26.1.3 Motor Faults, Engine Faults, Electrical Faults, Vibrations, Vehicles

Lu, S., He, Q., Yuan, T., Kong, F.,
Online Fault Diagnosis of Motor Bearing via Stochastic-Resonance-Based Adaptive Filter in an Embedded System,
SMCS(47), No. 7, July 2017, pp. 1111-1122.
IEEE DOI 1706
Brushless DC motors, Fault diagnosis, Induction motors, Permanent magnet motors, Signal to noise ratio, Acoustic signal processing, adaptive filters, ball bearings, brushless motors, dc motors, digital signal processing, embedded software, fault diagnosis, optimization methods, stochastic, resonance, (SR) BibRef

Xiao, B.[Bing], Yin, S.[Shen],
An Intelligent Actuator Fault Reconstruction Scheme for Robotic Manipulators,
Cyber(48), No. 2, February 2018, pp. 639-647.
IEEE DOI 1801
Actuators, Manipulator dynamics, Observers, Service robots, Torque, Actuator fault, finite-time convergence, observer, reconstruction, robotic manipulators BibRef

Agrawal, V., Panigrahi, B.K., Subbarao, P.M.V.,
Increasing Reliability of Fault Detection Systems for Industrial Applications,
IEEE_Int_Sys(33), No. 3, May 2018, pp. 28-39.
IEEE DOI 1808
Fault detection, Coal mining, Data models, Noise reduction, Mathematical model, Wavelet transforms, Adaptive learning, robust fault detection BibRef

Yu, Q., Qin, Y., Liu, P., Ren, G.,
A Panel Data Model-Based Multi-Factor Predictive Model of Highway Electromechanical Equipment Faults,
ITS(19), No. 9, September 2018, pp. 3039-3045.
IEEE DOI 1809
Road transportation, Data models, Humidity, Circuit faults, Predictive models, Wind speed, Temperature, panel data model BibRef

Kwong, R.H., Yonge-Mallo, D.L.,
Fault Diagnosis in Discrete-Event Systems with Incomplete Models: Learnability and Diagnosability,
Cyber(45), No. 7, July 2015, pp. 1236-1249.
IEEE DOI 1506
Communities BibRef

Yu, H., Wang, K., Li, Y.,
Multiscale Representations Fusion With Joint Multiple Reconstructions Autoencoder for Intelligent Fault Diagnosis,
SPLetters(25), No. 12, December 2018, pp. 1880-1884.
IEEE DOI 1812
fault diagnosis, learning (artificial intelligence), sensor fusion, signal classification, signal reconstruction, multiscale representations learning BibRef

Yu, H., Wang, K., Li, Y., Zhao, W.,
Representation Learning With Class Level Autoencoder for Intelligent Fault Diagnosis,
SPLetters(26), No. 10, October 2019, pp. 1476-1480.
IEEE DOI 1909
Vibrations, Feature extraction, Fault diagnosis, Training, Decoding, Linear programming, Degradation, Intelligent fault diagnosis, autoencoder BibRef

Hu, L.Q.[Li-Qiang], He, C.F.[Chao-Feng], Cai, Z.Q.[Zhao-Quan], Wen, L.[Long], Ren, T.[Teng],
Track circuit fault prediction method based on grey theory and expert system,
JVCIR(58), 2019, pp. 37-45.
Elsevier DOI 1901
Track circuit, Fault prediction, Grey theory, Expert system BibRef

Xie, N.[Ning], Li, H.[Hui], Zhao, W.Z.[Wen-Zhong], Ni, Y.[Ying], Liu, C.W.[Chen-Wen], Zhang, Y.[Yi], Xu, Z.G.[Zhi-Gang],
Measurement of dynamic vibration in cycling using portable terminal measurement system,
IET-ITS(13), No. 3, March 2019, pp. 469-474.
DOI Link 1903
BibRef

Su, J., Chen, W.,
Model-Based Fault Diagnosis System Verification Using Reachability Analysis,
SMCS(49), No. 4, April 2019, pp. 742-751.
IEEE DOI 1903
Observers, Uncertainty, Robustness, Algorithm design and analysis, Reachability analysis, Uncertain systems, Fault diagnosis, verification and validation BibRef

