This paper develops an explainable deep learning model that estimates the remaining useful lives of rotating machinery. The model extracts high-level features from Fourier transform using an autoencoder. The features are used as input to a feedforward neural network to estimate the remaining useful lives. The paper explains the model's behavior by analyzing the composition of the features and the relationships between the features and the estimation results. In order to make the model explainable, the paper introduces octave-band filtering. The filtering reduces the input size of the autoencoder and simplifies the model. A case study demonstrates the methods to explain the model. The study also shows that the octave band-filtering in the model imitates the functionality of low-level convolutional layers. This result supports the validity of using the filtering to reduce the depth of the model.
Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management
Center for Risk and Reliability, University of Maryland, College Park（马里兰大学帕克分校风险与可靠性中心）；Civil and Environmental Engineering Department & Garrick Institute for the Risk Sciences, University of California, Los Angeles（加州大学洛杉矶分校土木与环境工程系及加里克风险科学研究所）
An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery
School of Mechanical Engineering, Federal University of Uberlândia（Uberlândia联邦大学机械工程学院）；University of Padova（帕多瓦大学）；Federal University of São João del Rei（圣若昂德尔雷伊联邦大学）
Explainable histomorphology-based survival prediction of glioblastoma, IDH-wildtype
Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所）；Hannover Medical School（汉诺威医学院）；Institute of Neuropathology, RWTH Aachen University Hospital（亚琛工业大学医院神经病理学研究所）；Department of Neuropathology, Center for Pathology, Klinikum Bremen-Mitte（不来梅米特医院病理中心神经病理科）；Bremen Cancer Registry, Leibniz Institute for Prevention Research and Epidemiology - BIPS（不来梅癌症登记处，莱布尼茨预防研究与流行病学研究所 - BIPS）；Leibniz Institute for Prevention Research and Epidemiology - BIPS（莱布尼茨预防研究与流行病学研究所 - BIPS）；Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB（弗劳恩霍夫光学、系统技术与图像 exploitation 研究所 IOSB）；Robert Koch Institute（罗伯特·科赫研究所）；Department of Neurosurgery, RWTH Aachen University Hospital（亚琛工业大学医院神经外科）；Institute of Pathology, RWTH Aachen University Hospital（亚琛工业大学医院病理学研究所）；Institute of Neuropathology, Medical Center - University of Freiburg（弗莱堡大学医学中心神经病理学研究所）；Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD)（亚琛-波恩-科隆-杜塞尔多夫综合肿瘤中心 (CIO ABCD)）
RmGPT: A Foundation Model with Generative Pre-trained Transformer for Fault Diagnosis and Prognosis in Rotating Machinery
School of Aeronautics and Astronautics, Shanghai Jiao Tong University（航空宇航学院，上海交通大学）；Shanghai Innovation Institute（上海创新研究院）；MINES Paris-PSL University（巴黎-里昂商学院）；Energy Department, Politecnico di Milano（能源部门，米兰理工学院）；Beijing Aeronautical Technology Research Center（北京航空航天科技研究所）
Deep Learning in Alzheimer's disease: Diagnostic Classification and Prognostic Prediction using Neuroimaging Data
Indiana University School of Medicine（印第安纳大学医学院）；Indiana Alzheimer Disease Center（印第安纳阿尔茨海默病中心）；Indiana University Network Science Institute（印第安纳大学网络科学研究所）
Vocal Prognostic Digital Biomarkers in Monitoring Chronic Heart Failure: A Longitudinal Observational Study
Centre for Digital Health Interventions, ETH Zurich（苏黎世联邦理工学院数字健康干预中心）；Agentic System Lab, ETH Zurich（苏黎世联邦理工学院代理系统实验室）；Cardiology, University Heart Center Zurich, University Hospital Zurich（苏黎世大学医院心脏中心心脏病学）；Center for Laryngeal Surgery and Voice Rehabilitation, Massachusetts General Hospital（麻省总医院喉外科与嗓音康复中心）；Department of Surgery, Harvard Medical School（哈佛医学院外科学系）；Speech and Hearing Bioscience and Technology, Division of Medical Sciences, Harvard Medical School（哈佛医学院医学科学部言语与听觉生物科学与技术）；MGH Institute of Health Professions（麻省总医院健康职业学院）；Centre for Digital Health Interventions, University of St. Gallen（圣加仑大学数字健康干预中心）
