Induction Motor Stator Current AM-FM Model and Demodulation Analysis for Planetary Gearbox Fault Diagnosis

This paper deals with the possibilities of bearing fault diagnosis of induction motors by stator currents analysis based on Park’s vector approach. The main theoretical aspects, the experimental results and their analysis are presented. It is shown that preventive stator current diagnostics of induction motors based on Park’s vector allows us early detection of bearing defects in the inner and outer races. The current spectra additional harmonics appearing in the case of bearing damage are found.

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Stator current fault diagnosis of induction motor bearings based on the fast Fourier transform

Russian Electrical Engineering ◽

10.3103/s1068371216120063 ◽

2016 ◽

Vol 87 (12) ◽

pp. 661-665 ◽

Cited By ~ 10

Author(s):

N. R. Safin ◽

V. A. Prakht ◽

V. A. Dmitrievskii ◽

A. A. Dmitrievskii

Keyword(s):

Fourier Transform ◽

Fault Diagnosis ◽

Fast Fourier Transform ◽

Induction Motor ◽

Stator Current

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Acquisition and pre-processing of three phase induction motor stator current signal for fault diagnosis using FPGA, NI Compact-RIO real time controller

2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON) ◽

10.1109/upcon.2016.7894635 ◽

2016 ◽

Author(s):

Shubhasish Sarkar ◽

Pranab Kumar Hembram ◽

Prithwiraj Purkait ◽

Santanu Das

Keyword(s):

Fault Diagnosis ◽

Real Time ◽

Induction Motor ◽

Current Signal ◽

Stator Current ◽

Three Phase

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Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor

Energies ◽

10.3390/en13061475 ◽

2020 ◽

Vol 13 (6) ◽

pp. 1475 ◽

Cited By ~ 4

Author(s):

Maciej Skowron ◽

Teresa Orlowska-Kowalska ◽

Marcin Wolkiewicz ◽

Czeslaw T. Kowalski

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Induction Motor ◽

Current Data ◽

Operating Conditions ◽

Stator Winding ◽

Stator Current ◽

Short Circuits ◽

The Impact

In this paper, the idea of using a convolutional neural network (CNN) for the detection and classification of induction motor stator winding faults is presented. The diagnosis inference of the stator inter-turn short-circuits is based on raw stator current data. It offers the possibility of using the diagnostic signal direct processing, which could replace well known analytical methods. Tests were carried out for various levels of stator failures. In order to assess the sensitivity of the applied CNN-based detector to motor operating conditions, the tests were carried out for variable load torques and for different values of supply voltage frequency. Experimental tests were conducted on a specially designed setup with the 3 kW induction motor of special construction, which allowed for the physical modelling of inter-turn short-circuits in each of the three phases of the machine. The on-line tests prove the possibility of using CNN in the real-time diagnostic system with the high accuracy of incipient stator winding fault detection and classification. The impact of the developed CNN structure and training method parameters on the fault diagnosis accuracy has also been tested.

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Observer-based fault diagnosis and field oriented fault tolerant control of induction motor with stator inter-turn fault

Archives of Electrical Engineering ◽

10.2478/v10171-012-0015-1 ◽

2012 ◽

Vol 61 (2) ◽

pp. 165-188 ◽

Cited By ~ 19

Author(s):

Djilali Toumi ◽

Mohamed Boucherit ◽

Mohamed Tadjine

Keyword(s):

Fault Diagnosis ◽

Induction Motor ◽

Fault Tolerant ◽

Short Circuit ◽

Fault Tolerant Control ◽

Field Oriented Control ◽

Stator Current ◽

Rotating Reference Frame ◽

Rotor Flux ◽

Rotor Field Oriented Control

Observer-based fault diagnosis and field oriented fault tolerant control of induction motor with stator inter-turn fault This paper describes a fault-tolerant controller (FTC) of induction motor (IM) with inter-turn short circuit in stator phase winding. The fault-tolerant controller is based on the indirect rotor field oriented control (IRFOC) and an observer to estimate the motor states, the amount of turns involved in short circuit and the current in the short circuit. The proposed fault controller switches between the control of the two components of measured stator current in the synchronously rotating reference frame and the control of the two components of estimated current in the case of faulty condition when the estimated current in the short circuit is not destructive of motor winding. This technique is used to eliminate the speed and the rotor flux harmonics and to assure the decoupling between the rotor flux and torque controls. The results of the simulation for controlling the speed and rotor flux of the IM demonstrate the applicability of the proposed FTC.

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Condition Monitoring and Feature Extraction of Stator Current Phasors for Enhanced Fault Diagnosis in AC Drive

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a3173.1011121 ◽

2021 ◽

Vol 11 (1) ◽

pp. 174-180

Author(s):

Jyothi R ◽

◽

Tejas Holla ◽

Umesh NS ◽

K Uma Rao ◽

...

Keyword(s):

Fault Diagnosis ◽

Induction Motor ◽

Condition Monitoring ◽

Industrial Applications ◽

Voltage Source ◽

Variable Frequency ◽

Process Industries ◽

Ac Drives ◽

Stator Current ◽

Variable Frequency Drives

AC drives are employed mainly in process plants for various applications. In most industrial applications, Induction motor drives are preferred as they are robust, reliable, and efficient. Process industries have seen a paradigm shift from manual control to automatic control. Advancements in power electronics technology have led to smooth control of the induction motor using variable frequency drives over an entire speed range. Variable Frequency Drives (VFD) comprises of Voltage source inverter and a three phase squirrel cage induction motor. Various electric faults that are incipient in the VFD cause an abrupt change in circuit parameters resulting in insulation damage, reduced efficiency, and leading to catastrophic failure of the entire system. Hence, continuous monitoring of the system parameters such as stator current, speed, and the vibration of the machine is essential to diagnose incipient faults in the system. AI techniques have been effectively used in the fault diagnosis of electrical systems. In the proposed work, simulation results of machine learning-based fault diagnosis techniques are presented. Real-time IoT-based condition monitoring of the Variable Frequency Drive is also implemented for enhanced fault diagnosis of various incipient electrical faults in AC drives. The experimental results obtained are validated with the simulation data.

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