Zhang, K., Jiang, B., Yan, X., Mao, Z.,
Incipient Fault Detection for Traction Motors of High-Speed Railways Using an Interval Sliding Mode Observer,
ITS(20), No. 7, July 2019, pp. 2703-2714.
IEEE DOI 1907
Observers, Traction motors, Stators, Circuit faults, Fault detection, Uncertainty, Generators, Incipient fault detection, traction motors BibRef

Qian, W.W.[Wei-Wei], Li, S.M.[Shun-Ming], Jiang, X.X.[Xing-Xing],
Deep transfer network for rotating machine fault analysis,
PR(96), 2019, pp. 106993.
Elsevier DOI 1909
Intelligent fault diagnosis, Rotating machine, Deep transfer network, Weighted joint domain adaptation BibRef

Sultan, V.[Vivian], Hilton, B.[Brian],
A Spatial Analytics Framework to Investigate Electric Power-Failure Events and Their Causes,
IJGI(9), No. 1, 2020, pp. xx-yy.
DOI Link 2001
BibRef

Zhou, M., Cao, Z., Zhou, M., Wang, J., Wang, Z.,
Zonotoptic Fault Estimation for Discrete-Time LPV Systems With Bounded Parametric Uncertainty,
ITS(21), No. 2, February 2020, pp. 690-700.
IEEE DOI 2002
Uncertainty, Observers, Perturbation methods, Measurement uncertainty, Fault detection, discrete-time LPV systems BibRef

van Wyk, F., Wang, Y., Khojandi, A., Masoud, N.,
Real-Time Sensor Anomaly Detection and Identification in Automated Vehicles,
ITS(21), No. 3, March 2020, pp. 1264-1276.
IEEE DOI 2003
Cyber-physical systems, fault diagnosis, intelligent vehicles, intrusion detection, vehicle safety BibRef

Wang, Y., Masoud, N., Khojandi, A.,
Real-Time Sensor Anomaly Detection and Recovery in Connected Automated Vehicle Sensors,
ITS(22), No. 3, March 2021, pp. 1411-1421.
IEEE DOI 2103
Anomaly detection, Delays, Adaptation models, Acceleration, Fault detection, Safety, Intelligent transportation systems, automated vehicles BibRef

de Vita, F.[Fabrizio], Bruneo, D.[Dario], Das, S.K.[Sajal K.],
On the use of a full stack hardware/software infrastructure for sensor data fusion and fault prediction in industry 4.0,
PRL(138), 2020, pp. 30-37.
Elsevier DOI 2010
Industry4.0, Deep learning, Data fusion, IIoT industrial testbed BibRef

Canal, R.[Ramon], Hernandez, C.[Carles], Tornero, R.[Rafa], Cilardo, A.[Alessandro], Massari, G.[Giuseppe], Reghenzani, F.[Federico], Fornaciari, W.[William], Zapater, M.[Marina], Atienza, D.[David], Oleksiak, A.[Ariel], Piundefinedtek, W.[Wojciech], Abella, J.[Jaume],
Predictive Reliability and Fault Management in Exascale Systems: State of the Art and Perspectives,
Surveys(53), No. 5, September 2020, pp. xx-yy.
DOI Link 2010
survey, faults, HPC, supercomputing, failures, exascale, prediction, reliability BibRef

Zhang, J.T.[Jing-Ting], Yuan, C.Z.[Cheng-Zhi], Stegagno, P.[Paolo], He, H.B.[Hai-Bo], Wang, C.[Cong],
Small Fault Detection of Discrete-Time Nonlinear Uncertain Systems,
Cyber(51), No. 2, February 2021, pp. 750-764.
IEEE DOI 2101
Artificial neural networks, System dynamics, Uncertain systems, Adaptive systems, Nonlinear dynamical systems, Adaptation models, small fault detection (sFD) BibRef

