Online Model-Based Remaining-Useful-Life Prognostics for Aircraft Cooling Units Using Time-Warping Degradation Clustering

Mitici, Mihaela; Pater, Ingeborg de

doi:10.3390/aerospace8060168

Cited by 10 publications

(4 citation statements)

References 43 publications

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“…For example, RUL prognostics for aircraft landing gear brakes are developed using stochastic regression models in [5]. The RUL of aircraft cooling units is estimated using particle filtering in [6]. RUL prognostics for electromechanical actuators are obtained using a Gaussian process regression in [7].…”

Section: Relevant Studies On Rul Prognosticsmentioning

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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Section: Relevant Studies On Rul Prognosticsmentioning

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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“…[75] regards the forecast and control of the health of aircraft engines; ref. [76] is about aircraft cooling devices; ref. [77] describes the degradation of rotation bearings.…”

Section: Analysis Of Degradation Modelsmentioning

confidence: 99%

Method for Forecasting the Remaining Useful Life of a Furnace Transformer Based on Online Monitoring Data

et al. 2023

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Implementing the concept of a “smart furnace transformer” should stipulate its information support throughout its life cycle. This requires improving techniques for estimating the transformer’s health and forecasting its remaining useful life (RUL). A brief review of the problem being solved has shown that the known RUL estimation techniques include processing the results of measuring the facility state parameters using various mathematical methods. Data processing techniques (deep learning, SOLA, etc.) are used, but there is no information on their application in online monitoring systems. Herewith, fast (shock) changes in the resource caused by the failures and subsequent recoveries of the facility’s health have not been considered. This reduces the RUL forecasting accuracy for the repairable equipment, including transformers. It is especially relevant to consider the impact of sudden state changes when it comes to furnace transformers due to a cumulative wear effect determined by their frequent connections to the grid (up to 100 times a day). The proposed approach is based on calculating the RUL by analytical dependencies, considering the failures and recoveries of the facility state. For the first time, an engineering RUL forecasting technique has been developed, based on the online diagnostic monitoring data results provided in the form of time series. The equipment’s relative failure tolerance index, calculated with analytical dependencies, has first been used in RUL forecasting. As a generalized indicator, a relative failure tolerance index considering the facility’s state change dynamics has been proposed. The application of the RUL forecasting technique based on the results of dissolved gas analysis of a ladle furnace unit’s transformer is demonstrated. The changes in the transformer state during the operation period from 2014 to 2022 have been studied. The RUL was calculated in the intensive aging interval; the winding dismantling results were demonstrated, which confirmed developing destructive processes in the insulation. The key practical result of the study is reducing accidents and increasing the service life of the arc and ladle furnace transformers. The techno-economic effect aims to ensure process continuity and increase the metallurgical enterprise’s output (we cannot quantify this effect since it depends on the performance of a particular enterprise). It is recommended to use the technique to forecast the RUL of repairable facilities equipped with online monitoring systems.

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“…With the advent of data-driven methodologies and sophisticated technologies, modern SPHM approaches have catalyzed a paradigm shift in aircraft maintenance procedures. These approaches broadly fall into two categories: data-driven approaches [18] and model-based/hybrid approaches [19,20]. Data-driven methods utilize machine learning techniques such as support vector machine (SVM), random forest (RF), and decision trees (DTs), as well as deep learning techniques such as convolutional neural network (CNN), and convolutional autoencoder (CAE).…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Emerging Trends in Aircraft Structural Prognostics and Health Management

Khalid,

Song,

Azad

et al. 2023

Mathematics

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This review paper addresses the critical need for structural prognostics and health management (SPHM) in aircraft maintenance, highlighting its role in identifying potential structural issues and proactively managing aircraft health. With a comprehensive assessment of various SPHM techniques, the paper contributes by comparing traditional and modern approaches, evaluating their limitations, and showcasing advancements in data-driven and model-based methodologies. It explores the implementation of machine learning and deep learning algorithms, emphasizing their effectiveness in improving prognostic capabilities. Furthermore, it explores model-based approaches, including finite element analysis and damage mechanics, illuminating their potential in the diagnosis and prediction of structural health issues. The impact of digital twin technology in SPHM is also examined, presenting real-life case studies that demonstrate its practical implications and benefits. Overall, this review paper will inform and guide researchers, engineers, and maintenance professionals in developing effective strategies to ensure aircraft safety and structural integrity.

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Online Model-Based Remaining-Useful-Life Prognostics for Aircraft Cooling Units Using Time-Warping Degradation Clustering

Cited by 10 publications

References 43 publications

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

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

Method for Forecasting the Remaining Useful Life of a Furnace Transformer Based on Online Monitoring Data

A Comprehensive Review of Emerging Trends in Aircraft Structural Prognostics and Health Management

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