Unlocking the black box: an in-depth review on interpretability, explainability, and reliability in deep learning
Deep learning models have revolutionized numerous fields, yet their decision-making
processes often remain opaque, earning them the characterization of “black-box” models …
processes often remain opaque, earning them the characterization of “black-box” models …
Explainable predictive maintenance: a survey of current methods, challenges and opportunities
Predictive maintenance is a well studied collection of techniques that aims to prolong the life
of a mechanical system by using artificial intelligence and machine learning to predict the …
of a mechanical system by using artificial intelligence and machine learning to predict the …
Restricted sparse networks for rolling bearing fault diagnosis
H Pu, K Zhang, Y An - IEEE Transactions on Industrial …, 2023 - ieeexplore.ieee.org
The application of deep learning-based rolling bearing fault diagnosis methods in high
reliability scenarios is limited due to low transparency. In addition, the scaling up of the deep …
reliability scenarios is limited due to low transparency. In addition, the scaling up of the deep …
Explainable AI algorithms for vibration data-based fault detection: use case-adadpted methods and critical evaluation
O Mey, D Neufeld - Sensors, 2022 - mdpi.com
Analyzing vibration data using deep neural networks is an effective way to detect damages
in rotating machinery at an early stage. However, the black-box approach of these methods …
in rotating machinery at an early stage. However, the black-box approach of these methods …
[HTML][HTML] An explainable predictive maintenance strategy for multi-fault diagnosis of rotating machines using multi-sensor data fusion
Abstract Industry 4.0 denotes smart manufacturing, where rotating machines predominantly
serve as the fundamental components in production sectors. The primary duty of …
serve as the fundamental components in production sectors. The primary duty of …
DCFF-MTAD: a multivariate time-series anomaly detection model based on dual-channel feature fusion
Z Xu, Y Yang, X Gao, M Hu - Sensors, 2023 - mdpi.com
The detection of anomalies in multivariate time-series data is becoming increasingly
important in the automated and continuous monitoring of complex systems and devices due …
important in the automated and continuous monitoring of complex systems and devices due …
Enhancing reliability through interpretability: A comprehensive survey of interpretable intelligent fault diagnosis in rotating machinery
This paper presents a comprehensive survey on interpretable intelligent fault diagnosis for
rotating machinery, addressing the challenge of the “black box” nature of machine learning …
rotating machinery, addressing the challenge of the “black box” nature of machine learning …
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 …
components in manufacturing industries. In the vast world of Industry 4.0, where an IoT …
Artificial intelligence enabled digital twin For predictive maintenance in industrial automation system: a novel framework and case study
Industrial automation systems are excessively used in advanced manufacturing
environments. These systems are always prone to failure which not only disturbs smooth …
environments. These systems are always prone to failure which not only disturbs smooth …
[HTML][HTML] An explainable artificial intelligence model for predictive maintenance and spare parts optimization
Maintenance strategies are vital for industrial and manufacturing systems. This study
considers a proactive maintenance strategy and emphasizes using analytics and data …
considers a proactive maintenance strategy and emphasizes using analytics and data …