis and health assessment methods. In this chapter, based on the kernel density estimation (KDE) and the Kullback-Leibler divergence (KLID), a new data-driven fault diagnosis approach and a new health assessment approach are introduced. By utilizing the KDE, the statistical distribution of selected features can be readily estimated without assuming any parametric family of distributions, whereas the KLID is able to quantify the discrepancy between two probability distributions of selected features. An integrated Kullback-Leibler divergence, which aggregates the KLID of all the selected features, is introduced to discriminate various fault types or health status of rotating machinery. The effectiveness of the proposed approaches is demonstrated through three case studies of fault diagnosis and health assessment of rotating machinery.

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“…These approaches use residuals as features, which are the outcomes of consistency checks between the sensed measurements of system and the outputs of a mathematical model [4]. Some of the published researches using these approaches can be found in references [3][4][5][6][7][8]. However, these techniques are merely applied for some specific components and each requires a different mathematical model.…”

Section: Introductionmentioning

confidence: 99%

Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems

Tran

Yang

Tan

2009

Expert Systems with Applications

101

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This paper presents an approach to predict the operating conditions of machine based on classification and regression trees (CART) and adaptive neuro-fuzzy inference system (ANFIS) in association with direct prediction strategy for multi-step ahead prediction of time series techniques. In this study, the number of available observations and the number of predicted steps are initially determined by using false nearest neighbor method and auto mutual information technique, respectively. These values are subsequently utilized as inputs for prediction models to forecast the future values of the machines' operating conditions. The performance of the proposed approach is then evaluated by using real trending data of low methane compressor. A comparative study of the predicted results obtained from CART and ANFIS models is also carried out to appraise the prediction capability of these models. The results show that the ANFIS prediction model can track the change in machine conditions and has the potential for using as a tool to machine fault prognosis.

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Stochastic Prognostics for Rolling Element Bearings

Cited by 181 publications

References 11 publications

Machine Fault Diagnosis and Prognosis: The State of The Art

Machine Fault Diagnosis and Prognosis: The State of The Art

Fault Diagnosis and Health Assessment for Rotating Machinery Based on Kernel Density Estimation and Kullback-Leibler Divergence

Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems

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