Works matching DE "FAULT diagnosis"
Results: 5000
Data-Based Fault Diagnosis Model Using a Bayesian Causal Analysis Framework.
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- International Journal of Information Technology & Decision Making, 2018, v. 17, n. 2, p. 583, doi. 10.1142/S0219622018500025
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Bayesian inference for multi-label classification for root cause analysis and probe card maintenance decision support and an empirical study.
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- Journal of Intelligent Manufacturing, 2025, v. 36, n. 3, p. 1943, doi. 10.1007/s10845-024-02336-z
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Dual prototypical contrastive network: a novel self-supervised method for cross-domain few-shot fault diagnosis.
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- Journal of Intelligent Manufacturing, 2025, v. 36, n. 1, p. 475, doi. 10.1007/s10845-023-02237-7
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DL-MSCNN: a general and lightweight framework for fault diagnosis with limited training samples.
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- Journal of Intelligent Manufacturing, 2025, v. 36, n. 1, p. 147, doi. 10.1007/s10845-023-02217-x
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Trustworthy and intelligent fault diagnosis with effective denoising and evidential stacked GRU neural network.
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- Journal of Intelligent Manufacturing, 2024, v. 35, n. 7, p. 3523, doi. 10.1007/s10845-023-02221-1
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A multisensory time-frequency features fusion method for rotating machinery fault diagnosis under nonstationary case.
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- Journal of Intelligent Manufacturing, 2024, v. 35, n. 7, p. 3197, doi. 10.1007/s10845-023-02198-x
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Image deep learning in fault diagnosis of mechanical equipment.
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- Journal of Intelligent Manufacturing, 2024, v. 35, n. 6, p. 2475, doi. 10.1007/s10845-023-02176-3
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Digital twin enhanced fault diagnosis reasoning for autoclave.
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- Journal of Intelligent Manufacturing, 2024, v. 35, n. 6, p. 2913, doi. 10.1007/s10845-023-02174-5
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Fault diagnosis and self-healing for smart manufacturing: a review.
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- Journal of Intelligent Manufacturing, 2024, v. 35, n. 6, p. 2441, doi. 10.1007/s10845-023-02165-6
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Early fault diagnosis in rolling element bearings: comparative analysis of a knowledge-based and a data-driven approach.
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- Journal of Intelligent Manufacturing, 2024, v. 35, n. 5, p. 2327, doi. 10.1007/s10845-023-02151-y
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A domain adaptation method for bearing fault diagnosis using multiple incomplete source data.
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- Journal of Intelligent Manufacturing, 2024, v. 35, n. 2, p. 777, doi. 10.1007/s10845-023-02075-7
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Integrated circuit probe card troubleshooting based on rough set theory for advanced quality control and an empirical study.
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- Journal of Intelligent Manufacturing, 2024, v. 35, n. 1, p. 275, doi. 10.1007/s10845-022-02042-8
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A spatio-temporal fault diagnosis method based on STF-DBN for reciprocating compressor.
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- Journal of Intelligent Manufacturing, 2024, v. 35, n. 1, p. 199, doi. 10.1007/s10845-022-02025-9
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A systematic review of data-driven approaches to fault diagnosis and early warning.
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- Journal of Intelligent Manufacturing, 2023, v. 34, n. 8, p. 3277, doi. 10.1007/s10845-022-02020-0
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Unified discriminant manifold learning for rotating machinery fault diagnosis.
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- Journal of Intelligent Manufacturing, 2023, v. 34, n. 8, p. 3483, doi. 10.1007/s10845-022-02011-1
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Imbalanced fault diagnosis based on semi-supervised ensemble learning.
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- Journal of Intelligent Manufacturing, 2023, v. 34, n. 7, p. 3143, doi. 10.1007/s10845-022-01985-2
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A novel data-driven method based on sample reliability assessment and improved CNN for machinery fault diagnosis with non-ideal data.
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- Journal of Intelligent Manufacturing, 2023, v. 34, n. 5, p. 2449, doi. 10.1007/s10845-022-01944-x
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Guided deep subdomain adaptation network for fault diagnosis of different types of rolling bearings.
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- Journal of Intelligent Manufacturing, 2023, v. 34, n. 5, p. 2225, doi. 10.1007/s10845-022-01910-7
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Few-shot fault diagnosis of rotating machinery with two-branch prototypical networks.
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- Journal of Intelligent Manufacturing, 2023, v. 34, n. 4, p. 1667, doi. 10.1007/s10845-021-01904-x
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Deep graph feature learning-based diagnosis approach for rotating machinery using multi-sensor data.
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- Journal of Intelligent Manufacturing, 2023, v. 34, n. 4, p. 1965, doi. 10.1007/s10845-021-01884-y
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Increasing Lifetime and Fault Tolerance Capability in Wireless Sensor Networks by Providing a Novel Management Framework.