Liu, Z.H.[Zhao-Hua], Lu, B.L.[Bi-Liang], Wei, H.L.[Hua-Liang], Chen, L.[Lei], Li, X.H.[Xiao-Hua], Rätsch, M.[Matthias],
Deep Adversarial Domain Adaptation Model for Bearing Fault Diagnosis,
SMCS(51), No. 7, July 2021, pp. 4217-4226.
IEEE DOI 2106
Fault diagnosis, Feature extraction, Rolling bearings, Deep learning, Data mining, Data models, Training, unsupervised learning BibRef

Javed, A.R.[Abdul Rehman], Usman, M.[Muhammad], Ur Rehman, S.[Saif], Khan, M.U.[Mohib Ullah], Haghighi, M.S.[Mohammad Sayad],
Anomaly Detection in Automated Vehicles Using Multistage Attention-Based Convolutional Neural Network,
ITS(22), No. 7, July 2021, pp. 4291-4300.
IEEE DOI 2107
Anomaly detection, Machine learning, Kalman filters, Convolutional neural networks, Computer crime, Accidents, multi-source anomaly detection BibRef

Chen, H.[Hao], Liu, R.N.[Ruo-Nan], Xie, Z.X.[Zong-Xia], Hu, Q.H.[Qing-Hua], Dai, J.H.[Jian-Hua], Zhai, J.H.[Jun-Hai],
Majorities help minorities: Hierarchical structure guided transfer learning for few-shot fault recognition,
PR(123), 2022, pp. 108383.
Elsevier DOI 2112
Transfer learning, Fault recognition, Few-shot problem, Hierarchical category structure, Complex systems BibRef

Jumaboev, S.[Sherozbek], Jurakuziev, D.[Dadajon], Lee, M.[Malrey],
Photovoltaics Plant Fault Detection Using Deep Learning Techniques,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Yan, S.[Shuai], Sun, W.C.[Wei-Chao], Yu, X.H.[Xing-Hu], Gao, H.J.[Hui-Jun],
Adaptive Sensor Fault Accommodation for Vehicle Active Suspensions via Partial Measurement Information,
Cyber(52), No. 11, November 2022, pp. 12290-12301.
IEEE DOI 2211
Observers, Suspensions (mechanical systems), Adaptive systems, Actuators, Measurement uncertainty, Adaptation models, sensor bias fault BibRef

Jeon, Y.[Youngbae], Han, H.[Hyekyung], Yoon, J.W.[Ji Won],
Manifold Learning-based Frequency Estimation for extracting ENF signal from digital video,
ICPR22(189-195)
IEEE DOI 2212
Electric network frequency (ENF). Manifolds, Forensics, Frequency conversion, Frequency estimation, Manifold learning BibRef

Huang, D.J.[Da-Jian], Zhang, W.A.[Wen-An], Guo, F.[Fanghong], Liu, W.J.[Wei-Jiang], Shi, X.M.[Xiao-Ming],
Wavelet Packet Decomposition-Based Multiscale CNN for Fault Diagnosis of Wind Turbine Gearbox,
Cyber(53), No. 1, January 2023, pp. 443-453.
IEEE DOI 2301
Feature extraction, Vibrations, Convolutional neural networks, Fault diagnosis, Wavelet packets, Wind turbines, Wind farms, wind turbine (WT) gearbox BibRef

Tian, S.[Sheng], Li, J.[Jia], Zhang, J.M.[Jin-Ming], Li, C.W.[Cheng-Wei],
STLRF-Stack: A fault prediction model for pure electric vehicles based on a high dimensional imbalanced dataset,
IET-ITS(17), No. 2, 2023, pp. 400-417.
DOI Link 2302
BibRef

Samal, L.[Laxmipriya], Palo, H.K.[Hemanta Kumar], Sahu, B.N.[Badri Narayan],
The recognition of 3-phase power quality events using optimal feature selection and random forest classifier,
IJCVR(13), No. 3, 2023, pp. 235-246.
DOI Link 2305
BibRef

