Asymmetric Loss Functions for Deep Learning Early Predictions of Remaining Useful Life in Aerospace Gas Turbine Engines

Rengasamy, Divish; Rothwell, Benjamin; Figueredo, Grazziela P.

doi:10.1109/ijcnn48605.2020.9207051

Cited by 9 publications

(5 citation statements)

References 17 publications

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“…Full-load electrical power prediction RUL inference S. Kiakojoori and K. Khorasani [91] 2016 NARX, Elman NN Estimation of compressor fouling and turbine erosion dynamic degradation Apeksha Wankhede and Vilas Ghate [98] 2018 ANN Electrical power prediction Iman Koleini et al [102] 2018 MRP, ANN EGT prediction based on shaft velocity Divish Rengasamy et al [92] 2020 DNN, CNN, LSTM RUL prediction Zuming Liu and Iftekhar A. Karimi [94] 2020 HDMR + ANN Compressor and turbine operation characteristics prediction…”

Section: Several ML Algorithms Bayesian Hierarchical Modelmentioning

confidence: 99%

Exploring the Landscape of Gas Turbine Health Monitoring through Machine Learning: A Comprehensive Survey

Yadav

2024

IJSREM

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Within today's fiercely competitive industrial landscape, effective condition monitoring, diagnostics, and prognostics play pivotal roles. The digitization of equipment has exponentially expanded data availability across industrial processes, driving the development of advanced techniques that significantly enhance machine performance. This paper delves into a decade- spanning survey of the evolution of condition monitoring, diagnostics, and prognostics, specifically focusing on machine learning (ML)-based approaches for optimizing gas turbine operational efficiency. Through an exhaustive literature review, this publication evaluates the performance of ML models and their applications in the realm of gas turbines. It also addresses key challenges and opportunities in gas turbine research. Ultimately, the synthesis of data collected from various sources coupled with ML techniques demonstrates promising potential in enhancing the accuracy, robustness, precision, and overall performance of industrial gas turbine systems.

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Section: Several ML Algorithms Bayesian Hierarchical Modelmentioning

confidence: 99%

Exploring the Landscape of Gas Turbine Health Monitoring through Machine Learning: A Comprehensive Survey

Yadav

2024

IJSREM

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“…We used the mean squared logarithmic error (MSLE) [50] to calculate the difference between the real RUL (RUL t ) and the RUL estimated by our Robust-MBDL model ( RUL t ) during both the training and testing phases:…”

Section: Loss Functionsmentioning

confidence: 99%

Robust-MBDL: A Robust Multi-Branch Deep-Learning-Based Model for Remaining Useful Life Prediction of Rotating Machines

Tran,

Vu,

Pham

et al. 2024

Mathematics

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Predictive maintenance (PdM) is one of the most powerful maintenance techniques based on the estimation of the remaining useful life (RUL) of machines. Accurately estimating the RUL is crucial to ensure the effectiveness of PdM. However, current methods have limitations in fully exploring condition monitoring data, particularly vibration signals, for RUL estimation. To address these challenges, this research presents a novel Robust Multi-Branch Deep Learning (Robust-MBDL) model. Robust-MBDL stands out by leveraging diverse data sources, including raw vibration signals, time–frequency representations, and multiple feature domains. To achieve this, it adopts a specialized three-branch architecture inspired by efficient network designs. The model seamlessly integrates information from these branches using an advanced attention-based Bi-LSTM network. Furthermore, recognizing the importance of data quality, Robust-MBDL incorporates an unsupervised LSTM-Autoencoder for noise reduction in raw vibration data. This comprehensive approach not only overcomes the limitations of existing methods but also leads to superior performance. Experimental evaluations on benchmark datasets such as XJTU-SY and PRONOSTIA showcase Robust-MBDL’s efficacy, particularly in rotating machine health prognostics. These results underscore its potential for real-world applications, heralding a new era in predictive maintenance practices.

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“…A problem linked to these methods is that assessing the exact cost of a given error is highly domain-dependent and not straightforward [10]. Other methods include the optimization of an asymmetric loss function in standard learning algorithms [21,9]. In the context of regression, few contributions have been made regarding optimization techniques.…”

Section: Related Workmentioning

confidence: 99%

Model Optimization in Imbalanced Regression

Monteiro-Silva¹,

Ribeiro²,

Moniz³

2022

Preprint

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Imbalanced domain learning aims to produce accurate models in predicting instances that, though underrepresented, are of utmost importance for the domain. Research in this field has been mainly focused on classification tasks. Comparatively, the number of studies carried out in the context of regression tasks is negligible. One of the main reasons for this is the lack of loss functions capable of focusing on minimizing the errors of extreme (rare) values. Recently, an evaluation metric was introduced: Squared Error Relevance Area (SERA). This metric posits a bigger emphasis on the errors committed at extreme values while also accounting for the performance in the overall target variable domain, thus preventing severe bias. However, its effectiveness as an optimization metric is unknown. In this paper, our goal is to study the impacts of using SERA as an optimization criterion in imbalanced regression tasks. Using gradient boosting algorithms as proof of concept, we perform an experimental study with 36 data sets of different domains and sizes. Results show that models that used SERA as an objective function are practically better than the models produced by their respective standard boosting algorithms at the prediction of extreme values. This confirms that SERA can be embedded as a loss function into optimization-based learning algorithms for imbalanced regression scenarios.

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Asymmetric Loss Functions for Deep Learning Early Predictions of Remaining Useful Life in Aerospace Gas Turbine Engines

Cited by 9 publications

References 17 publications

Exploring the Landscape of Gas Turbine Health Monitoring through Machine Learning: A Comprehensive Survey

Exploring the Landscape of Gas Turbine Health Monitoring through Machine Learning: A Comprehensive Survey

Robust-MBDL: A Robust Multi-Branch Deep-Learning-Based Model for Remaining Useful Life Prediction of Rotating Machines

Model Optimization in Imbalanced Regression

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