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A Fault Analysis Method for Three‐Phase Induction Motors Based on Spiking Neural P Systems
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Entailment for intuitionistic fuzzy sets based on generalized belief structures
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Fault classification in three-phase motors based on vibration signal analysis and artificial neural networks
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Uncertainty measure in evidence theory
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Novel approach using Hilbert Transform for multiple broken rotor bars fault location detection for three phase induction motor
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Mechanical fault detection in electric motors measured by a digital signal processing device in an optical mouse
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Induction Motors Fault Diagnosis Using Finite Element Method: A Review
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Detection and Diagnosis of Faults in Induction Motor Using an Improved Artificial Ant Clustering Technique
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Enhanced Sparse Period-Group Lasso for Bearing Fault Diagnosis
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Moving Integration Filter-Based Open-Switch Fault-Diagnosis Method for Three-Phase Induction Motor Drive Systems
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Automatic Implementation of Fuzzy Reasoning Spiking Neural P Systems for Diagnosing Faults in Complex Power Systems
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Induction Motor Stator Interturn Short Circuit Fault Detection in Accordance with Line Current Sequence Components Using Artificial Neural Network
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Detection of Deterioration of Three-phase Induction Motor using Vibration Signals
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Adaptive Fault Diagnosis of Motors Using Comprehensive Learning Particle Swarm Optimizer with Fuzzy Petri Net
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Automatic Fault Diagnostic System for Induction Motors under Transient Regime Optimized with Expert Systems
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