Wang, Z.P.[Zhi-Peng], Wang, N.[Ning], Zhang, H.Y.[Hui-Yue], Jia, L.M.[Li-Min], Qin, Y.[Yong], Zuo, Y.K.[Ya-Kun], Zhang, Y.S.[Yu-Sheng], Dong, H.H.[Hong-Hui],
Segmentalized mRMR Features and Cost-Sensitive ELM With Fixed Inputs for Fault Diagnosis of High-Speed Railway Turnouts,
ITS(24), No. 5, May 2023, pp. 4975-4987.
IEEE DOI 2305
Fault diagnosis, Feature extraction, Rail transportation, Power systems, Rails, Force, Indexes, High-speed railway turnout, imbalanced data BibRef

Wei, Z.X.[Ze-Xian], He, D.Q.[De-Qiang], Jin, Z.Z.[Zhen-Zhen], Liu, B.[Bin], Shan, S.[Sheng], Chen, Y.J.[Yan-Jun], Miao, J.[Jian],
Density-Based Affinity Propagation Tensor Clustering for Intelligent Fault Diagnosis of Train Bogie Bearing,
ITS(24), No. 6, June 2023, pp. 6053-6064.
IEEE DOI 2306
Tensors, Fault diagnosis, Clustering algorithms, Signal processing algorithms, Monitoring, Rails, Time complexity, intelligent fault diagnosis BibRef

Chen, L.[Liheng], Fu, S.[Shasha], Qiu, J.B.[Jian-Bin], Feng, Z.G.[Zhi-Guang],
An Adaptive Fuzzy Approach to Fault Estimation Observer Design With Actuator Fault and Digital Communication,
Cyber(53), No. 8, August 2023, pp. 5048-5058.
IEEE DOI 2307
Observers, Iron, Quantization (signal), Actuators, Adaptive systems, Digital communication, Nonlinear systems, output quantization BibRef

Insausti, X.[Xabier], Zárraga-Rodríguez, M.[Marta], Nolasco-Ferencikova, C.[Carolina], Gutiérrez-Gutiérrez, J.[Jesús],
In-Network Algorithm for Passive Sensors in Structural Health Monitoring,
SPLetters(30), 2023, pp. 952-956.
IEEE DOI 2308
Sensors, Wireless sensor networks, Monitoring, Wireless communication, Signal processing algorithms, iterative algorithm BibRef

Su, N.[Naiquan], Zhang, Q.H.[Qing-Hua], Zhou, L.[Lingmeng], Chang, X.X.[Xiao-Xiao], Xu, T.[Ting],
A Fault Diagnosis of Rotating Machinery Based on a Mutual Dimensionless Index and a Convolution Neural Network,
IEEE_Int_Sys(38), No. 4, July 2023, pp. 33-41.
IEEE DOI 2309
BibRef

Shi, Z.[Zengshu], Du, Y.[Yiman], Yao, X.W.[Xin-Wen],
Fault diagnosis of ZDJ7 railway point machine based on improved DCNN and SVDD classification,
IET-ITS(17), No. 8, 2023, pp. 1649-1674.
DOI Link 2309
classification, fault diagnosis, improved deep convolutional neural network, unbalanced samples BibRef

Coskun, O.[Osman], Pages, G.[Gaël], Vilà-Valls, J.[Jordi], Vincent, F.[François], Chaumette, E.[Eric],
Invariance Approach to Integrity Monitoring Fault Detectors,
SPLetters(30), 2023, pp. 1062-1066.
IEEE DOI 2309
BibRef

Shan, N.L.[Nan-Liang], Xu, X.H.[Xing-Hua], Bao, X.Q.[Xian-Qiang], Xu, C.C.[Cheng-Cheng], Zhu, G.Y.[Guang-Yu], Wu, E.Q.[Edmond Q.],
Multisensor Anomaly Detection and Interpretable Analysis for Linear Induction Motors,
ITS(24), No. 9, September 2023, pp. 9861-9870.
IEEE DOI 2310
BibRef

Wang, N.[Ning], Jia, L.M.[Li-Min], Zhang, H.Y.[Hui-Yue], Qin, Y.[Yong], Zhao, X.J.[Xue-Jun], Wang, Z.P.[Zhi-Peng],
Manifold-Contrastive Broad Learning System for Wheelset Bearing Fault Diagnosis,
ITS(24), No. 9, September 2023, pp. 9886-9900.
IEEE DOI 2310
BibRef

