• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.14 no.8
• /
• pp.675-683
• /
• 2004

The motor current signature provides an important source of the information for the faults diagnosis of three-phase induction motor. The theoretical principles behind the generation of unique signal characteristics, which are indicative of failure mechanisms, are Presented. The fault detection techniques that can be used to diagnose mechanical Problems, stator and rotor winding failure mechanisms, and air-gap eccentricity are described. A theoretical analysis is presented which predicts the presence of unique signature patterns in the current that are only characteristics of the fault. The predictions are verified by experimental results from a special fault Producing test rig and on-site tests in a steel company. And this study have made new diagnostic algorithm for the operating induction motors with the test results. These developments are including the use of monitoring and analysis of electric current to diagnose mechanical and electrical problems and gave the precise test results automatically.

• Proceedings of the KIEE Conference
• /
• 2011.07a
• /
• pp.2077-2078
• /
• 2011

Now a days, the induction motor is widely used in industry automation. Without monitoring the motor fault, maintenance cost is increased undesirably high. The slip frequency is included in the feature frequency, so rotating rotor speed is needed. In this paper, a sensorless motor speed estimation method, rotor slot harmonic(RSH) method is suggested and a solution of rotor bar diagnosis is proposed for motor running with light-load. When the rotor is rotating, it shows the harmonic signal of back-emf voltage related with number of rotor slot. So from the power spectrum of current signal, we can find the rotor speed.

• The KSFM Journal of Fluid Machinery
• /
• v.7 no.2 s.23
• /
• pp.41-49
• /
• 2004

Condition monitoring is very important in turbo machinery because single failure could cause critical damages to its plant. So, automatic fault recognition has been one of the main research topics in condition monitoring area. We have used a relatively new fault recognition method, Hidden Markov Model(HMM), for mechanical system. It has been widely used in speech recognition, however, its application to fault recognition of mechanical signal has been very limited despite its good potential. In this paper, discrete HMM(DHMM) was used to recognize the faults of rotor system to study its fault recognition ability. We set up a rotor kit under unbalance and oil whirl conditions and sampled vibration signals of two failure conditions. DHMMS of each failure condition were trained using sampled signals. Next, we changed the setup and the rotating speed of the rotor kit. We sampled vibration signals and each DHMM was applied to these sampled data. It was found that DHMMs trained by data of one rotating speed have shown good fault recognition ability in spite of lack of training data, but DHMMs trained by data of four different rotating speeds have shown better robustness.

• Journal of Institute of Control, Robotics and Systems
• /
• v.9 no.11
• /
• pp.874-882
• /
• 2003

The condition assessment of engineering systems has increased in importance because the manpower needed to operate and supervise various plants has been reduced. Especially, induction motors are at the core of most engineering processes, and there is an indispensable need to monitor their health and performance. So detection and diagnosis of motor faults is a base to improve efficiency of the industrial plant. In this paper, a model-based fault detection system is developed for induction motors, using steady state vibration signals. Early various fault detection systems using vibration signals are a trivial method and those methods are prone to have missed fault or false alarms. The suggested motor fault detection system was developed using a model-based reference value. The stationary signal had been extracted from the non-stationary signal using a data segmentation method. The signal processing method applied in this research is FFT. A reference model with spectra signal is developed and then the residuals of the vibration signal are generated. The ratio of RMS values of vibration residuals is proposed as a fault indicator for detecting faults. The developed fault detection system is tested on 800 hp motor and it is shown to be effective for detecting faults in the air-gap eccentricities and broken rotor bars. The suggested system is shown to be effective for reducing missed faults and false alarms. Moreover, the suggested system has advantages in the automation of fault detection algorithms in a random signal system, and the reference model is not complicated.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2010.05a
• /
• pp.100-101
• /
• 2010

• Journal of Electrical Engineering and Technology
• /
• v.10 no.6
• /
• pp.2315-2325
• /
• 2015

Induction motors are widely used in industrial processes since they offer a very high degree of reliability. But like any other machine, they are vulnerable to faults, which if left unmonitored, might lead to an unexpected interruption at the industrial plant. Therefore, the condition monitoring of the induction motors have been a challenging topic for many electrical machine researchers. Indeed, the effectiveness of the fault diagnosis and prognosis techniques depends very much on the quality of the fault features selection. However, in induction-motor drives, rotor defects are the most complex in terms of detection since they interact with the supply frequency within a restricted band around this frequency, especially in the no-loaded case. To overcome this drawback, this paper deals with an efficient and new method to diagnose the induction-motor rotor fault based on the digital implementation of the monitoring algorithm based on the association of the Time Synchronous Averaging technique and Discrete Wavelet Transform. Experimental results are presented in order to show the effectiveness of the proposed method. The obtained results are largely satisfactory, indicating a promising industrial application of the combined “Time Synchronous Averaging – Discrete Wavelet Transform” approach.

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

• Proceedings of the KIEE Conference
• /
• 2001.07b
• /
• pp.635-637
• /
• 2001

The faults of the squirrel cage induction motor is grew increasingly complex as the faults resulting in the shorting of a stator winding and the broken rotor bar or cracked rotor end ring, bearing faults, and so on. The users of electrical machines initially relied on simple protections such as over-current, over-voltage, earth-fault, etc. to ensure safe and reliable operation. but this method cause heavy financial losses and the threat of safety therefore it has now become very important to diagnose faults at there very inception. in this paper, we are going to discuss the detection method of broken rotor bar of squirrel cage induction motor by the motor current signal analysis(MCSA) and the opening terminal voltage signal analysis.

• Journal of Power Electronics
• /
• v.12 no.6
• /
• pp.982-991
• /
• 2012

In this study, a new method has been presented for the detection of broken rotor bar (BRB) faults in inverter driven induction motors controlled via Field Oriented Control (FOC). To this end, a FOC controlled induction motor with a BRB fault was modeled using the Matlab/Simulink program. Experiments were carried out using the prepared simulation model at various loads and operating speeds. The motor current and speeds were monitored for healthy, 1, 2 and 3 BRB faults. The Resampling Based Order Tracking Analysis (RB-OTA) method was applied to the monitored signals. The obtained results were compared by using the classic Fast Fourier Transform (FFT) method. When the obtained results were analyzed via the FFT method no information regarding any faults was determined in the run up or run down regions of the motor and the presented method gave very good results. The reliability of the proposed method was validated with experimental results. The main innovative part of this study is that the RB-OTA method was implemented on the induction motor current signal for detecting BRB faults.

• Proceedings of the KIEE Conference
• /
• 2009.07a
• /
• pp.2019_2020
• /
• 2009

A recurrent surge oscillograph(RSO) test was performed at the Taean thermal power plant on #5 turbine generator. The test was conducted using a rotor reflectometer and digital oscilloscope. A DC voltage step is applied to each end of the rotor winding in turn. Each reflected wave, at the input end of the winding, is monitored and the two waveforms are superimposed automatically and monitored on a single channel oscilloscope. As the half windings in a rotor are identical, the two waveforms monitored at each end of the rotor will also be identical for a healthy winding. A winding with a fault will cause different voltages to be monitored at the two ends.