Review on Health Indices Extraction and Trend Modeling for Remaining Useful Life Estimation

Djeziri, Mohand; Benmoussa, Samir; Zio, Enrico

doi:10.1007/978-3-030-42726-9_8

Cited by 25 publications

(18 citation statements)

References 91 publications

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“…Djeziri et al [ 35 ] categorized the RUL estimation approaches into the following categories: Expert model-based: Expert models, fuzzy logic Data-driven approaches Trend modeling methods: Machine learning, statistical models, stochastic models, deterministic models, probabilistic models Machine Learning Model-based approaches: Specific degradation models …”

Section: Background and Related Workmentioning

confidence: 99%

Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks

Kang

Çatal

Tekinerdoğan

2021

Sensors

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Predictive maintenance of production lines is important to early detect possible defects and thus identify and apply the required maintenance activities to avoid possible breakdowns. An important concern in predictive maintenance is the prediction of remaining useful life (RUL), which is an estimate of the number of remaining years that a component in a production line is estimated to be able to function in accordance with its intended purpose before warranting replacement. In this study, we propose a novel machine learning-based approach for automating the prediction of the failure of equipment in continuous production lines. The proposed model applies normalization and principle component analysis during the pre-processing stage, utilizes interpolation, uses grid search for parameter optimization, and is built with multilayer perceptron neural network (MLP) machine learning algorithm. We have evaluated the approach using a case study research to predict the RUL of engines on NASA turbo engine datasets. Experimental results demonstrate that the performance of our proposed model is effective in predicting the RUL of turbo engines and likewise substantially enhances predictive maintenance results.

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Section: Background and Related Workmentioning

confidence: 99%

Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks

Kang

Çatal

Tekinerdoğan

2021

Sensors

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“…A large number of research works related to gearbox fault diagnosis via detection of fault related impacts use the data-driven approach [ 63 ]. In these works, selection of the diagnostically relevant frequency bands is normally performed via the spectral kurtosis, applied to vibration data, without model-based knowledge of dependencies between these frequency bands and mechanical properties of the gearboxes [ 49 ].…”

Section: Application Of the Higher-order Wavelet Spectral Cross-comentioning

confidence: 99%

Novel Higher-Order Spectral Cross-Correlation Technologies for Vibration Sensor-Based Diagnosis of Gearboxes

Gelman

Solinski

Ball

2020

Sensors

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Novel vibration sensor-based diagnostic technologies, built on the higher order wavelet spectral cross-correlation (WSC), are proposed, investigated and applied to gearbox vibration diagnosis for the first time in worldwide terms. The proposed WSC-based technologies do not feature any constrains in selection of signal spectral components, relations between which are analysed. That is a radical improvement in comparison with the higher-order spectra (HOS). The WSC technologies are applied for an experimental diagnosis of a local gear tooth fault of a helical gearbox that is developed during a long duration gearbox endurance test. Differences between the applied technologies and advantages of the novel WSC approach over the classical HOS are explained in detail. Superiority of the WSC technologies is justified by high validity comprehensive experimental comparison with the HOS technologies: i.e., the wavelet bicoherence and the wavelet tricoherence.

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“…In the specialized literature, we can distinguish two main families of approaches for fault diagnosis and failure prognosis: approaches using physical models and data-driven ones. These large families can then be decomposed according to the tools used in each work, as proposed by Lin et al [1] and Djeziri et al [2]. The use of physical or data-driven models is a function of the compromise that has to be found between the advantages and the drawbacks of each of them.…”

Section: Introductionmentioning

confidence: 99%

Failure Prognosis Based on Relevant Measurements Identification and Data-Driven Trend-Modeling: Application to a Fuel Cell System

et al. 2021

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Fuel cells are key elements in the transition to clean energy thanks to their neutral carbon footprint, as well as their great capacity for the generation of electrical energy by oxidizing hydrogen. However, these cells operate under straining conditions of temperature and humidity that favor degradation processes. Furthermore, the presence of hydrogen—a highly flammable gas—renders the assessment of their degradations and failures crucial to the safety of their use. This paper deals with the combination of physical knowledge and data analysis for the identification of health indices (HIs) that carry information on the degradation process of fuel cells. Then, a failure prognosis method is achieved through the trend modeling of the identified HI using a data-driven and updatable state model. Finally, the remaining useful life is predicted through the calculation of the times of crossing of the predicted HI and the failure threshold. The trend model is updated when the estimation error between the predicted and measured values of the HI surpasses a predefined threshold to guarantee the adaptation of the prediction to changes in the operating conditions of the system. The effectiveness of the proposed approach is demonstrated by evaluating the obtained experimental results with prognosis performance analysis techniques.

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Review on Health Indices Extraction and Trend Modeling for Remaining Useful Life Estimation

Cited by 25 publications

References 91 publications

Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks

Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks

Novel Higher-Order Spectral Cross-Correlation Technologies for Vibration Sensor-Based Diagnosis of Gearboxes

Failure Prognosis Based on Relevant Measurements Identification and Data-Driven Trend-Modeling: Application to a Fuel Cell System

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