Yan, X.Y.[Xu-Yang], Sarkar, M.[Mrinmoy], Lartey, B.[Benjamin], Gebru, B.[Biniam], Homaifar, A.[Abdollah], Karimoddini, A.[Ali], Tunstel, E.[Edward],
An Online Learning Framework for Sensor Fault Diagnosis Analysis in Autonomous Cars,
ITS(24), No. 12, December 2023, pp. 14467-14479.
IEEE DOI 2312
BibRef

Fu, R.[Rao], Bi, Y.G.[Yuan-Guo], Han, G.J.[Guang-Jie], Zhang, X.L.[Xiao-Ling], Liu, L.[Li], Zhao, L.[Liang], Hu, B.[Bing],
MAGVA: An Open-Set Fault Diagnosis Model Based on Multi-Hop Attentive Graph Variational Autoencoder for Autonomous Vehicles,
ITS(24), No. 12, December 2023, pp. 14873-14889.
IEEE DOI 2312
BibRef

Wei, Y.[Yang], Wang, K.[Kai],
Domain Invariant Feature Learning Based on Cluster Contrastive Learning for Intelligence Fault Diagnosis With Limited Labeled Data,
SPLetters(30), 2023, pp. 1787-1791.
IEEE DOI 2312
BibRef

Wan, W.Q.[Wen-Qing], Chen, J.L.[Jing-Long], Xie, J.S.[Jing-Song],
Graph-Based Model Compression for HSR Bogies Fault Diagnosis at IoT Edge via Adversarial Knowledge Distillation,
ITS(25), No. 2, February 2024, pp. 1787-1796.
IEEE DOI 2402
Fault diagnosis, Internet of Things, Generative adversarial networks, Feature extraction, Internet of Things (IoT) BibRef

Xu, Z.T.[Zong-Tang], Ma, Y.[Yumei], Pan, Z.K.[Zhen-Kuan], Zheng, X.Y.[Xiao-Yang],
Deep Spiking Residual Shrinkage Network for Bearing Fault Diagnosis,
Cyber(54), No. 3, March 2024, pp. 1608-1613.
IEEE DOI 2402
Neurons, Fault diagnosis, Noise reduction, Training, Machine learning, Hidden Markov models, spiking neural network (SNN) BibRef

Qian, S.[Shenyi], Tian, Z.Q.[Zi-Qiao], Wang, G.Z.[Guo-Zhu], Zou, Q.[Qiang],
Research on Industrial Monitoring Model Based on Neural Network Improvement,
CVIDL23(609-612)
IEEE DOI 2403
Process monitoring, Training, Fault diagnosis, Deep learning, Analytical models, Fault detection, Neural networks, fault diagnosis BibRef

Liu, Y.[Yun],
Fault Signal Perception of Nanofiber Sensor for 3D Human Motion Detection Using Multi-Task Deep Learning,
IJIG(24), No. 2, March 2024, pp. 2550060.
DOI Link 2404
BibRef

Song, W.Q.[Wan-Qing], Deng, W.[Wujin], Cattani, P.[Piercarlo], Qi, D.Y.[De-Yu], Yang, X.H.[Xian-Hua], Yao, X.[Xuyin], Chen, D.D.[Dong-Dong], Yan, W.[Wenduan], Zio, E.[Enrico],
On the prediction of power outage length based on linear multifractional Lévy stable motion,
PRL(181), 2024, pp. 120-125.
Elsevier DOI 2405
Power system reliability, Power outage length, Multifractal, Long-range dependence, Non-Gaussian, Heavy tail, Linear multifractional Lévy stable motion BibRef

Gupta, A.[Aditi], Onumanyi, A.J.[Adeiza J.], Ahlawat, S.[Satyadev], Prasad, Y.[Yamuna], Singh, V.[Virendra], Abu-Mahfouz, A.M.[Adnan M.],
DAT: A robust Discriminant Analysis-based Test of unimodality for unknown input distributions,
PRL(182), 2024, pp. 125-132.
Elsevier DOI 2405
Discriminant analysis, Fault detection, Distributions, Statistics, Unimodality BibRef

