Title not available by Bouali, Fakhreddine, Fedala, Semchedine, André, Hugo, Felkaoui, Ahmed

“…Finally, the classification is carried out by a cubic support vector machine (SVM) for the detection and identification stages of various bearings fault conditions. …”
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Application of Machine Learning Techniques for Bearing Fault Diagnosis by Sarra Eddai, Nabih Feki, Ahmed Ghorbel, Abdelkhalak El Hami, Mohamed Haddar

“…Notably, the SVM algorithm demonstrates exceptional performance, attaining a 99.2% accuracy rate in inner-race fault identification. This investigation provides a comprehensive analysis of the Case Western Reserve University (CWRU) dataset, data preprocessing procedures, feature extraction techniques, and machine learning algorithms utilized for fault detection. …”
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A study on rolling bearing fault diagnosis using RIME-VMD by Zhenrong Ma, Ying Zhang

“…It facilitates faster identification of decomposition parameters under various fault conditions, enhancing the robustness of fault signal detection and enabling rapid, efficient identification of rolling bearing faults. …”
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Study on Fault Arc Recognition Based on Back-Propagation Neural Network by QIAO Weide

“…In view of the problems of low loop current and ineffective detection of traditional line protection equipment when series fault arc occurs, a fault arc recognition neural network model based on wavelet analysis and backpropagation(BP) neural network is established to identify the fault arc quickly and accurately. …”
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Gearbox Fault Diagnosis based on LMD Approximate Entropy and PSO-ELM by Zhang Yuxue, Pan Hongxia

“…For the detection and identification problems of common faults in the use of gearbox,considering the nonlinear,non-stationary properties of the gearbox vibration response signal,a method for the gearbox fault diagnosis based on local mean decomposition（ LMD） approximate entropy and PSO-ELM is proposed.Firstly,the LMD decomposition method is used for gearbox vibration signal,with correlation coefficient method extracted the first four PF components which contain the main fault information. …”
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Bearing Early Fault Diagnosis Based on an Improved Multiscale Permutation Entropy and SVM by Qunyan Jiang, Juying Dai, Faming Shao, Shengli Song, Fanjie Meng

“…Experimental results show that the new method of early weak fault identification based on IMPE-SVM was effective in detecting rolling bearing faults with different severity.…”
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DEFORMATION WAVES AS A TRIGGER MECHANISM OF SEISMIC ACTIVITY IN SEISMIC ZONES OF THE CONTINENTAL LITHOSPHERE by S. I. Sherman

“…By integration of vectors of migration of epicentres at active faults, it is possible to demonstrate a pattern of vectors of movements of the deformation waves in the seismic zones of the continental lithosphere (Fig. 18).Regional and trans-regional deformation waves are analyzed. …”
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Intelligent Diagnosis Method for Centrifugal Pump System Using Vibration Signal and Support Vector Machine by Hongtao Xue, Zhongxing Li, Huaqing Wang, Peng Chen

“…Finally, the possibility functions of the SSP are used to construct a sequential fuzzy diagnosis for fault detection and fault-type identification by possibility theory and DST. …”
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A Data-Driven-Based Grounding Fault Location Method for the Auxiliary Power Supply System in an Electric Locomotive by Xinyao Hou, Yang Meng, Qiang Ni

“…Timely identification and localization of these faults are crucial for ensuring the stable operation of electric locomotives and the safety of passengers. …”
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An Integrated Model of Atom Search Optimization-Based Resonance Sparse Signal Decomposition and Cross-Validation SVM for Gearbox Fault Diagnosis by Fengfeng Bie, Yifan Wu, Ying Zhang, Jian Peng, Hongfei Zhu

“…In the aspect of gearbox fault diagnosis, the periodic pulse signal containing fault characteristics is often overwhelmed with other strong interference components, which brings a great challenge for gearbox fault detection and status identification. …”
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Detecting Software Anomalies in Robots by Means of One-class Classifiers by Héctor Quintián, Esteban Jove, Francisco Zayas-Gato, Nuño Basurto, Carlos Cambra, Álvaro Herrero

“…The growing dependence on collaborative robots in essential industrial and service sectors raises urgent concerns regarding their reliability and ability to handle faults. Undetected software issues can degrade performance, jeopardize safety, and result in expensive downtimes. …”
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Circuit Breaker Energy Storage State Identification Based on Quick Extraction of Vibration Signal Interval Features by Xiaofei XIA, Yufeng LU, Yi SU, Jian YANG

“…Finally, the fuzzy C-means clustering (KFCM) was used to pre-classify the features to obtain the optimal hyperplane with the least risk, and a training model was established with support vector machine (SVM) for state identification. The experimental results show that the proposed state identification method only takes 0.2 s to extract features with reliable recognition accuracy, which has important application value in the field of circuit breaker state monitoring.…”
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Detrended Fluctuation Analysis and Hough Transform Based Self-Adaptation Double-Scale Feature Extraction of Gear Vibration Signals by JiaQing Wang, Han Xiao, Yong Lv, Tao Wang, Zengbing Xu

“…For further understanding, the simulation analysis is performed to investigate the reasons for double-scale of gear’s fault vibration signal. According to the analysis results, a DFA double logarithmic plot based feature vector combined with scale exponent and intercept of the small time scale is utilized to achieve a better performance of fault identification. …”
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