Artificial intelligence in prognostics and health management of engineering systems

Ochella, Sunday; Shafiee, Mahmood; Dinmohammadi, Fateme

doi:10.1016/j.engappai.2021.104552

Cited by 47 publications

(8 citation statements)

References 178 publications

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“…As well as RNNs, LSTMs and Gated Recurrent Units (GRUs) have been used in the literature for prognostic purposes. SundayOchell [14] et al analyzed many algorithmic models including Deep Learning.…”

Section: Related Workmentioning

confidence: 99%

Camera-based PHM method in rotating machinery equipment micro-action scenarios

An¹,

liu²,

Hao³

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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The health operation of rotating machinery guarantees safety of the project. To ensure a good operating environment, current subway equipment inspections frequency is high, resulting in a waste of resources. Small abnormal changes in mechanical equipment will also contribute to the development of mechanical component defects, which will ultimately lead to the failure of the equipment. Therefore, mechanical equipment defects should be detected and diagnosed as soon as possible. Through the use of graphic processing and deep learning, this paper proposes Rmcad Framework with three aspects: condition monitoring, anomaly detection, defect early warning. Using a network algorithm, this paper proposes an improved model that has the characteristics of two-stream and multi-loss functions, which improves the accuracy of detection. Additionally, a defect warning method is constructed to improve the perception ability of equipment before failure occurs and reduce the frequency of frequent maintenance by detecting anomalies according to the degree of opening.

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Section: Related Workmentioning

confidence: 99%

Camera-based PHM method in rotating machinery equipment micro-action scenarios

An¹,

liu²,

Hao³

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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show abstract

“…For example, for a Boeing 787, approximately 1000 parameters are continuously monitored for the engine, amounting to a total of 20 terabytes of data per flight hour [1]. Such data are the basis for Remaining-Useful-Life (RUL) estimation [2] and predictive aircraft maintenance planning [3]. In this paper, we are interested in integrating RUL prognostics into predictive aircraft maintenance.…”

Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning for predictive aircraft maintenance using probabilistic Remaining-Useful-Life prognostics

Lee

Mitici

2023

Reliability Engineering & System Safety

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“…There are in total three approaches to PHM: model-based, datadriven, and fusion approaches. The details of these three approaches can be found in the work by Ochella et al (2022). Data-driven PHM, the approach used in this paper, involves using sensor data from various monitoring devices installed in an asset, along with machine learning (ML) algorithms, to determine the state of health of the asset and then predicting its RUL to make accurate maintenance decisions.…”

Section: Introductionmentioning

confidence: 99%