Explainable Predictive Maintenance of Rotating Machines Using LIME, SHAP, PDP, ICE

Artificial Intelligence (AI) is a key component in Industry 4.0. Rotating machines are critical components in manufacturing industries. In the vast world of Industry 4.0, where an IoT network acts as a monitoring and decision-making system, predictive maintenance is quickly gaining importance. Predi...

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Published inIEEE access Vol. 12; pp. 29345 - 29361
Main Authors Gawde, Shreyas, Patil, Shruti, Kumar, Satish, Kamat, Pooja, Kotecha, Ketan, Alfarhood, Sultan
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Artificial intelligence
Classification algorithms
Critical components
Data acquisition
Decisions
Explainable AI
Explainable artificial intelligence
Feature extraction
Fourth Industrial Revolution
ICE
Industrial applications
industrial rotating machines
Industry 4.0
LIME
PDP
Prediction algorithms
Predictive maintenance
Predictive models
Rotating machinery
Rotating machines
SHAP
Online AccessGet full text
ISSN2169-3536
2169-3536
DOI10.1109/ACCESS.2024.3367110

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Abstract Artificial Intelligence (AI) is a key component in Industry 4.0. Rotating machines are critical components in manufacturing industries. In the vast world of Industry 4.0, where an IoT network acts as a monitoring and decision-making system, predictive maintenance is quickly gaining importance. Predictive maintenance is a method that uses AI to handle potential problems before they cause breakdowns in operations, processes or systems. However, there is a significant issue with the AI models' (also known as "black boxes") inability to explain their decisions. This interpretability is vital for making maintenance decisions and validating the model's reliability, leading to improved trust and acceptance of AI-driven predictive maintenance strategies. Explainable AI is the solution because it provides human-understandable insights into how the AI model arrives at its predictions. In this regard, the paper presents Explainable AI-based predictive maintenance of Industrial rotating machines. The proposed approach unfolds in four comprehensive stages: 1) Multi-sensor based multi-fault (5 different fault classes) data acquisition; 2) frequency-domain statistical feature extraction; and c) comparison of results for multiple AI algorithms, and d) XAI integration using "Local Interpretable Model Agnostic Explanation (LIME)", "SHapley Additive exPlanation (SHAP)", "Partial Dependence Plot (PDP)" and "Individual Conditional Expectation (ICE)" to interpret the results.
AbstractList Artificial Intelligence (AI) is a key component in Industry 4.0. Rotating machines are critical components in manufacturing industries. In the vast world of Industry 4.0, where an IoT network acts as a monitoring and decision-making system, predictive maintenance is quickly gaining importance. Predictive maintenance is a method that uses AI to handle potential problems before they cause breakdowns in operations, processes or systems. However, there is a significant issue with the AI models' (also known as "black boxes") inability to explain their decisions. This interpretability is vital for making maintenance decisions and validating the model's reliability, leading to improved trust and acceptance of AI-driven predictive maintenance strategies. Explainable AI is the solution because it provides human-understandable insights into how the AI model arrives at its predictions. In this regard, the paper presents Explainable AI-based predictive maintenance of Industrial rotating machines. The proposed approach unfolds in four comprehensive stages: 1) Multi-sensor based multi-fault (5 different fault classes) data acquisition; 2) frequency-domain statistical feature extraction; and c) comparison of results for multiple AI algorithms, and d) XAI integration using "Local Interpretable Model Agnostic Explanation (LIME)", "SHapley Additive exPlanation (SHAP)", "Partial Dependence Plot (PDP)" and "Individual Conditional Expectation (ICE)" to interpret the results.
Author Alfarhood, Sultan
Patil, Shruti
Kotecha, Ketan
Kumar, Satish
Gawde, Shreyas
Kamat, Pooja
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Snippet Artificial Intelligence (AI) is a key component in Industry 4.0. Rotating machines are critical components in manufacturing industries. In the vast world of...
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SubjectTerms Algorithms
Artificial intelligence
Classification algorithms
Critical components
Data acquisition
Decisions
Explainable AI
Explainable artificial intelligence
Feature extraction
Fourth Industrial Revolution
ICE
Industrial applications
industrial rotating machines
Industry 4.0
LIME
PDP
Prediction algorithms
Predictive maintenance
Predictive models
Rotating machinery
Rotating machines
SHAP
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Title Explainable Predictive Maintenance of Rotating Machines Using LIME, SHAP, PDP, ICE
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