The use of machine learning rapidly increases in high-risk scenarios where decisions are required, for example in healthcare or industrial monitoring equipment. In crucial situations, a model that can offer meaningful explanations of its decision-making is essential. In industrial facilities, the equipment's well-timed maintenance is vital to ensure continuous operation to prevent money loss. Using machine learning, predictive and prescriptive maintenance attempt to anticipate and prevent eventual system failures. This paper introduces a visualisation tool incorporating interpretations to display information derived from predictive maintenance models, trained on time-series data.
Triplot: model agnostic measures and visualisations for variable importance in predictive models that take into account the hierarchical correlation structure
Climate-resilient electric vehicle charging infrastructure for sustainable cities: An interpretable causal-ensemble framework for preventive maintenance and low-carbon mobility
Foshan University（佛山科学技术学院）；Jilin University（吉林大学）；National University of Singapore（新加坡国立大学）
GalaxAI: Machine learning toolbox for interpretable analysis of spacecraft telemetry data
Bias Variance Labs（Bias Variance实验室）；Jožef Stefan Institute（约泽夫·斯蒂芬研究所）；LSE Space GmbH（LSE航天有限公司）；ESOC, European Space Agency（欧洲空间局欧洲空间运行中心）；Solenix Engineering（Solenix工程公司）；University of Cambridge（剑桥大学）
Interpretable Rules for Online Failure Prediction: A Case Study on the Metro do Porto dataset
Lamarr Institute for Machine Learning and Artificial Intelligence, TU Dortmund University（拉马尔机器学习与人工智能研究所，多特蒙德工业大学）；Faculty of Economics - University of Porto（波尔图大学经济学院）
Short-Horizon Predictive Maintenance of Industrial Pumps Using Time-Series Features and Machine Learning
Humboldt-Universität zu Berlin, Institut für Physik, AG Moderne Optik（洪堡-柏林大学，物理研究所，现代光学小组）；University of Doha for Science and Technology, College of Engineering and Technology, Department of Mechanical Engineering Technology（多哈科学技术大学，工程与技术学院，机械工程技术系）
LiteInception: A Lightweight and Interpretable Deep Learning Framework for General Aviation Fault Diagnosis
Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）；Science and Technology on Complex Aviation System Simulation Laboratory（复杂航空系统仿真重点实验室）；The First Aircraft Institute of Aviation Industry Corporation of China（中国航空工业集团公司第一飞机设计研究院）；School of Reliability and Systems Engineering, Beihang University（北京航空航天大学可靠性与系统工程学院）
AI-Powered Machine Learning Approaches for Fault Diagnosis in Industrial Pumps
Humboldt-Universität zu Berlin, Institut für Physik, AG Moderne Optik（柏林洪堡大学物理学院现代光学小组）；University of Doha for Science and Technology, College of Engineering and Technology, Department of Mechanical Engineering Technology（多哈科学技术大学工程学院机械工程技术系）
Reliability-Calibrated Edge-IoT Early Fault Warning for Rotating Machinery with a Physics-Guided Tiny-Mamba Transformer
Great Bay University（大亚湾大学）；Huizhou University（惠州大学）；National University of Singapore（国立新加坡大学）；Shenzhen University（深圳大学）；James Cook University（詹姆斯库克大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