Ma, Q.H.[Qing-Hua], Dong, M.[Ming], Xia, C.J.[Chang-Jie], He, X.Y.[Xin-Yi], Chen, R.[Rongfa], Ren, M.[Ming], Song, M.Y.[Mei-Yan],
A Multivariate Normal Distribution Data Generative Model in Small-Sample-Based Fault Diagnosis: Taking Traction Circuit Breaker as an Example,
ITS(25), No. 6, June 2024, pp. 5825-5841.
IEEE DOI 2406
Training, Fault diagnosis, Data models, Circuit faults, Feature extraction, Maximum likelihood estimation, circuit breakers BibRef

Zabihi, M.[Mehdi], Mehrizi, R.V.[Reza Valiollahi], Kasaiezadeh, A.[Alireza], Pirani, M.[Mohammad], Khajepour, A.[Amir],
A Hybrid Model-Data Vehicle Sensor and Actuator Fault Detection and Diagnosis System,
ITS(25), No. 7, July 2024, pp. 8121-8133.
IEEE DOI 2407
Fault detection, Estimation, Actuators, Kernel, Data models, Kalman filters, Real-time systems, Hybrid fault detection, hybrid estimator BibRef

Zhang, S.[Shuang], Puig, V.[Vicenç], Ifqir, S.[Sara],
Robust LPV Fault Diagnosis Using the Set-Based Approach for Autonomous Ground Vehicles,
ITS(25), No. 8, August 2024, pp. 9078-9090.
IEEE DOI 2408
Linear Parameter Varying. Mathematical models, Fault diagnosis, Fault detection, Observers, Uncertainty, Computational modeling, Actuators, Fault diagnosis, autonomous ground vehicles BibRef

Manchadi, O.[Oumaima], Dehbi, Z.E.[Zineb El_Otmani], Ben-Bouazza, F.E.[Fatima-Ezzahraa], Edder, A.[Ayman], Tafala, I.[Idriss], Jioudi, B.[Bassma],
IoT-Powered Predictive Maintenance Framework for ICU Ventilators,
ISCV24(1-7)
IEEE DOI 2408
Mechanical sensors, Ventilators, Medical conditions, Hospitals, Safety, Internet of Things, Smart devices, Internet of things, Mechanical ventilator BibRef

Li, S.[Shuo], Zhang, J.F.[Jin-Feng], Tian, Y.P.[Yu-Ping],
Fault Detection for Switched Positive Systems With Application to Traffic Signal Systems,
SMCS(54), No. 10, October 2024, pp. 6162-6175.
IEEE DOI 2410
Observers, Fault detection, Switches, Time factors, Indexes, Sensitivity, Vectors, L_ fault detection functional observer, traffic signal systems BibRef

Xing, C.[Chao], Zhu, Y.[Yueying], Wang, J.Y.[Jia-Ying], Lin, Y.[Yier],
Braking Torque Distribution Reconfiguration Strategy of Vehicle With Faults of In-Wheel Motor Drive System,
ITS(25), No. 10, October 2024, pp. 14476-14485.
IEEE DOI 2410
Torque, Circuit faults, Force, Wheels, Fault diagnosis, Optimization, Motors, Electric vehicle, hierarchical control, regenerative braking BibRef

Fu, S.[Shui], Tang, W.T.[Wen-Tao], Wang, R.[Rui], Wen, S.X.[Si-Xin], Sun, X.M.[Xi-Ming],
Actuator Fault-Tolerant Control for Aero-Engine Control System: A Zonotope-Based Approach,
ITS(25), No. 11, November 2024, pp. 18861-18871.
IEEE DOI 2411
Control systems, Aircraft propulsion, Observers, Fault tolerant systems, Fault tolerance, Actuators, Uncertainty, sliding surface BibRef

Li, J.[Jing], Lu, Q.C.[Qing-Chang], Xu, P.C.[Peng-Cheng], Wang, S.X.[Shi-Xin], Xie, C.[Chi],
Cascading Failures on Multimodal Public Transportation Networks: The Role of Station Coupling Strength,
ITS(25), No. 11, November 2024, pp. 17187-17199.
IEEE DOI 2411
Power system protection, Power system faults, Couplings, Network topology, Topology, Load modeling, Public transportation, metro-bus coupled networks BibRef

