Application of Explainable AI (Xai) For Anomaly Detection and Prognostic of Gas Turbines with Uncertainty Quantification.

Nor, Ahmad Kamal Mohd; Pedapati, Srinivasa Rao; Muhammad, Masdi

doi:10.20944/preprints202109.0034.v1

Cited by 5 publications

(2 citation statements)

References 34 publications

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“…Similar approaches are presented in [14][15][16]. For anomaly detection and prognosis of gas turbines, Bayesian long short-term memory (LSTM) is employed and the outputs are explained using SHAP [17]. Alongside the prediction, two output layers, the AU layer and the EU layer, are added to generate data and parameter uncertainty.…”

Section: Related Workmentioning

confidence: 99%

Explainable Artificial Intelligence Approach for Diagnosing Faults in an Induction Furnace

Moosavi,

Razavi-Far,

Palade

et al. 2024

Electronics

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For over a century, induction furnaces have been used in the core of foundries for metal melting and heating. They provide high melting/heating rates with optimal efficiency. The occurrence of faults not only imposes safety risks but also reduces productivity due to unscheduled shutdowns. The problem of diagnosing faults in induction furnaces has not yet been studied, and this work is the first to propose a data-driven framework for diagnosing faults in this application. This paper presents a deep neural network framework for diagnosing electrical faults by measuring real-time electrical parameters at the supply side. Experimental and sensory measurements are collected from multiple energy analyzer devices installed in the foundry. Next, a semi-supervised learning approach, known as the local outlier factor, has been used to discriminate normal and faulty samples from each other and label the data samples. Then, a deep neural network is trained with the collected labeled samples. The performance of the developed model is compared with several state-of-the-art techniques in terms of various performance metrics. The results demonstrate the superior performance of the selected deep neural network model over other classifiers, with an average F-measure of 0.9187. Due to the black box nature of the constructed neural network, the model predictions are interpreted by Shapley additive explanations and local interpretable model-agnostic explanations. The interpretability analysis reveals that classified faults are closely linked to variations in odd voltage/current harmonics of order 3, 11, 13, and 17, highlighting the critical impact of these parameters on the model’s prediction.

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Section: Related Workmentioning

confidence: 99%

Explainable Artificial Intelligence Approach for Diagnosing Faults in an Induction Furnace

Moosavi,

Razavi-Far,

Palade

et al. 2024

Electronics

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“…Several publications support the idea that XAIs are essential for providing an accurate and comprehensible model for estimating RUL (Nor, Pedapait, and Muhammad 2021). However, one might question the reliability of these XAI methods.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Explainability in Predictive Maintenance : Investigating the Impact of Data Preprocessing Techniques on XAI Effectiveness

NDAO,

YOUNESS,

NIANG

et al. 2024

FLAIRS

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In predictive maintenance, the complexity of the data often requires the use of Deep Learning models. These models, called “black boxes”, have proved their worth in predicting the Remaining Useful Life (RUL) of industrial machines. However, the inherent opacity of these models requires the incorporation of post-hoc explanation methods to enhance transparency. The quality of the explanations provided is then assessed using so-called evaluation metrics. Modeling is a whole process that includes an important data preprocessing phase, with parameter selection such as time window, smoothing parameter, or rectified RUL when dealing with multivariate time series dataset. We propose to analyze the impact of these preprocessing methods on the quality of explanations provided by the local post-hoc models LIME, KernelSHAP, and L2X, utilizing six evaluation metrics: stability, consistency, congruence, selectivity, completeness, and acumen. This analysis will be based on NASA's Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset with the LSTM model. Our findings reveal that the choice of specific pre-processing parameters can significantly improve predictive performance. Furthermore, the quality of explanations depends on the selection of explicability methods. In addition, a factorial analysis of the evaluation metrics reveals that they do not all point in the same direction.Indeed, understanding the nuanced relationships between evaluation metrics is essential for a comprehensive and accurate assessment of explainability methods.

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How to Explain It to Energy Engineers?

Degen

Budnik

Conte

et al. 2022

Lecture Notes in Computer Science

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Application of Explainable AI (Xai) For Anomaly Detection and Prognostic of Gas Turbines with Uncertainty Quantification.

Cited by 5 publications

References 34 publications

Explainable Artificial Intelligence Approach for Diagnosing Faults in an Induction Furnace

Explainable Artificial Intelligence Approach for Diagnosing Faults in an Induction Furnace

Enhancing Explainability in Predictive Maintenance : Investigating the Impact of Data Preprocessing Techniques on XAI Effectiveness

How to Explain It to Energy Engineers?

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