• Journal of Korea Multimedia Society
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• v.20 no.11
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• pp.1811-1819
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• 2017

As the induction motor is the core production equipment of the industry, it is necessary to construct a fault prediction and diagnosis system through continuous monitoring. Many researches have been conducted on motor fault diagnosis algorithm based on signal processing techniques using Fourier transform, neural networks, and fuzzy inference techniques. In this paper, we propose a fault diagnosis method of induction motor using LPC and DNN. To evaluate the performance of the proposed method, the fault diagnosis was carried out using the vibration data of the induction motor in steady state and simulated various fault conditions. Experimental results show that the learning time of our proposed method and the conventional spectrum+DNN method is 139 seconds and 974 seconds each executed on the experimental PC, and our method reduces execution time by 1/8 compared with conventional method. And the success rate of the proposed method is 98.08%, which is similar to 99.54% of the conventional method.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.2073-2074
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• 2011

In this study, an algorithm for diagnosis of dc motor with winding open fault is suggested. A dc motor used in this paper, is consisted of a permanent magnet field stator, double 16-turn series winding rotating armature with 12-slot, brush and 12-commutator, etc. A current signal of dc motor which has brushes and commutatorswas considered for fault diagnosis. By commutation, this current signal shows different wave form according to the fault condition of the motor. In this study, operation of the data was easily through simplification of the current signal by the signal processing. Computation method is presented reference value($C_{dv}$) for diagnosis of winding open fault and verified through experiments that can be diagnosed using the reference value($C_{dv}$).

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1876-1877
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• 2011

In this paper, a winding fault diagnosis method base on MCSA(Motor Current Signature Analysis) for BLDC motor is proposed. This method is programmed by LabVIEW for winding fault diagnosis. For winding fault diagnosis, two types of winding fault(shorted turn at one pole, shorted turn at two pole in same phase) are put intentionally in on phase. The motor current is collected by hole sensor, and transformed by the Park's transform, and then the Park's Vector Pattern are obtained, Usually this pattern is formed an ellipse, so a proper threshold value of distortion ratio(the ratio of the shortest axis and longest axis of ellipse) is suggested for winding faults diagnosis.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.10
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• pp.516-523
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• 2000

In this paper, intelligent methods using digital protective relay in power supervisory control system is developed in order to protect power systems by means of timely fault detection and diagnosis during operation for induction motor which has various load environments and capacities in power systems. The spectrum pattern of input currents was used to monitor to state of induction motors, and by clustering the spectrum pattern of input currents, the newly occurrence of spectrums pattern caused by faults were detected. For diagnosis of the fault detected, the fuzzy fault tree was derived, and the fuzzy relation equation representing the relation between an induction motor fault and each fault type, was solved. The solution of the fuzzy relation equation shows the possibility of each fault's occurring. The results obtained are summarized as follows: 1) The test result on the basis of KEMC1120 and IEC60255, show that the operation time error of the digital motor protective relay is improved within ${\pm}5%$. 2) Using clustering algorithm by unsupervisory learning, an on-line fault detection method, not affected by the characteristics of loads and rates, was implemented, and the degree of dependency by experts during fault detection was reduced. 3) With the fuzzy fault tree, fault diagnosis process became systematic and expandable to the whole system, and the diagnosis for sub-systems can be made as an object-oriented module.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.154-156
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• 1993

In this paper, fault analysis using simulation method and fault diagnosis scheme are presented for induction motor drive system. Major faults such as inverter 'a' phase open fault, inverter 'a'-'b' phase short circuit fault and inverter 'a' phase ground fault are analyzed and simulated. On-line and off-line fault diagnosis systems are proposed.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.2069-2070
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• 2011

In this paper, the fault diagnosis for a gearbox of BLDC motor. The stator of BLDC motor consists of coil winding so it is easy to cool down and it also has a high reliability. In addition, it doesn't have a brush so it is less trouble and good in maintenance. Coupling with the motor which is the power sources, the gear has a high power transfer efficiency and various rotation speed. The gear gets a high driving force through deceleration. Thus it has been widely used. The gearbox fault detection area has not attracted much attention from electrical engineering community. A few papers describe gearbox fault based on vibration. Gearbox fault is diagnosed through FFT analysis of current and voltage. Fault characteristic frequency side band detected by calculating fault frequency. A threshold value is suggested by comparing normal peak value with fault peak value using detected fault characteristic frequency side band. Experimental results demonstrate that motor current and voltage signal analysis are viable tools in detecting these gear faults. Lower side band(LSB) is bigger than upper side band(USB) in current FFT. LSB and USB are similar in voltage FFT. Finally, fault diagnosis system that can easily detect flaws is developted for gearbox of BLDC motor.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.308-314
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• 2003

This paper presents a serial communication based fault diagnosis scheme for a brushless DC (BLDC) motor using parameter estimation and Bayes classifier. The presented scheme consists of a smart network board, and a fault detection and isolation (FDI) master. The smart network board is installed near the BLDC motor drive system to acquire motor data and transmit motor data to the FDI-master via serial communication channel. The FDI-master estimates BLDC motor resistance to detect symptom of faults, and assign symptom to fault type using Bayes classifier. In this scheme, since communication time delay has a serious effect on performance, periodic and fixed communication protocol is designed. Hence, the delay time is priory known. By experiment result, presented scheme was verified.

• Proceedings of the KIPE Conference
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• 2007.07a
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• pp.506-508
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• 2007

In this paper, the induction motor bearing fault diagnosis system using current signals which are measured by over-sampling method is presented. In the case of inverter fed motor drive unlike line-driven motor drive, that make a lot of noise which can cause a wrong fault signals because of PWM(pulse width modulation) voltage. So, the current signals for fault diagnosis need very precise and high resolution information, which means this system demand additional hardware such as low pass filter, high resolution ADC system and so on to use fault diagnosis system. Therefore, the proposed over-sampling method is expected to contribute to low cost fault diagnosis system even though previous inverter fed motor drive without any additional hardware. In order to confirm the presented algorithms, various experiments for bearing faults are tested and the line current spectrum of each faulty situation using park transformation is compared with a FFT results.

• International Journal of Automotive Technology
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• v.6 no.4
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• pp.341-350
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• 2005

In this study, fault diagnosis and monitoring of serial wound pre-engaged starter motors have been carried out. Starter motors are DC motors that enable internal combustion engine (ICE) to run. In case of breakdown of a starter motor, internal combustion engine can not be worked. Starter motors have vital importance on internal combustion engines. Kohonen network based fault diagnosis system is proposed for fault diagnosis and monitoring of starter motors. A graphical user interface (GUI) software has been developed by using Visual Basic 6.0 for fault diagnosis. Six faults, seen in starter motors, have been diagnosed successfully by using the developed fault diagnosis system. GUI software makes it possible to diagnose the faults in starter motors before they occur by keeping fault records of past occurrences.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.819-820
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• 2016

Electrical power distribution is consists of high voltage, low voltage and motor control center(MCC). Motor control centers involves turning the motor on and off, it is configured electronic over current relay to detect a motor overcurrent flows. Existing electronic over current relay detects electrical fault such as overcurrent, undercurrent, phase sequence, negative sequence current, current unbalance and earth fault. However, it is difficult to detect mechanical fault such as locked rotor, motor stator and rotor and bearing fault. In this paper, we propose a condition monitoring and fault diagnosis system for electrical and mechanical fault detection of rotating machinery. The proposed system is designed with signal input and control part, system interface part and data acquisition board for condition monitoring and fault diagnosis, it was possible to detect electrical fault and mechanical fault through measurement and control of insulation resistance, locked rotor, MC counter and bearing temperature.