Remaining useful life estimation: review

Ahmadzadeh, Farzaneh; Lundberg, Jan

doi:10.1007/s13198-013-0195-0

Cited by 100 publications

(80 citation statements)

References 72 publications

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“…Si et al [199] reviewed the recent modeling developments for estimating the RUL, which is centered on statistical data driven approaches. Sikorska et al [200] and Ahmadzadeh and Lundberg [201] reviewed the advances in RUL estimation from the industry point of view. Tsui et al [202] provided a concise review of mainstream methods in major aspects of the prognostic and health management framework.…”

Section: Damage Prognosis Methodologiesmentioning

confidence: 99%

The State-of-the-Art on Framework of Vibration-Based Structural Damage Identification for Decision Making

2017

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Abstract:Research on detecting structural damage at the earliest possible stage has been an interesting topic for decades. Among them, the vibration-based damage detection method as a global technique is especially pervasive. The present study reviewed the state-of-the-art on the framework of vibration-based damage identification in different levels including the prediction of the remaining useful life of structures and the decision making for proper actions. This framework consists of several major parts including the detection of damage occurrence using response-based methods, building reasonable structural models, selecting damage parameters and constructing objective functions with sensitivity analysis, adopting optimization techniques to solve the problem, predicting the remaining useful life of structures, and making decisions for the next actions. For each part, the commonly used methods were reviewed and the merits and drawbacks were summarized to give recommendations. This framework is aimed to guide the researchers and engineers to implement step by step the structure damage identification using vibration measurements. Finally, the future research work in this field is recommended.

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Section: Damage Prognosis Methodologiesmentioning

confidence: 99%

The State-of-the-Art on Framework of Vibration-Based Structural Damage Identification for Decision Making

2017

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“…With the rapid development of computer science and artificial intelligence (AI), the data-driven prediction methodologies have covered a great number of new technologies and AI algorithms such as time series prediction model, particle filtering, regression analysis, hidden Markov model (HMM), artificial neural network (ANN), support vector machine (SVM) and extreme learning machine (ELM) etc. 11 The main idea of the date-driven prediction methodology is to employ data from past operations and current bearing conditions in order to predict the bearing fault trend or even forecast the remaining useful life (RUL). 12 It is interesting to find out that there are both connections and differences among ANN, SVM and ELM.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Trend Prediction Based on Composite Weighted KELM

Yu¹,

Li²,

Tian³

et al. 2018

IJAV

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The abilities of different degradation feature types to characterize rolling bearing fault trend are distinctive. And even the characteristic ability of the same degradation feature can change at various times. Thus these feature samples possess heteroscedasticity. However, traditional kernel extreme learning machine (KELM) model assumes that different input samples' effects on the predicted value are equal, which results in low prediction precision and low computing efficiency. To solve this problem, a novel composite weighted KELM (CWKELM) prediction model, which is fused with explicit weighting and implicit weighting, is proposed. In both feature type scale and sample time scale, the feature samples and the prediction model are weighted according to the prediction error. An adaptive mutation particle swarm optimization (AMPSO) algorithm is applied in optimizing the penalty factor and the kernel parameter in the model. Taking various entropy features as the input samples, the proposed model is adopted to conduct one-step and multi-step prediction for rolling bearing fault trend. Experimental results show that this prediction model has higher prediction accuracy and computing efficiency compared with the traditional KELM model.

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“…There are many models for RUL prediction [29], which can be divided into three categories: physics of failure, data driven and fusion [30]. The data-driven model is a common type of prediction models and it predicts the RUL by using condition monitoring data or event data.…”

Section: Introductionmentioning

confidence: 99%