Handling imbalanced data for aircraft predictive maintenance using the BACHE algorithm

Dangut, Maren David; Skaf, Zakwan; Jennions, Ian K.

doi:10.1016/j.asoc.2022.108924

Cited by 13 publications

(4 citation statements)

References 34 publications

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“…Among the best known sampling techniques, SMOTE 21 has gained popularity. It generates new minority class objects as weighted average of closest neighbors (from the same minority class), and the results during the training phase support its good acceptance in the scientific community [22][23][24][25][26] . Nevertheless, the performance of the classifier-even with better quality learning due to training balance-still depends on the imbalance of the test set.…”

mentioning

confidence: 76%

Classification performance assessment for imbalanced multiclass data

Aguilar-Ruiz,

Michalak

2024

Sci Rep

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The evaluation of diagnostic systems is pivotal for ensuring the deployment of high-quality solutions, especially given the pronounced context-sensitivity of certain systems, particularly in fields such as biomedicine. Of notable importance are predictive models where the target variable can encompass multiple values (multiclass), especially when these classes exhibit substantial frequency disparities (imbalance). In this study, we introduce the Imbalanced Multiclass Classification Performance (IMCP) curve, specifically designed for multiclass datasets (unlike the ROC curve), and characterized by its resilience to class distribution variations (in contrast to accuracy or F$$_\beta$$ β -score). Moreover, the IMCP curve facilitates individual performance assessment for each class within the diagnostic system, shedding light on the confidence associated with each prediction—an aspect of particular significance in medical diagnosis. Empirical experiments conducted with real-world data in a multiclass context (involving 35 types of tumors) featuring a high level of imbalance demonstrate that both the IMCP curve and the area under the IMCP curve serve as excellent indicators of classification quality.

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confidence: 76%

Classification performance assessment for imbalanced multiclass data

Aguilar-Ruiz,

Michalak

2024

Sci Rep

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“…Promising strategies include particle filtering, Long Short-Term Memory (LSTM) [12] or even Physics Informed Neural Networks (PNN) combined with classification algorithms like Support Vector Machines (SVMs) or random forests. Moreover, the approaches should take into consideration the "few-shot" phenomenon, which lead to the substantially unbalanced healthy-unhealthy datasets typical of PHM tasks [12,13]. During the development phase there will be an extensive use of modelling techniques based on physical laws and experimental data: thanks to high and low fidelity models, the expected component behavior can be outlined and compared with the actual trends.…”

Section: Methodsmentioning

confidence: 99%

Condition-based-maintenance for fleet management

Baldo

2023

Materials Research Proceedings

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Abstract. New “enlightened” and holistic maintenance strategies are shaping the industrial world from the inside, providing intelligent and focused solutions where high availability, reliability and safety are required. Maintenance planning and scheduling is an extremely daunting and multi-faceted task which involves competences from fairly different fields: customer support, quality, engineering, production, RAMS, cost estimation etc. In the aerospace sector, a significant percentage of Life Cycle Costs (LCCs) and, in particular, operating costs, are determined by Maintenance, Repair and Overhaul (MRO) activities and the relative asset unavailability due to down-time or turn-around-time [1,2]. This is the reason why currently there is an ongoing intense effort in the research community and in the industry towards new maintenance strategies which could overcome the limitations of preventive maintenance thus streamlining operations, without jeopardizing mission safety. This research project is hence spot on and focuses on the development of optimized maintenance strategies, built around the system health status.

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“…Helmiriawan et al [ 99 ] present a deep learning method for maintenance in oil refineries where datasets consists of six months of normal conditions and five months of abnormal conditions. However, in general, failure prediction methods have to be trained on extremely unbalanced datasets [ 100 , 101 ].…”

Section: Potential Of Data Intelligencementioning

confidence: 99%

Refinery 4.0, a Review of the Main Challenges of the Industry 4.0 Paradigm in Oil & Gas Downstream

Olaizola

Quartulli

Unzueta

et al. 2022

Sensors

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Industry 4.0 concept has become a worldwide revolution that has been mainly led by the manufacturing sector. Continuous Process Industry is part of this global trend where there are aspects of the “fourth industrial revolution” that must be adapted to the particular context and needs of big continuous processes such as oil refineries that have evolved to control paradigms supported by sector-specific technologies where big volumes of operation-driven data are continuously captured from a plethora of sensors. The introduction of Artificial Intelligence techniques can overcome the current limitations of Advanced Control Systems (mainly MPCs) by providing better performance on highly non-linear and complex systems and by operating with a broader scope in terms of signals/data and sub-systems. Moreover, the state of the art of traditional PID/MPC based solutions is showing an asymptotic improvement that requires a disruptive approach in order to reach relevant improvements in terms of efficiency, optimization, maintenance, etc. This paper shows the key aspects in oil refineries to successfully adopt Big Data and Machine Learning solutions that can significantly improve the efficiency and competitiveness of continuous processes.

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Handling imbalanced data for aircraft predictive maintenance using the BACHE algorithm

Cited by 13 publications

References 34 publications

Classification performance assessment for imbalanced multiclass data

Classification performance assessment for imbalanced multiclass data

Condition-based-maintenance for fleet management

Refinery 4.0, a Review of the Main Challenges of the Industry 4.0 Paradigm in Oil & Gas Downstream

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