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

Chapter Contents (Back)
Faults.

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Elsevier DOI 1101
Fault diagnosis; Induction motor; Stator currents; Current patterns; Features extraction BibRef

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Hajj-Ahmad, A., Garg, R., Wu, M.[Min],
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Acoustic signal processing BibRef

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acoustic signal processing BibRef

Wang, R.R.[Rong-Rong], Wang, J.M.[Jun-Min],
Actuator-Redundancy-Based Fault Diagnosis for Four-Wheel Independently Actuated Electric Vehicles,
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condition monitoring BibRef

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fault diagnosis BibRef

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Dynamic Bayesian Networks for Fault Detection, Identification, and Recovery in Autonomous Spacecraft,
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Automotive engineering BibRef

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Vibration analysis BibRef

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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

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Actuators, Manipulator dynamics, Observers, Service robots, Torque, Actuator fault, finite-time convergence, observer, reconstruction, robotic manipulators BibRef

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IEEE DOI 1808
Fault detection, Coal mining, Data models, Noise reduction, Mathematical model, Wavelet transforms, Adaptive learning, robust fault detection BibRef

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IEEE DOI 1809
Road transportation, Data models, Humidity, Circuit faults, Predictive models, Wind speed, Temperature, panel data model BibRef

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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

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Elsevier DOI 1901
Track circuit, Fault prediction, Grey theory, Expert system BibRef

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DOI Link 1903
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IEEE DOI 1903
Observers, Uncertainty, Robustness, Algorithm design and analysis, Reachability analysis, Uncertain systems, Fault diagnosis, verification and validation BibRef

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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

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Elsevier DOI 1909
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IEEE DOI 2002
Uncertainty, Observers, Perturbation methods, Measurement uncertainty, Fault detection, discrete-time LPV systems BibRef

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IEEE DOI 2003
Cyber-physical systems, fault diagnosis, intelligent vehicles, intrusion detection, vehicle safety BibRef

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IEEE DOI 2103
Anomaly detection, Delays, Adaptation models, Acceleration, Fault detection, Safety, Intelligent transportation systems, automated vehicles BibRef

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Industry4.0, Deep learning, Data fusion, IIoT industrial testbed BibRef

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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

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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],
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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],
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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],
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.[Zhenkuan], 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


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
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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, Pattern recognition 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

Bedoui, M., Mestiri, H., Bouallegue, B., Machhout, M.,
A reliable fault detection scheme for the AES hardware implementation,
ISIVC16(47-52)
IEEE DOI 1704
Algorithm design and analysis BibRef

López-Lopera, A.F.[Andrés F.], Álvarez, M.A.[Mauricio A.], Orozco, Á.Á.[Álvaro Á.],
Sparse Linear Models Applied to Power Quality Disturbance Classification,
CIARP16(521-529).
Springer DOI 1703
BibRef

Ridi, A.[Antonio], Gisler, C.[Christophe], Hennebert, J.[Jean],
A Survey on Intrusive Load Monitoring for Appliance Recognition,
ICPR14(3702-3707)
IEEE DOI 1412
Power grid monitoring. BibRef

He, W.P.[Wang-Peng], Zi, Y.Y.[Yan-Yang],
Sparsity-assisted signal representation for rotating machinery fault diagnosis using the tunable Q-factor wavelet transform with overlapping group shrinkage,
ICWAPR14(18-23)
IEEE DOI 1402
Fault diagnosis BibRef

Tacón, J.[Juan], Melgarejo, D.[Damián], Rodríguez, F.[Fernanda], Lecumberry, F.[Federico], Fernández, A.[Alicia],
Semisupervised Approach to Non Technical Losses Detection,
CIARP14(698-705).
Springer DOI 1411
electrical losses detection. BibRef

Carbajal-Hernández, J.J.[José Juan], Sánchez-Fernández, L.P.[Luis Pastor], Suárez-Guerra, S.[Sergio], Hernández-Bautista, I.[Ignacio],
Rotor Unbalance Detection in Electrical Induction Motors Using Orbital Analysis,
MCPR14(371-379).
Springer DOI 1407
BibRef
Earlier: A1, A2, Only:
Misalignment Identification in Induction Motors Using Orbital Pattern Analysis,
CIARP13(II:50-58).
Springer DOI 1311
BibRef

Rauber, T.W.[Thomas W.], Varejão, F.M.[Flávio M.],
Motor Pump Fault Diagnosis with Feature Selection and Levenberg-Marquardt Trained Feedforward Neural Network,
CAIP13(449-456).
Springer DOI 1308
BibRef

Weiss, P.[Patrick], Zenker, P.[Patrick], Maehle, E.[Erik],
Feed-forward friction and inertia compensation for improving backdrivability of motors,
ICARCV12(288-293).
IEEE DOI 1304
BibRef

Wong, P.K.[Pak Kin], Wong, H.C.[Hang Cheong], Vong, C.M.[Chi Man],
Modelling and prediction of automotive engine air-ratio using relevance vector machine,
ICARCV12(1710-1715).
IEEE DOI 1304
BibRef