Bhatia, A.[Ardaan], Vishwakarma, V.[Vinay],
Machine Learning for Fault Detection in DC Motors and the Role of Mounting Configurations,
ICCVMI23(1-5)
IEEE DOI 2403
Productivity, Profitability, Machine learning, Maintenance engineering, Predictive models, DC motors, Vibration Testing BibRef

Schmidt, J.[Jonas], Kühnberger, K.U.[Kai-Uwe], Pape, D.[Dennis], Pobandt, T.[Tobias],
Detecting Loose Wheel Bolts of a Vehicle Using Accelerometers in the Chassis,
IbPRIA23(665-679).
Springer DOI 2307
BibRef

Liu, B.[Boya], Bi, X.W.[Xiao-Wen], Gu, L.J.[Li-Juan], Liu, B.Z.[Bao-Zhong],
Application of Radar Fault Diagnosis Method Based on Bayesian Network,
ICRVC22(239-243)
IEEE DOI 2301
Fault diagnosis, Condition monitoring, Knowledge engineering, Fuses, Maintenance engineering, Fault location, Radar antennas, precision BibRef

Li, M.H.[Ming Hang], Wang, M.[Mei], Bao, Y.F.[Yu-Fei],
Review of the Intelligent Diagnosis Methods for the Power Transmission Lines,
ICIVC22(836-842)
IEEE DOI 2301
Power transmission lines, Pollution, Lightning, Fault location, Power systems, intelligent diagnoses, power transmission, traveling wave signals BibRef

Marinel, C.[Cédric], Mathon, B.[Benjamin], Losson, O.[Olivier], Macaire, L.[Ludovic],
Comparison of Phase-based Sub-Pixel Motion Estimation Methods,
ICIP22(561-565)
IEEE DOI 2211
Vibrations, Quantization (signal), Motion estimation, Modal analysis, Estimation, White noise, Vibration measurement, mechanical structure BibRef

Zhou, G.X.[Guan-Xing], Zhuang, Y.H.[Yi-Hong], Ding, X.H.[Xing-Hao], Huang, Y.[Yue], Abbas, S.[Saqlain], Tu, X.T.[Xiao-Tong],
A Simple Siamese Framework for Vibration Signal Representations,
ICIP22(2456-2460)
IEEE DOI 2211
Fault diagnosis, Vibrations, Representation learning, Learning systems, Face recognition, Data collection, SigSiam, class imbalanced fault diagnosis BibRef

Oubouaddi, H.[Hafid], Brouri, A.[Adil], Ouannou, A.[Abdelmalek],
Speed control of Switched Reluctance Machine using fuzzy controller and neural network,
ISCV22(1-6)
IEEE DOI 2208
Vibrations, Torque, Neural networks, Windings, Velocity control, Switches, Switched reluctance motors, SRM, proportional-integral-derivative controller (PID) BibRef

Maliuk, A.[Andrei], Ahmad, Z.[Zahoor], Kim, J.M.[Jong-Myon],
GMM-Aided DNN Bearing Fault Diagnosis Using Sparse Autoencoder Feature Extraction,
IbPRIA22(555-564).
Springer DOI 2205
BibRef

Baireddy, S.[Sriram], Desai, S.R.[Sundip R.], Mathieson, J.L.[James L.], Foster, R.H.[Richard H.], Chan, M.W.[Moses W.], Comer, M.L.[Mary L.], Delp, E.J.[Edward J.],
Spacecraft Time-Series Anomaly Detection Using Transfer Learning,
AI4Space21(1951-1960)
IEEE DOI 2109
Space vehicles, Training, Adaptation models, Transfer learning, Predictive models, Data models BibRef

He, J.[Jia], Cheng, M.[Maggie],
Graph Convolutional Neural Networks for Power Line Outage Identification,
ICPR21(4198-4205)
IEEE DOI 2105
Graph Convolutional Neural Network, Spatial Domain, Spectral Domain, Graph Fourier Transform BibRef

