Remaining useful life prediction based on noisy condition monitoring signals using constrained Kalman filter

Son, Junbo; Zhou, Shiyu; Sankavaram, Chaitanya; Du, Xinyu; Zhang, Yilu

doi:10.1016/j.ress.2016.02.006

Cited by 74 publications

(33 citation statements)

References 36 publications

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“…Furthermore, data can be noisy to the extent that it greatly influences the predicted RUL and hence reduces accuracy. Son et al [48] delves into this issue. A method is proposed to utilise condition monitored data to predict the RUL with an acceptable error.…”

Section: Stochastic Algorithmsmentioning

confidence: 99%

Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

Olesen

Shaker

2020

Sensors

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Thermal power plants are an important asset in the current energy infrastructure, delivering ancillary services, power, and heat to their respective consumers. Faults on critical components, such as large pumping systems, can lead to material damage and opportunity losses. Pumps plays an essential role in various industries and as such clever maintenance can ensure cost reductions and high availability. Prognostics and Health Management, PHM, is the study utilizing data to estimate the current and future conditions of a system. Within the field of PHM, Predictive Maintenance, PdM, has been gaining increased attention. Data-driven models can be built to estimate the remaining-useful-lifetime of complex systems that would be difficult to identify by man. With the increased attention that the Predictive Maintenance field is receiving, review papers become increasingly important to understand what research has been conducted and what challenges need to be addressed. This paper does so by initially conceptualising the PdM field. A structured overview of literature in regard to application within PdM is presented, before delving into the domain of thermal power plants and pump systems. Finally, related challenges and trends will be outlined. This paper finds that a large number of experimental data-driven models have been successfully deployed, but the PdM field would benefit from more industrial case studies. Furthermore, investigations into the scale-ability of models would benefit industries that are looking into large-scale implementations. Here, examining a method for automatic maintenance of the developed model will be of interest. This paper can be used to understand the PdM field as a broad concept but does also provide a niche understanding of the domain in focus.

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Section: Stochastic Algorithmsmentioning

confidence: 99%

Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

Olesen

Shaker

2020

Sensors

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“…The relative stress proposed in this study could be used for the estimation of condition deterioration in roller levelers. Plenty of recent studies focus on the prediction of remaining useful life and prognosis of deteriorating systems (Benkedjouh et al 2015;He et al 2012;Mosallam et al 2016;Ragab et al 2016;Shi and Zheng 2016;Son et al 2016). The proposed relative stress features could be used as the monitored indicators in these kind of approaches.…”

Section: Fig 10mentioning

confidence: 99%

Prediction of mechanical stress in roller leveler based on vibration measurements and steel strip properties

et al. 2017

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The continuous development of steel products generates new challenges for the maintenance of manufacturing machines in steel mills. Substantial mechanical stress is inflicted on the machines during the processing of modern high-strength steels. This increases the risks of damage and flaws in the processed material may appear if the capability of a machine is exceeded. Therefore, new approaches are needed to prevent the machine condition from deteriorating. This study introduces an approach to the prediction of mechanical stress inflicted on a roller leveler during the processing of cold steel strips. The relative stress level is indicated by features extracted from an acceleration signal. These features are based on the calculation of generalized norms. Steel strip properties are used as explanatory variables in regression models to predict values for the extracted vibration features. The models tested in this study include multiple linear regression, partial least squares regression and generalized regression neural network. The models were tested using an extensive data set from a roller leveler that is in continuous operation in a steel mill. The prediction accuracy of the best models identified indicates that the relative stress level inflicted by each steel strip could be predicted based on its properties.

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“…Generally, reliability assessment focuses on predicting the future system reliability or State of Health (SOH) based on Condition Monitoring (CM) signals (observable indicators used to infer the unobservable underlying SOH, e.g., the capacity of a battery or the bearing vibration of a gear-box) [1]. It provides fundamental analysis for failure prognostics methods such as Remaining Useful Life (RUL) estimation or other methodologies aiming at avoiding system sudden shutdowns, increasing system availability and safety, and reducing the cost of accident and maintenance [2].…”

Section: Introductionmentioning

confidence: 99%

A novel support vector regression method for online reliability prediction under multi-state varying operating conditions

Tao

Zio

Zhao

2018

Reliability Engineering & System Safety

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Modeling the evolution of system reliability in the presence of Condition Monitoring (CM) signals is an important issue for improved reliability assessment and system lifetime prediction. In practice, during its lifetime, a system usually works under varying operating conditions due to internal or external factors such as the ambient environments, operational profiles or workloads. In this context, the system reliability can show varying evolution behaviors (follow changing underlying trajectories), which presents new challenges to describe precisely the dynamics of system reliability. Thus, this paper proposes a novel data-driven approach to address the problems including the identification of varying operating conditions, the construction and dynamical updating of evolution model, and finally the online prediction of system reliability, focusing on systems under one common and typical case of varying operating conditions, the multi-state operating condition. Experiments based on artificial data and some widely studied real reliability cases reveal that the proposed method has superior performance compared with some existing benchmark approaches, in the case under consideration. This improved reliability prediction provides fundamental basis for advanced prognostics such as the Remaining Useful Life (RUL) estimation.

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Remaining useful life prediction based on noisy condition monitoring signals using constrained Kalman filter

Cited by 74 publications

References 36 publications

Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

Prediction of mechanical stress in roller leveler based on vibration measurements and steel strip properties

A novel support vector regression method for online reliability prediction under multi-state varying operating conditions

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