Dais, J., Ying, J.[Jin],
Multivariable robust H-inf control for aeroengines using modified Particle Swarm Optimization algorithm,
ICARCV12(1605-1609).
IEEE DOI 1304
BibRef

Lin, S.Q.[Shao-Qian], Jia, Y.K.[Yu-Kun], Lei, I.P.[Iok Peng], Xu, Q.S.[Qing-Song],
Design and optimization of a long-stroke compliant micropositioning stage driven by voice coil motor,
ICARCV12(1716-1721).
IEEE DOI 1304
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Lu, Y.[Ye], Qi, R.[Ruiyun],
Adaptive observer-based output feedback control design for fault compensation and tolerance,
ICARCV12(731-736).
IEEE DOI 1304
BibRef

Chen, J.L.[Jian-Liang], Cao, Y.Y.[Yong-Yan],
Robust fault detection observer design for LPV systems,
ICARCV12(504-511).
IEEE DOI 1304
BibRef

Wang, Z.F.[Ze-Feng], Zarader, J.L.[Jean-Luc], Argentieri, S.[Sylvain],
A novel aircraft fault diagnosis and prognosis system based on Gaussian Mixture Models,
ICARCV12(1794-1799).
IEEE DOI 1304
BibRef

Miraliakbari, A., Hahn, M., Engels, J.,
Vibrations of a Gyrocopter: An Analysis Using IMUS,
ISPRS12(XXXIX-B1:497-502).
DOI Link 1209
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Robertson, P.[Paul], Coney, W.B.[William B.], Bobrow, R.[Robert],
Vehicle load estimation from observation of vibration response,
AIPR10(1-8).
IEEE DOI 1010
BibRef

Pérez, E.I.[Eduardo Islas], Rada, J.B.[Jessica Bahena], Lima, J.R.[Jesus Romero], Marín, M.M.[Mirna Molina],
Design and Costs Estimation of Electrical Substations Based on Three-Dimensional Building Blocks,
ISVC10(III: 574-583).
Springer DOI 1011
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Koprinska, I.[Irena], Sood, R.[Rohen], Agelidis, V.[Vassilios],
Variable Selection for Five-Minute Ahead Electricity Load Forecasting,
ICPR10(2901-2904).
IEEE DOI 1008
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Liu, Q.J.[Qing-Jie], Liu, X.F.[Xiao-Fang], Chen, G.M.[Gui-Ming],
Study on feature extraction of high speed precision electric machine vibration signal,
IASP10(466-469).
IEEE DOI 1004
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Mei, W.[Wang], Dan, Z.[Zhou], Li, W.[Wang],
Power Cable Faults Diagnosis Based on the Convex Hull Binary Tree SVM,
CISP09(1-5).
IEEE DOI 0910
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Nor, M.A.[Mohammed Asri], Abdullah, A.H.[Abdul Halim], Saman, A.M.[Alias Mat],
Harmonic Balance Simulation for the Nonlinear Analysis of Vibration Isolation System Using Negative Stiffness,
ICMV09(339-342).
IEEE DOI 0912
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Wang, S.C.[Sheng-Chun], Song, S.J.[Shi-Jun], Jin, T.H.[Tong-Hong], Wang, X.W.[Xiao-Wei],
Adaptive Chirplet Decomposition Method and Its Application in Machine Fault Diagnosis,
CISP09(1-5).
IEEE DOI 0910
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Passadis, K., Loizos, G.,
Core Power Losses Estimation of Wound Core Distribution Transformers with Support Vector Machines,
WSSIP09(1-4).
IEEE DOI 0906
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Xiang, L.[Ling], Chen, X.J.[Xiu-Juan], Tang, G.J.[Gui-Ji],
The Torsional Vibration of Turbo-Generator Groups in Mechanically and Electrically Coupled Influences,
CISP09(1-4).
IEEE DOI 0910
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Dong, Y.H.[Yu-Hua], Xiao, Y.[Ying], Xu, S.[Shuang],
Research on Telemetry Vibration Signal Processing by Hilbert-Huang Transform,
CISP09(1-4).
IEEE DOI 0910
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Kumar, M.[Mahendra], Kar, I.N.,
Fault Diagnosis of an Air-Conditioning System Using LS-SVM,
PReMI09(555-560).
Springer DOI 0912
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Peng, D.G.[Dao-Gang], Zhang, H.[Hao], Weng, J.N.[Jian-Nian], Xia, F.[Fei],
Research of the Embedded Data Pre-Processing and Fault Prognostics System for Turbine-Generator Units,
CISP09(1-5).
IEEE DOI 0910
BibRef

Li, H.[Hui], Fu, L.H.[Li-Hui], Zheng, H.Q.[Hai-Qi],
Bearings Fault Detection and Diagnosis Using Envelope Spectrum of Laplace Wavelet Transform,
CISP09(1-5).
IEEE DOI 0910
BibRef

