Detection and Phase Identification of Inter-Turn Short-Circuit Faults in Three-Phase Induction Motors Using MEMS Accelerometer and Hilbert Transform

Author(s):  
Marco Rocha ◽  
Guilherme Lucas ◽  
Wallace Souza ◽  
Bruno Albuquerque de Castro ◽  
Andre Andreoli
2012 ◽  
Vol 10 (6) ◽  
pp. 2241-2248
Author(s):  
Marcel Chuma Cerbantes ◽  
Waldemar Pereira Mathias ◽  
Jose Roberto Sanches Mantovani

2020 ◽  
Vol 11 ◽  
pp. 11-17
Author(s):  
Gabriel Nicolae Popa ◽  
Corina Maria Diniș

Low-voltage three-phase induction motors are most often used in industrial electric drives. Electric motors must be protected by electric and/or electronic devices against: short-circuit, overloads, asymmetrical currents, two-phase voltage operation, under-voltage, and over-temperature. To design the electronic protection currents, voltages and temperature must be measured to determine whether they fall within normal limits. The electronic protection was design into low capacity PLC. The paper presents the designs and analysis of complex electronic protection for general purpose low-voltage three-phase induction motors. The electronic protection has Hall transducers and conversion electronic devices for AC currents to DC voltages, AC voltages to DC voltage, temperature to DC voltage, a low capacity PLC, switches, motor’s power contactors, and signalling lamps has been developed. Experiments with complex electronic protection, for different faults are presented. The proposed protection has the advantages of incorporating all usual protections future for the low-voltage three-phase induction motors.


2014 ◽  
Vol 875-877 ◽  
pp. 1923-1928 ◽  
Author(s):  
Surya Hardi ◽  
Ismail Daut ◽  
Ismail Rohana ◽  
Muhd Hafizi

Voltage sags and interruption are one of most important of power quality problems. They can influence performance of equipment such as induction motors. They are generally caused by short circuit faults in transmission and distribution systems which propagate in entire of power systems. When their appear at a motor terminal, its effects are the speed and the torque will decrease to a level lower than values of the normal and even the motor become stall if magnitude of the voltage sags and duration exceed certain limit. The voltage can return to nominal voltage after end of the voltage sag and interruption. The motor will experience re-acceleration to normal condition is accompanied by large inrush current. A study on induction motors was carried out to confirm these effects. Single-phase and three-phase of small induction motors were used for investigating the effects caused by symmetrical voltage sags and interruption through experimental and simulation.


2013 ◽  
Vol 433-435 ◽  
pp. 705-708 ◽  
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu

In fault diagnosis of three-phase induction motors, traditional methods usually fail because of the complex system of three-phase induction motors. Short circuit is a very common stator fault in all the faults of three-phase induction motors. Probabilistic neural network is a kind of artificial neural network which is widely used due to its fast training and simple structure. In this paper, the diagnosis method based on probabilistic neural network is proposed to deal with stator short circuits. First, the principle and structure of probabilistic neural network is studied in this paper. Second, the method of fault setting and fault feature extraction of three-phase induction motors is proposed on the basis of the fault diagnosis of stator short circuits. Then the establishment of the diagnosis model based on probabilistic neural network is illustrated with details. At last, training and simulation tests are done for the model. And simulation results show that this method is very practical with its high accuracy and fast speed.


2020 ◽  
Vol 2 (1) ◽  
pp. 32
Author(s):  
Guilherme Lucas ◽  
Marco Rocha ◽  
Bruno Castro ◽  
José Leão ◽  
André Andreoli

Three-phase induction motors (TIMs) play a key role in industrial production lines. Due to their robustness and versatility, TIMs are commonly used to drive different devices like fans, conveyors, sieves, and compressors. However, these devices are often exposed to mechanical and electrical faults. Among them, failures in stator winding insulation lead to severe damage to the TIMs and can cause operational interruptions. Therefore, several approaches have been developed to monitor electrical faults in induction motors. The acoustic emission (AE) stands out as an efficient non-invasive technique (NIT) for TIM diagnosis. In this work, the AE analysis was applied to detect winding insulation faults and identify which electrical phase was affected. To achieve this proposal, a TIM was subjected to insulation faults in each of the three electrical phases, and the acoustic signals were acquired by four piezoelectric sensors attached to the motor. These signals were processed using a new technique, which calculates the energy of a specific range of the signal spectrum and assigns the energy values of each piezoelectric sensor to a coordinate axis (x, y). By ploting the values for each fault condition, this technique allows the detection of insulation faults and correctly identifies the affected phase by clustering the resulting values. Finally, the proposed methodology presented satisfactory results in winding insulation diagnosis.


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