A Review: Prediction Method for the Remaining Useful Life of the Mechanical System

Lei, Jianyong; Zhang, Wenbo; Jiang, Zhinong; Gao, Zhilong

doi:10.1007/s11668-022-01532-4

Cited by 11 publications

(6 citation statements)

References 147 publications

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“…The RUL of a SSC is a key element in prognostics, as its estimation is a valuable input to establish effective predictive maintenance plans and, subsequently, for the optimization of the O&M of WTs thereof. There are different approaches used for RUL prediction [22,[42][43][44]. The ones presented in this paper include physics based-models, data-driven models, and hybrid models.…”

Section: Prognostics Rul Estimationmentioning

confidence: 99%

“…Accurate RUL prediction depends on effective uncertainty estimation and management. There have been several papers in this field, but still there is a need for additional investigation [42]. This uncertainty may arise from sensor noise (as mentioned in 4.2), data processing, filtering, estimation methods, model uncertainty, working condition fluctuations, prediction uncertainty, and others [42,64].…”

Section: Prognostics Models Challengesmentioning

confidence: 99%

“…There have been several papers in this field, but still there is a need for additional investigation [42]. This uncertainty may arise from sensor noise (as mentioned in 4.2), data processing, filtering, estimation methods, model uncertainty, working condition fluctuations, prediction uncertainty, and others [42,64]. Uncertainty is inherent, but techniques such as Bayesian neural networks have arisen to reduce it [64][65][66][67][68].…”

Section: Prognostics Models Challengesmentioning

confidence: 99%

See 2 more Smart Citations

Challenges on prognostics and health management for wind turbine components

Cuesta,

Leturiondo,

Vidal

et al. 2024

J. Phys.: Conf. Ser.

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This paper explores the applicability of prognostics and health management (PHM) for wind turbines (WTs), presenting the PHM approach along with challenges and opportunities in the context of WT components. First, the PHM framework is introduced, consisting of three blocks: observation, analysis, and action. Critical components and failure modes for WTs are identified, and data acquisition strategies using supervisory control and data adquisition (SCADA) and condition monitoring (CM) data are discussed. Prognostics, specifically remaining useful life (RUL) estimation, employs physics model-based, data-driven, and hybrid models. Finally, challenges and opportunities related to data, analysis and CM, and developing RUL prediction models have been found. Data challenges include data standardization, limited public datasets, and data quality issues. Analysis and CM challenges address new sensorless and non-intrusive techniques, as well as the fusion of data sources. Prognostics model challenges involve uncertainty management, interpretability issues, and the need for online updates. Addressing challenges requires incorporating physical knowledge, utilizing transfer learning, and improving online RUL prediction methods.

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Section: Prognostics Rul Estimationmentioning

confidence: 99%

Section: Prognostics Models Challengesmentioning

confidence: 99%

Section: Prognostics Models Challengesmentioning

confidence: 99%

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Challenges on prognostics and health management for wind turbine components

Cuesta,

Leturiondo,

Vidal

et al. 2024

J. Phys.: Conf. Ser.

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“…In a broader context, vibration-based RULE technology has witnessed rapid advancements over the previous two decades, yielding a wealth of literature and applications at both component and system level [ 6 , 7 , 8 , 9 , 10 ]. Vibration-based RULE is typically pursued via two main data-driven types of methods: a more commonly used one that addresses RULE as a prediction problem , and another that treats it as a multi-health state classification problem.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Estimation of Hollow Worn Railway Vehicle Wheels via On-Board Random Vibration-Based Wheel Tread Depth Estimation

Iliopoulos,

Sakellariou

2024

Sensors

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The problem of remaining useful life estimation (RULE) of hollow worn railway vehicle wheels in terms of remaining mileage via wheel tread depth estimation using on-board vibration signals from a single accelerometer on the bogie frame is presently investigated. This is achieved based on the introduction of a statistical time series method that employs: (i) advanced data-driven stochastic Functionally Pooled models for the modeling of the vehicle dynamics under different wheel tread depths in a range of interest until a critical limit, as well as tread depth estimation through a proper optimization procedure, and (ii) a wheel tread depth evolution function with respect to the vehicle running mileage that interconnects the estimated hollow wear with the remaining useful mileage. The method’s RULE performance is investigated via hundreds of Simpack-based Monte Carlo simulations with an Attiko Metro S.A. vehicle and many hollow worn wheels scenarios which are not used for the method’s training. The obtained results indicate the accurate estimation of the wheels tread depth with a mean absolute error of ∼0.07 mm that leads to a corresponding small error of ∼3% with respect to the wheels remaining useful mileage. In addition, the comparison with a recently introduced Multiple Model (MM)-based multi-health state classification method for RULE, demonstrates the better performance of the postulated method that achieves 81.17% True Positive Rate (TPR) which is significantly higher than the 45.44% of the MM method.

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“…As a result, remaining useful life (RUL) prediction is proposed to identify the state of PVS [11]. RUL prediction methods are generally divided into failure mechanism analysis, data-driven, and fusion [12][13][14]. The failure mechanism analysis method is established individually according to specific equipment.…”

Section: Introductionmentioning

confidence: 99%

RUL Prediction for Piezoelectric Vibration Sensors Based on Digital-Twin and LSTM Network

Fu,

Gao,

Zhang

2024

Mathematics

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Piezoelectric vibration sensors (PVSs) are widely used in high-temperature environments, such as vibration measurements in aero-engines, because of their high accuracy, small size, and high temperature resistance. Accurate prediction of its RUL (Remaining Useful Life) is essential for applying and maintaining PVSs. Based on PVSs’ characteristics and main failure modes, this work combines the Digital-Twin (DT) and Long Short-Term Memory (LSTM) networks to predict the RUL of PVSs. In this framework, DT can provide rich data collection, analysis, and simulation capabilities, which have advantages in RUL prediction, and LSTM network has good results in predicting time sequence data. The proposed method exploits the advantages of those techniques in feature data collection, sample optimization, and RUL multiclassification. To verify the prediction of this method, a DT platform is established to conduct PVS degradation tests, which generates sample datasets, then the LSTM network is trained and validated. It has been proved that prediction accuracy is more than 99.7%, and training time is within 94 s. Based on this network, the RUL of PVSs is predicted using different test samples. The results show that the method performed well in prediction accuracy, sample data utilization, and compatibility.

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A Review: Prediction Method for the Remaining Useful Life of the Mechanical System

Cited by 11 publications

References 147 publications

Challenges on prognostics and health management for wind turbine components

Challenges on prognostics and health management for wind turbine components

Remaining Useful Life Estimation of Hollow Worn Railway Vehicle Wheels via On-Board Random Vibration-Based Wheel Tread Depth Estimation

RUL Prediction for Piezoelectric Vibration Sensors Based on Digital-Twin and LSTM Network

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