Li, H.[Hui], Fu, L.H.[Li-Hui], Zheng, H.Q.[Hai-Qi],
Bearings Fault Diagnosis Based on Second Order Cyclostationary Analysis,
CISP09(1-5).
IEEE DOI 0910
BibRef

Zhang, W.B.[Wen-Bin], Cai, Q.[Qun], Shen, L.[Lu], Wang, H.J.[Hong-Jun], Li, J.S.[Jun-Sheng],
A New Method for Fault Diagnosis of Rotating Machinery Based on Harmonic Wavelet Filtering,
CISP09(1-3).
IEEE DOI 0910
BibRef

Guo, T.D.[Tian-Dong], Wang, Q.[Qi], Song, K.[Kai], Shen, Z.G.[Zheng-Guang],
Voltage Flicker Analysis Based on Second Order Blind Identification,
CISP09(1-4).
IEEE DOI 0910
BibRef

Kaur, A.[Arashdeep], Sandhu, P.S.[Parvinder S.], Bra, A.S.[Amanpreet Singh],
Early Software Fault Prediction Using Real Time Defect Data,
ICMV09(242-245).
IEEE DOI 0912
BibRef

Xing, H.J.[Hao-Jiang], Zhang, D.L.[Dong-Lai],
Phase Error Measurement Algorithm for Sampling System in Power Fault Recorder,
CISP09(1-5).
IEEE DOI 0910
BibRef

Huang, N.[Nantian], Xu, D.G.[Dian-Guo], Liu, X.S.[Xiao-Sheng], Qi, J.J.[Jia-Jin],
Power Quality Disturbance Recognition Based on S-Transform and SOM Neural Network,
CISP09(1-5).
IEEE DOI 0910
BibRef

Zhao, W.Q.[Wen-Qing], Zhang, Y.F.[Yan-Fang], Zhu, Y.L.[Yong-Li],
Diagnosis for Transformer Faults Based on Combinatorial Bayes Network,
CISP09(1-3).
IEEE DOI 0910
BibRef

Krishnanand, K.R., Nayak, S.K.[Santanu Kumar], Panigrahi, B.K., Pandi, V.R.[V. Ravikumar], Dash, P.[Priyadarshini],
Classification of Power Quality Disturbances Using GA Based Optimal Feature Selection,
PReMI09(561-566).
Springer DOI 0912
BibRef

Wang, Z.X.[Zhong-Xing], Lin, J.[Jun], Rong, L.L.[Liang-Liang], Jiang, C.D.[Chuan-Dong],
Real-Time Power Line Harmonics Suppression from MRS Based on Stacking and ANC,
CISP09(1-5).
IEEE DOI 0910
BibRef

Rognvaldsson, T.[Thorsteinn], Panholzer, G.[Georg], Byttner, S.[Stefan], Svensson, M.[Magnus],
A self-organized approach for unsupervised fault detection in multiple systems,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Hulkkonen, J.[Jenni], Heikkonen, J.[Jukka],
A minimum description length principle based method for signal change detection in machine condition monitoring,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Estupiñan, E.[Edgar], White, P.[Paul], Martin, C.S.[César San],
A Cyclostationary Analysis Applied to Detection and Diagnosis of Faults in Helicopter Gearboxes,
CIARP07(61-70).
Springer DOI 0711
BibRef

Rajshekhar, Gupta, A.[Ankur], Samanta, A.N., Kulkarni, B.D., Jayaraman, V.K.,
Fault Diagnosis Using Dynamic Time Warping,
PReMI07(57-66).
Springer DOI 0712
BibRef

Chen, H.F.[Hai-Feng], Jiang, G.F.[Guo-Fei], Yoshihira, K.[Kenji],
Fault Detection in Distributed Systems by Representative Subspace Mapping,
ICPR06(IV: 912-915).
IEEE DOI 0609
Faults in computing systems. BibRef

Ilonen, J., Paalanen, P., Kamarainen, J.K., Lindh, T., Ahola, J., Kälviäinen, H., Partanen, J.,
Toward Automatic Motor Condition Diagnosis,
SCIA05(970-977).
Springer DOI 0506
BibRef

Gerek, Ö.N.[Ömer N.], Ece, D.G.,
A 2D representation for analysis and coding of power quality events,
ICIP03(III: 561-564).
IEEE DOI 0312
BibRef

Stevens, M.R., Snorrason, M., Petrovich, D.,
Identifying vehicles using vibrometry signatures,
ICPR02(III: 253-256).
IEEE DOI 0211
BibRef

Ben Dhaou, I.[Imed], Akopian, D., Kuosmanen, P., Astola, J.T.,
Fault detection in stack filter circuits based on sample selection probabilities,
ICIP96(I: 765-768).
IEEE DOI 9610
BibRef

Chapter on New Unsorted Entries, and Other Miscellaneous Papers continues in
Financial Analysis, Business Systems .


Last update:Mar 16, 2024 at 20:36:19