Prognostic modelling options for remaining useful life estimation by industry

Sikorska, Joanna; Hodkiewicz, Melinda; Ma, Lin

doi:10.1016/j.ymssp.2010.11.018

Cited by 779 publications

(577 citation statements)

References 150 publications

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“…A comprehensive condition monitoring program consists of three phases, namely, feature extraction, fault diagnosis, and prediction [3]. Feature extraction and fault diagnosis are usually used in detecting the abnormal state, determining the fault location, and predicting the failure extent [4]. Prognostic techniques relate to the remaining useful life (RUL) prediction, which is used in planning an effective maintenance strategy that can improve system reliability [5].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

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Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

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Section: Introductionmentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

View full text Add to dashboard Cite

show abstract

“…The necessity of data modelling to determine alarm threshold has been shown by [8], where he considered threshold setting using Chebyshev's inequality, Weibull and Pareto distributions. On the other hand, arguments in favor of other distributions, especially heavy-tailed ones, has been made in [9].…”

Section: Introductionmentioning

confidence: 99%

Automatic calculation of thresholds for load dependent condition indicators by modelling of probability distribution functions – maintenance of gearboxes used in mining conveying system

Wodecki¹,

Stefaniak²,

Michalak³

et al. 2017

Vib. proced.

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Abstract. Limit values for gearbox vibration-based condition indicators are key to determine in order to be able to estimate moment when object is in a need of maintenance. Further decision making process usually might utilize simple if-then-else rule using established threshold values. If diagnostic data takes the values from the Gaussian distribution, finding the decision boundaries is not difficult. Simplistically, that comes down to standard pattern recognition technique for "good condition" and "bad condition" based on probability density functions (PDFs) of diagnostic data. This situation is becoming more and more complicated when distribution is not Gaussian. Such cases require to develop much more advanced analytically solution. In this paper, we present the case of belt conveyor's gearbox for which PDFs of diagnostic features overlap each other because of strong influence of time varying operating conditions on spectral features. New approach to automatic threshold recognition has been proposed based on modeling diagnostic features with Weibull distribution and using agglomerative clustering to distinguish classes of technical condition, which leads to determination of thresholds separating them.

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“…to carry out diagnosis and prognosis processes [2,6,16]. Physics-based models use physical laws and mathematical formulations to obtain the response of a system under certain operating conditions, making them an appropriate choice for such endeavours [14,37]. These models can be used together with other methods, such as the data-driven approach or symbolic modelling, to create a hybrid model that aims to overcome the limitations of each method [9,25].…”

Section: Introductionmentioning

confidence: 99%

Multi-body modelling of rolling element bearings and performance evaluation with localised damage

Leturiondo¹,

Salgado²,

Galar³

2016

EiN

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Prognostic modelling options for remaining useful life estimation by industry

Cited by 779 publications

References 150 publications

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Automatic calculation of thresholds for load dependent condition indicators by modelling of probability distribution functions – maintenance of gearboxes used in mining conveying system

Multi-body modelling of rolling element bearings and performance evaluation with localised damage

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