Fuzzy diagnostics for gearbox failures based on induction motor current and wavelet entropy

2021 ◽  
Vol 43 (5) ◽  
Author(s):  
Alexander Patrick Chaves de Sena ◽  
Isaac Soares de Freitas ◽  
Abel Cavalcante Lima Filho ◽  
Carlos Alberto Nobrega Sobrinho
Keyword(s):  
Induction Motor ◽  
Wavelet Entropy ◽  
Motor Current

scholarly journals Analysis of Induction Motor Performance Using Motor Current Signature Analysis Technique

2021 ◽  
Vol 5 (1) ◽  
pp. PRESS
Author(s):  
Ramadoni Syahputra ◽  
Hedi Purwanto ◽  
Rama Okta Wiyagi ◽  
Muhamad Yusvin Mustar ◽  
Indah Soesanti
Keyword(s):  
Power Plant ◽  
Induction Motor ◽  
Induction Motors ◽  
Signature Analysis ◽  
Steam Power ◽  
Reliable Technique ◽  
Steam Power Plant ◽  
Motor Current ◽  
Motor Current Signature Analysis ◽  
Current Signature

This paper discusses the analysis of the performance of an induction motor using the motor current signature analysis (MCSA) technique. Induction motor is a type of electric machine that is widely used in industry. One of the industries that utilize induction motors is a steam power plant (SPP). The role of induction motors is very vital in SPP operations. Therefore, it is necessary to monitor the performance, stability, and efficiency to anticipate disturbances that can cause damage or decrease the life of the induction motor. MCSA is a reliable technique that can be used to analyze damage to an induction motor. In this technique, the induction motor current signal is detected using a current transducer. The signal is then passed on to the signal conditioning and then into the data acquisition device. The important signal data is analyzed in adequate computer equipment. The results of this analysis determine the condition of the induction motor, whether it is normal or damaged. In this research, a case study was carried out at the Rembang steam power plant, Central Java, Indonesia. The results of the analysis of several induction motors show that most of them are in normal conditions and are still feasible to operate.

Modeling, Analysis, of Induction Motor's Stator Turns Fault Using Neuro-Fuzzy

2021 ◽  
pp. 498-520
Author(s):  
Hussein. A. Taha ◽  
M. E. Ammar ◽  
M. A. Moustafa Hassan
Keyword(s):  
Induction Motor ◽  
Diagnostic Tool ◽  
Short Circuit ◽  
Simulated Data ◽  
Fault Detection And Diagnosis ◽  
Modeling And Analysis ◽  
Motor Current ◽  
Neuro Fuzzy ◽  
Three Phase ◽  
Detection And Diagnosis

This chapter discusses modeling and analysis methods for fault detection and diagnosis of stator inter-turn short circuit in three-phase induction machines. dq frame was used to model the induction motor for both health and fault cases to facilitate recognition of motor current and simulate motor environment. Fault diagnosis system was designed with adaptive neuro-fuzzy inference system (ANFIS) to provide an efficient online diagnostic tool. ANFIS diagnostic tool was trained with simulated data that generated by induction motor healthy and faulty models. Approached tool is verified online with a motor under different loading conditions. It determines the fault severity values using the motor current signature analysis (MCSA). Developed tool performance is investigated with a case study of two HP three-phase induction motor using Matlab/Simulink® software.

scholarly journals New Spectral Markers for Broken Bars Diagnostics in Induction Motors

Machines ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 6 ◽  
Author(s):  
Georgii D. Baranov ◽  
Erivelton G. Nepomuceno ◽  
Michail A. Vaganov ◽  
Valerii Y. Ostrovskii ◽  
Denis N. Butusov
Keyword(s):  
Induction Motor ◽  
Induction Motors ◽  
Simulation Analysis ◽  
Real Life ◽  
Motor Current ◽  
Deterioration Process ◽  
Motor Current Signature Analysis ◽  
The Given ◽  
Current Signature ◽  
Theoretical Results

The paper discusses the spectral markers of fault rotor bars in induction motor current signature analysis (MCSA). The results of the simulation of the deterioration process for a single rotor bar, as well as the results of research for various mutual bracing of two broken bars, are reported. We proposed a simple empiric technique allowing one to obtain frequencies for spectrum markers of damaged rotor bars based on simulation analysis. The set of frequencies obtained in the experimental part of the study was compared with simulation results and the results of real-life measurements. The theoretical results were verified through the experiment with the real induction motor under load. Analysis of experimental results proved that the given algorithm for spectrum analysis is suitable for early detection of fault rotor bars in induction motors.

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