Explainable Fault Diagnosis Model using Stacked Autoencoder and Kernel SHAP

Choi, Suk Hoon; Lee, Jong Min

doi:10.1109/adconip55568.2022.9894124

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…Bi et al [654] acknowledged the need for incorporating both human and machine intelligence to develop smart systems, emphasizing the role of interpretability to make humans trust in machine intelligence. The authors identified three approaches to achieve interpretability: intrinsic interpretable models, such as Bayesian neural networks [533]; enhancing black-box models, like deep neural networks, with interpretability capabilities [327,328,[666][667][668]; and employing model-agnostic methods [390,669,670] such as LIME (local interpretable model-agnostic explanations) [671] and SHAP (Shapley additive explanations) [672]. Several recent publications have surveyed interpretability techniques for machine learning models [673][674][675][676][677][678][679].…”

Section: Model Interpretabilitymentioning

confidence: 99%

Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

Melo,

Câmara,

Pinto

2024

Processes

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This paper presents a comprehensive review of the historical development, the current state of the art, and prospects of data-driven approaches for industrial process monitoring. The subject covers a vast and diverse range of works, which are compiled and critically evaluated based on the different perspectives they provide. Data-driven modeling techniques are surveyed and categorized into two main groups: multivariate statistics and machine learning. Representative models, namely principal component analysis, partial least squares and artificial neural networks, are detailed in a didactic manner. Topics not typically covered by other reviews, such as process data exploration and treatment, software and benchmarks availability, and real-world industrial implementations, are thoroughly analyzed. Finally, future research perspectives are discussed, covering aspects related to system performance, the significance and usefulness of the approaches, and the development environment. This work aims to be a reference for practitioners and researchers navigating the extensive literature on data-driven industrial process monitoring.

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Section: Model Interpretabilitymentioning

confidence: 99%

Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

Melo,

Câmara,

Pinto

2024

Processes

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show abstract

“…The SHAP (SHapley Additive exPlanations) value [47] is a novel feature importance calculation method, which is suitable for both regression and classification problems that can represent a valid alternative to the most used feature selection methods, because it can provide a local interpretation of the model on a single prediction, as well as the global average behaviour of the model in terms of the contribution of each feature to the outcome of the ML algorithm [48].According to the existing literature [9,[49][50][51], the terms "interpretability" and "explainability" are often used synonymously, referring to models where users can understand how the inputs are mapped into outputs. This is the case for the techniques aimed at quantifying the contribution given by each feature to determine the outcome of their importance and highlight those with the greatest impact on prediction.…”

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confidence: 99%

Explainable AI for Machine Fault Diagnosis: Understanding Features’ Contribution in Machine Learning Models for Industrial Condition Monitoring

et al. 2023

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Although the effectiveness of machine learning (ML) for machine diagnosis has been widely established, the interpretation of the diagnosis outcomes is still an open issue. Machine learning models behave as black boxes; therefore, the contribution given by each of the selected features to the diagnosis is not transparent to the user. This work is aimed at investigating the capabilities of the SHapley Additive exPlanation (SHAP) to identify the most important features for fault detection and classification in condition monitoring programs for rotating machinery. The authors analyse the case of medium-sized bearings of industrial interest. Namely, vibration data were collected for different health states from the test rig for industrial bearings available at the Mechanical Engineering Laboratory of Politecnico di Torino. The Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) diagnosis models are explained by means of the SHAP. Accuracies higher than 98.5% are achieved for both the models using the SHAP as a criterion for feature selection. It is found that the skewness and the shape factor of the vibration signal have the greatest impact on the models’ outcomes.

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Explaining deep learning-based anomaly detection in energy consumption data by focusing on contextually relevant data

Noorchenarboo,

Grolinger

2025

Energy and Buildings

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Explainable Fault Diagnosis Model using Stacked Autoencoder and Kernel SHAP

Cited by 5 publications

References 15 publications

Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

Explainable AI for Machine Fault Diagnosis: Understanding Features’ Contribution in Machine Learning Models for Industrial Condition Monitoring

Explaining deep learning-based anomaly detection in energy consumption data by focusing on contextually relevant data

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