Evaluating covariance in prognostic and system health management applications

Menon, Sandeep; Jin, Xiaohang; Chow, Tommy W. S.; Pecht, Michael

doi:10.1016/j.ymssp.2014.10.012

Cited by 8 publications

(2 citation statements)

References 46 publications

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“…For instance [129], used correlation to select the variables to describe the degradation on jet engines data. Covariance evaluations are frequently performed when the degradation is described by several variables [30]. Statistical models are also used for prognostics.…”

Section: Statistical Modelsmentioning

confidence: 99%

Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics

Jimenez

Schwartz

Vingerhoeds

et al. 2020

Journal of Manufacturing Systems

156

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The use of a modern technological system requires a good engineering approach, optimized operations, and proper maintenance in order to keep the system in an optimal state. Predictive maintenance focuses on the organization of maintenance actions according to the actual health state of the system, aiming at giving a precise indication of when a maintenance intervention will be necessary. Predictive maintenance is normally implemented by means of specialized computational systems that incorporate one of several models to fulfil diagnostics and prognostics tasks. As complexity of technological systems increases over time, single-model approaches hardly fulfil all functions and objectives for predictive maintenance systems. It is increasingly common to find research studies that combine different models in multi-model approaches to overcome complexity of predictive maintenance tasks, considering the advantages and disadvantages of each single model and trying to combine the best of them. These multi-model approaches have not been extensively addressed by previous review studies on predictive maintenance. Besides, many of the possible combinations for multi-model approaches remain unexplored in predictive maintenance applications; this offers a vast field of opportunities when architecting new predictive maintenance systems. This systematic survey aims at presenting the current trends in diagnostics and prognostics giving special attention to multi-model approaches and summarizing the current challenges and research opportunities.

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Section: Statistical Modelsmentioning

confidence: 99%

Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics

Jimenez

Schwartz

Vingerhoeds

et al. 2020

Journal of Manufacturing Systems

156

View full text Add to dashboard Cite

show abstract

“…Information, such as precision recession trajectory, probability distribution, and accuracy reliability function, varies with the movement process. e prediction problem for accuracy reliability involves the interaction between internal factors and the external environment [1][2][3][4][5]. According to the existing research, the reliability theory of rolling bearings is primarily concerned with fatigue failure and static reliability problems and assumes that the lifetime data obey the Weibull distribution or lognormal distribution [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Prediction for Accuracy Maintaining Reliability of Superprecision Rolling Bearing in Service

Liang

Xia

Chang³

2018

Shock and Vibration

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A dynamic prediction method for accuracy maintaining reliability (AMR) of superprecision rolling bearings (SPRBs) in service is proposed by effectively fusing chaos theory and grey system theory and applying stochastic processes. In this paper, the time series of a vibration signal is used to characterize the state information for SPRB, and four runtime data points can be predicted in the future, which depends on four chaotic forecasting models to preprocess the time series. Using the grey bootstrap method and sampling from the four runtime data, a large amount of generated data (GD) are gained to analyze the changes in information on bearing service accuracy. Then, using a predefined accuracy threshold to match the Poisson count for the GD, the estimated value of variation intensity is obtained. Subsequently, with the help of the Poisson process, the dynamic evolution process is forecast in real time for AMR of the SPRB for each step in the future. Finally, according to a novel concept for maintaining relative reliability in an SPRB, the failure degree of a bearing maintaining an optimum accuracy status (BMOAS) is effectively described. Experimental investigation shows that multiple chaotic forecasting methods are accurate and feasible with all relative errors below 15%; the reliability of each step in the future can truly be described, and the prediction results for AMR over the same subseries show good consistency; dynamic monitoring of the health status of SPRB can be realized by the degree to which a BMOAS fails.

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Health and Remaining Useful Life Estimation of Electronic Circuits

Vasan¹,

Pecht

2018

Prognostics and Health Management of Electronics

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Evaluating covariance in prognostic and system health management applications

Cited by 8 publications

References 46 publications

Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics

Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics

Dynamic Prediction for Accuracy Maintaining Reliability of Superprecision Rolling Bearing in Service

Health and Remaining Useful Life Estimation of Electronic Circuits

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