Predicting Remaining Useful Life Based on Hilbert–Huang Entropy with Degradation Model

Zheng, Yuhuang

doi:10.1155/2019/3203959

Cited by 23 publications

(10 citation statements)

References 51 publications

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“…The case study involved a critical component of commercial aircraft. Zheng [ 40 ] presented a method to predict a bearing RUL based on a health indicator algorithm and a linear degradation model. Ordóñez et al [ 26 ] proposed an algorithm supported by ARIMA and SVM models for RUL prediction of aircraft engines.…”

Section: Data-driven Pdmmentioning

confidence: 99%

A Survey on Data-Driven Predictive Maintenance for the Railway Industry

Davari

Veloso

Costa

et al. 2021

Sensors

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In the last few years, many works have addressed Predictive Maintenance (PdM) by the use of Machine Learning (ML) and Deep Learning (DL) solutions, especially the latter. The monitoring and logging of industrial equipment events, like temporal behavior and fault events—anomaly detection in time-series—can be obtained from records generated by sensors installed in different parts of an industrial plant. However, such progress is incipient because we still have many challenges, and the performance of applications depends on the appropriate choice of the method. This article presents a survey of existing ML and DL techniques for handling PdM in the railway industry. This survey discusses the main approaches for this specific application within a taxonomy defined by the type of task, employed methods, metrics of evaluation, the specific equipment or process, and datasets. Lastly, we conclude and outline some suggestions for future research.

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Section: Data-driven Pdmmentioning

confidence: 99%

A Survey on Data-Driven Predictive Maintenance for the Railway Industry

Davari

Veloso

Costa

et al. 2021

Sensors

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“…Looking at the evolving trend of the bearing HI, the proposed approach addresses the issue of determining the time to start prediction (TSP) and the time to reach a prefixed dynamic failure. In [28], a novel prognostic approach to estimate a bearing RUL is developed. The proposed approach uses Hilbert-Huang entropy to construct a suitable HI based on vibration signals.…”

Section: Related Workmentioning

confidence: 99%

“…They use historical data (i.e., vibration and acoustic signals, temperature, pressure, oil level, torque, currents, voltage, etc.) collected from sensors to automatically learn a model of machine degradation behavior [27,28].…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction of Broken Rotor Bar Based on Data-Driven and Degradation Model

2021

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Rotating machines such as induction motors are crucial parts of most industrial systems. The prognostic health management of induction motor rotors plays an essential role in increasing electrical machine reliability and safety, especially in critical industrial sectors. This paper presents a new approach for rotating machine fault prognosis under broken rotor bar failure, which involves the modeling of the failure mechanism, the health indicator construction, and the remaining useful life prediction. This approach combines signal processing techniques, inherent metrics, and principal component analysis to monitor the induction motor. Time- and frequency-domains features allowing for tracking the degradation trend of motor critical components that are extracted from torque, stator current, and speed signals. The most meaningful features are selected using inherent metrics, while two health indicators representing the degradation process of the broken rotor bar are constructed by applying the principal component analysis. The estimation of the remaining useful life is then obtained using the degradation model. The performance of the prediction results is evaluated using several criteria of prediction accuracy. A set of synthetic data collected from a degraded Simulink model of the rotor through simulations is used to validate the proposed approach. Experimental results show that using the developed prognostic methodology is a powerful strategy to improve the prognostic of induction motor degradation.

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“…By the way, approaches considering RUL estimation constitute the majority of the literature. There are three families of RUL estimation models: similarity model, degenerate model, and survival model [40,41]. Of particular interest here, are the solutions based on AI because, due the improvement in this area in the last years, lots of new AI-based solutions for PdM have been proposed.…”

Section: Maintenance Decision-makingmentioning

confidence: 99%

A Model for Predictive Maintenance Based on Asset Administration Shell

Cavalieri

Salafia

2020

Sensors

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Maintenance is one of the most important aspects in industrial and production environments. Predictive maintenance is an approach that aims to schedule maintenance tasks based on historical data in order to avoid machine failures and reduce the costs due to unnecessary maintenance actions. Approaches for the implementation of a maintenance solution often differ depending on the kind of data to be analyzed and on the techniques and models adopted for the failure forecasts and for maintenance decision-making. Nowadays, Industry 4.0 introduces a flexible and adaptable manufacturing concept to satisfy a market requiring an increasing demand for customization. The adoption of vendor-specific solutions for predictive maintenance and the heterogeneity of technologies adopted in the brownfield for the condition monitoring of machinery reduce the flexibility and interoperability required by Industry 4.0. In this paper a novel approach for the definition of a generic and technology-independent model for predictive maintenance is presented. Such model leverages on the concept of the Reference Architecture Model for Industry (RAMI) 4.0 Asset Administration Shell, as a means to achieve interoperability between different devices and to implement generic functionalities for predictive maintenance.

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Predicting Remaining Useful Life Based on Hilbert–Huang Entropy with Degradation Model

Cited by 23 publications

References 51 publications

A Survey on Data-Driven Predictive Maintenance for the Railway Industry

A Survey on Data-Driven Predictive Maintenance for the Railway Industry

Remaining Useful Life Prediction of Broken Rotor Bar Based on Data-Driven and Degradation Model

A Model for Predictive Maintenance Based on Asset Administration Shell

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