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- Wireless Personal Communications, 2017, v. 92, n. 2, p. 603, doi. 10.1007/s11277-016-3559-3
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基于动态贝叶斯网络的液压系统可靠性研究.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 20, p. 219, doi. 10.3969/j.issn.1001-3881.2023.20.034
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基于 EKF 的 PMSM 失磁故障在线诊断方法.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 19, p. 211, doi. 10.3969/j.issn.1001-3881.2023.19.032
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基于云模型与 LSTM算法的旋转机械故障诊断研究.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 19, p. 223, doi. 10.3969/j.issn.1001-3881.2023.19.034
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基于特征融合与HPO-RVM的滚动轴承剩余寿命预测.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 17, p. 202, doi. 10.3969/j.issn.1001-3881.2023.17.033
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正流量液压挖掘机行走跑偏诊断序列决策.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 17, p. 159, doi. 10.3969/j.issn.1001-3881.2023.17.026
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基于卷积残差共享权值 LSTM 的旋转机械故障诊断.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 16, p. 215, doi. 10.3969/j.issn.1001-3881.2023.16.032
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基于多通道循环平稳特征融合的智能诊断方法.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 16, p. 222, doi. 10.3969/j.issn.1001-3881.2023.16.033
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基于 LSTM-SVM 的风电机组齿轮箱故障诊断.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 16, p. 211, doi. 10.3969/j.issn.1001-3881.2023.16.031
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基于ADASYN 和 Swin Transformer 的滚动轴承故障诊断研究.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 15, p. 209, doi. 10.3969/j.issn.1001-3881.2023.15.034
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改进的注意机制在滚动轴承故障诊断中的应用.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 12, p. 216, doi. 10.3969/j.issn.1001-3881.2023.12.034
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基于CEEMDAN 和层次波动离散熵的滚动轴承声音信号故障检测.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 12, p. 195, doi. 10.3969/j.issn.1001-3881.2023.12.031
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基于 5G 通信的数控机床智能监控与诊断系统设计.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 12, p. 142, doi. 10.3969/j.issn.1001-3881.2023.12.023
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基于 Bi-GRU 模型的航空发动机外部液压管路故障诊断研究.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 11, p. 224, doi. 10.3969/j.issn.1001-3881.2023.11.037
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基于 EMD-AR 谱频带能量特征的故障特征提取方法研究.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 11, p. 219, doi. 10.3969/j.issn.1001-3881.2023.11.036
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基于 WTD-AR 谱和 MEA-BPNN 的轴承故障诊断方法.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 11, p. 188, doi. 10.3969/j.issn.1001-3881.2023.11.031
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基于时频熵特征实现异步电机机械故障诊断.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 10, p. 215, doi. 10.3969/j.issn.1001-3881.2023.10.037
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Research on Vibration of Unit and Workshop of Yonghu Pump Station.
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- China Rural Water & Hydropower, 2022, n. 4, p. 170
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基于赛博链的云端动力电池管理.
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- Automotive Digest, 2024, n. 11, p. 42, doi. 10.19822/j.cnki.1671-6329.20220187
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Sensor Fault Diagnosis Using an Algorithm Based on Auto-Associative Neural Networks.
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- Iranian Journal of Oil & Gas Science & Technology, 2021, v. 10, n. 4, p. 14, doi. 10.22050/ijogst.2021.293463.1606
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Application of using fault detection techniques in different components in power systems.
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- Future Engineering Journal, 2021, v. 2, n. 2, p. 1
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Investigations on Using Intelligent Learning Techniques for Anomaly Detection and Diagnosis in Sensors Signals in Li-Ion Battery—Case Study.
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- Inventions (2411-5134), 2023, v. 8, n. 3, p. 74, doi. 10.3390/inventions8030074
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基于深度学习的复杂模拟电路故障识别.
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- Electronic Science & Technology, 2025, v. 38, n. 2, p. 23, doi. 10.16180/j.cnki.issn1007-7820.2025.02.004
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-种用于变压器故障诊断的贝叶斯网络优化方法.
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- Electronic Science & Technology, 2024, v. 37, n. 8, p. 34, doi. 10.16180/j.cnki.issn1007-7820.2024.08.005
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基于迁移 BN-CNN 框架的小样本工业过程故障诊断.
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- Electronic Science & Technology, 2023, v. 36, n. 7, p. 49, doi. 10.16180/j.cnki.issn1007-7820.2023.07.007
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基于改进麻雀搜索算法优化BN的变压器故障诊断研究.
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- Electronic Science & Technology, 2023, v. 36, n. 4, p. 52, doi. 10.16180/j.cnki.issn1007-7820.2023.04.007
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基于改进CNN的轴承声学故障诊断.
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- Electronic Science & Technology, 2023, v. 36, n. 1, p. 75, doi. 10.16180/j.cnki.issn1007-7820.2023.01.011
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基于集员未知输入观测器的风力机桨距执行器故障诊断.
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- Electronic Science & Technology, 2022, v. 35, n. 5, p. 38, doi. 10.16180/j.cnki.issn1007-7820.2022.05.007
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A dynamic-model-based fault diagnosis method for a wind turbine planetary gearbox using a deep learning network.
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- Protection & Control of Modern Power Systems, 2022, v. 7, n. 1, p. 1, doi. 10.1186/s41601-022-00244-z
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Reinforcement Learning for Rolling Bearing Fault Diagnosis--A Comprehensive Review.
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- Journal Européen des Systèmes Automatisés, 2024, v. 57, n. 4, p. 1185, doi. 10.18280/jesa.570425
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