Prognostics of slurry pumps based on a moving-average wear degradation index and a general sequential Monte Carlo method

Wang, Dong; Tse, Peter W.

doi:10.1016/j.ymssp.2014.10.010

Cited by 71 publications

(31 citation statements)

References 37 publications

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“…Calculation of the square root of the covariance matrix is a major disadvantage of the unscented Kalman filter because this step is sometimes unstable. In addition to various variants of the Kalman filters, the particle filer also attracts considerable attention for prognostics . The major idea of particle filter is to use many particles and their associate weights to approximate the posterior PDF.…”

Section: Other Models and Some Applicationsmentioning

confidence: 99%

Stochastic modelling and analysis of degradation for highly reliable products

Xie

2014

Appl Stoch Models Bus & Ind

561

240

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Degradation models have become an important analytic tool for complex systems. During the last two decades, a number of degradation models have been developed to capture the degradation dynamics of a system and aid the subsequent decision-makings. This paper is aimed at providing a summary of the state of the arts in the field, and discussing some further research issues from both analytical and practical point of view. In this paper, degradation models are classified into three classes, that is, stochastic process models, general path models, and other models beyond these two classes. A review on the three classes is given with emphasis on the class of stochastic process models. A comprehensive comparison between stochastic process models and general path models is given to expound the pros and cons of these two methods. Applications of degradation models in degradation test planning and burn-in modelling will also be discussed.

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Section: Other Models and Some Applicationsmentioning

confidence: 99%

Stochastic modelling and analysis of degradation for highly reliable products

Xie

2014

Appl Stoch Models Bus & Ind

561

240

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“…Based on the new vector x t , calculate the updating criterion by using (1). The parameters of those components in the recognized hidden state are updated by using (3). Denote the new adapted model as φ ( )′ t , and go to Step 5.…”

Section: Add Of a Hidden Statementioning

confidence: 99%

“…Condition-based maintenance (CBM), which implements preventive maintenance actions based on sensor information, is crucial to the efficient operation of machine running procedures that are subject to continuous health degradation. The primary goal of CBM is to use suitable sensor signals and monitoring techniques to identify and predict the health states of machines in order to reduce economic loss as a result of degradation or failure [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Adaptive hidden Markov model-based online learning framework for bearing faulty detection and performance degradation monitoring

2017

Mechanical Systems and Signal Processing

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“…State-Of-Health evaluations and assessments of the actual wear state are important advanced features of modern condition monitoring systems [1][2][3][4][5][6][7][8][9]. Evaluations of the hazard rate/probability of failure (load-dependent) and the actual remaining lifetime are goals in the development of advanced methods and implementations [6,10].…”

Section: Introductionmentioning

confidence: 99%

“…For example, a SVM is used in [8] for wear degree classification of slurry pumps. Another method for monitoring slurry pumps was proposed in [9], where a moving-average wear degradation index is classified.…”

Section: Introductionmentioning

confidence: 99%

New Approaches for Supervision of Systems with Sliding Wear: Fundamental Problems and Experimental Results Using Different Approaches

Söffker

Rothe

2017

Applied Sciences

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Reliability and availability of technically complex and safety-critical systems are of increasing importance. Besides the degree of wear, the quality of mechanical systems is significant for the system reliability. The focus of this contribution is the development and application of readily applicable and easily interpretable algorithms for industrial data obtained from technical systems during operation. The methods are within the focus of the production-oriented automation programs (Industrial Internet, Automation 4.0, China 2025). In this contribution as example a hydraulically driven machine in which parts slide over each other is chosen as sliding wear example. Monitoring is applied to distinguish normal and abnormal operation as well as to define end of useful lifetime. In this contribution four different methods will be introduced and experimentally compared without the availability of objective information about the wear state. The approaches differ with respect to the used measurements and data preparation. As measurements Acoustic Emission and the hydraulic pressure of the driving machine are used. For processing the accumulation of damage related values, a machine learning algorithm, and a sensitivity matrix are used. For comparison the experimental validation is based on identical data sets. Different operational states of the system denoted as actual system state are defined and classified. The comparison shows that the four introduced methods provide similar classification results although the underlying measurements are based on different physical principles. The newly introduced approaches allow online evaluation of the actual system state and can serve within improved maintenance strategies.

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Prognostics of slurry pumps based on a moving-average wear degradation index and a general sequential Monte Carlo method

Cited by 71 publications

References 37 publications

Stochastic modelling and analysis of degradation for highly reliable products

Stochastic modelling and analysis of degradation for highly reliable products

Adaptive hidden Markov model-based online learning framework for bearing faulty detection and performance degradation monitoring

New Approaches for Supervision of Systems with Sliding Wear: Fundamental Problems and Experimental Results Using Different Approaches

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