Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study with Industrial Data

Ahmad Kamal Mohd Nor, Srinivasa Rao Pedapati, Masdi Muhammad and Víctor Leiva
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Ahmad Kamal Mohd Nor: Mechanical Department, Universiti Teknologi Petronas, Seri Iskandar 32610, Malaysia
Srinivasa Rao Pedapati: Mechanical Department, Universiti Teknologi Petronas, Seri Iskandar 32610, Malaysia
Masdi Muhammad: Mechanical Department, Universiti Teknologi Petronas, Seri Iskandar 32610, Malaysia
Víctor Leiva: School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile

Mathematics, 2022, vol. 10, issue 4, 1-37

Abstract: Mistrust, amplified by numerous artificial intelligence (AI) related incidents, is an issue that has caused the energy and industrial sectors to be amongst the slowest adopter of AI methods. Central to this issue is the black-box problem of AI, which impedes investments and is fast becoming a legal hazard for users. Explainable AI (XAI) is a recent paradigm to tackle such an issue. Being the backbone of the industry, the prognostic and health management (PHM) domain has recently been introduced into XAI. However, many deficiencies, particularly the lack of explanation assessment methods and uncertainty quantification, plague this young domain. In the present paper, we elaborate a framework on explainable anomaly detection and failure prognostic employing a Bayesian deep learning model and Shapley additive explanations (SHAP) to generate local and global explanations from the PHM tasks. An uncertainty measure of the Bayesian model is utilized as a marker for anomalies and expands the prognostic explanation scope to include the model’s confidence. In addition, the global explanation is used to improve prognostic performance, an aspect neglected from the handful of studies on PHM-XAI. The quality of the explanation is examined employing local accuracy and consistency properties. The elaborated framework is tested on real-world gas turbine anomalies and synthetic turbofan failure prediction data. Seven out of eight of the tested anomalies were successfully identified. Additionally, the prognostic outcome showed a 19% improvement in statistical terms and achieved the highest prognostic score amongst best published results on the topic.

Keywords: anomaly detection; bayesian methods; black-box models; CUSUM method; data analytics; explainable artificial intelligence; machine learning; prognostic and health management; singular value decomposition (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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