Sabir, R.[Russell], Rosato, D.[Daniele], Hartmann, S.[Sven], Gühmann, C.[Clemens],
Signal Generation using 1d Deep Convolutional Generative Adversarial Networks for Fault Diagnosis of Electrical Machines,
ICPR21(3907-3914)
IEEE DOI 2105
Training, Machine learning algorithms, Convolution, Current measurement, Probability density function, Stators, deep learning BibRef

Liu, S., Ji, Z., Wang, Y.,
Improving Anomaly Detection Fusion Method of Rotating Machinery Based on ANN and Isolation Forest,
CVIDL20(581-584)
IEEE DOI 2102
condition monitoring, data mining, fault diagnosis, feature extraction, learning (artificial intelligence), Empirical Mode Decomposition. BibRef

Sun, C., Jiang, C., Yang, J., Song, Z.,
Optimization of Structural Parameters of Spark Gap Switch Based on ANSYS,
CVIDL20(646-650)
IEEE DOI 2102
electric breakdown, electrodes, finite element analysis, spark gaps, erosion, self-breakdown field strength, cathode, anode, electric field analysis BibRef

Gao, J.J.[Jian-Jun], Yang, X.Y.[Xiao-Yan], Li, Q.P.[Qiu-Ping], Guo, Q.[Qiang], Hao, H.Y.[He-Yuan],
Research on the Problems of Equipment Dynamic Maintenance Dispatch to Make the Amount of Restoration Maximum,
CVIDL20(401-403)
IEEE DOI 2102
decision making, dispatching, maintenance engineering, optimisation, equipment dynamic maintenance dispatch, Algorithm BibRef

Zhao, H.Q.[Hong-Qiang], Guo, F.[Feng],
A combination approach to forecast the spare parts faults of shipboard aircraft,
CVIDL20(711-714)
IEEE DOI 2102
aircraft maintenance, decision making, forecasting theory, naval engineering, optimisation, regression analysis, shipboard aircraft BibRef

Wu, Y.H.[Yu-Hui], Zhang, M.J.[Man-Jiao],
Application of Nonlinear Convolutional Neural Network in Small Samples Bearing Fault Classification,
CVIDL20(715-720)
IEEE DOI 2102
convolutional neural nets, fault diagnosis, feature extraction, learning (artificial intelligence), classification BibRef

Cheng, D.L., Lai, W.H.,
Application of Self-organizing Map On Flight Data Analysis For Quadcopter Health Diagnosis System,
UAV-g19(241-246).
DOI Link 1912
BibRef

Salazar-d'Antonio, D.[Diego], Meneses-Casas, N.[Nohora], Forero, M.G.[Manuel G.], López-Santos, O.[Oswaldo],
Automatic Fault Detection in a Cascaded Transformer Multilevel Inverter Using Pattern Recognition Techniques,
IbPRIA19(I:378-385).
Springer DOI 1910
BibRef

Carbone, R.[Rosario], Montella, R.[Raffaele], Narducci, F.[Fabio], Petrosino, A.[Alfredo],
DeepNautilus: A Deep Learning Based System for Nautical Engines' Live Vibration Processing,
CAIP19(II:120-131).
Springer DOI 1909
BibRef

Balouji, E.[Ebrahim], Salor, O.[Ozgul],
Classification of power quality events using deep learning on event images,
IPRIA17(216-221)
IEEE DOI 1712
data analysis, image classification, learning (artificial intelligence), power quality (PQ) BibRef

Jarmolowicz, M., Kornatowski, E.,
Method of vibroacoustic signal spectrum optimization in diagnostics of devices,
WSSIP17(1-5)
IEEE DOI 1707
Engines, Harmonic analysis, Optimization, Power transformers, Signal processing algorithms, Signal resolution, Vibrations, signal resampling, spectral resolution, vibroacoustic, diagnostics BibRef

Islam, M.R., Tushar, A.K., Kim, J.M.,
Efficient bearing fault diagnosis by extracting intrinsic fault information using envelope power spectrum,
IVPR17(1-5)
IEEE DOI 1704
Fault diagnosis BibRef