Unveiling the black box: A unified XAI framework for signal-based deep learning models

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dc.contributor.authorShojaeinasab, Ardeshir
dc.contributor.authorJalayer, Masoud
dc.contributor.authorBaniasadi, Amirali
dc.contributor.authorNajjaran, Homayoun
dc.date.accessioned2024-10-10T17:23:12Z
dc.date.available2024-10-10T17:23:12Z
dc.date.issued2024
dc.description.abstractCondition monitoring (CM) is essential for maintaining operational reliability and safety in complex machinery, particularly in robotic systems. Despite the potential of deep learning (DL) in CM, its ‘black box’ nature restricts its broader adoption, especially in mission-critical applications. Addressing this challenge, our research introduces a robust, four-phase framework explicitly designed for DL-based CM in robotic systems. (1) Feature extraction utilizes advanced Fourier and wavelet transformations to enhance both the model’s accuracy and explainability. (2) Fault diagnosis employs a specialized Convolutional Long Short-Term Memory (CLSTM) model, trained on the features to classify signals effectively. (3) Model refinement uses SHAP (SHapley Additive exPlanation) values for pruning nonessential features, thereby simplifying the model and reducing data dimensionality. (4) CM interpretation develops a system offering insightful explanations of the model’s decision-making process for operators. This framework is rigorously evaluated against five existing fault diagnosis architectures, utilizing two distinct datasets: one involving torque measurements from a robotic arm for safety assessment and another capturing vibration signals from an electric motor with multiple fault types. The results affirm our framework’s superior optimization, reduced training and inference times, and effectiveness in transparently visualizing fault patterns.
dc.description.reviewstatusReviewed
dc.description.scholarlevelFaculty
dc.description.sponsorshipThis research received the financial support of NTWIST Inc., Edmonton, Canada and Natural Sciences and Engineering Research Council (NSERC) Canada under the Alliance Grant ALLRP 555220-20, and collaboration of Fraunhofer IEM, Düspohl Gmbh, and Encoway Gmbh from Germany in this research.
dc.identifier.citationShojaeinasab, A., Jalayer, M., Baniasadi, A., & Najjaran, H. (2024). Unveiling the black box: A unified XAI framework for signal-based deep learning models. Machines, 12(2), Article 2. https://doi.org/10.3390/machines12020121
dc.identifier.urihttps://doi.org/10.3390/machines12020121
dc.identifier.urihttps://hdl.handle.net/1828/20562
dc.language.isoen
dc.publisherMachines
dc.rightsAttribution CC BY
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectcondition monitoring
dc.subjectexplainable artificial intelligence
dc.subjectfeature engineering
dc.subjectsignal processing
dc.subjecttrustworthy artificial intelligence
dc.titleUnveiling the black box: A unified XAI framework for signal-based deep learning models
dc.typeArticle

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