• Proceedings of the KIEE Conference
• /
• 1997.07b
• /
• pp.485-487
• /
• 1997

Schemes for detecting and diagnosing faults are presented. Faults are detected when residuals change significantly and thresholds are exceed. Two stage artificial neural networks are applied to diagnose faults. The idealized steady state patterns of residuals are defined and learned by ANNs using back propagation algorithm. The first stage ANN is trained to classify the subsystem in which the various faults are located. The first stage ANN could be also used to detect faults with threshold, checking. The second stage ANNs are trained to discriminate the specific cause of a fault at the subsystem level.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.66 no.11
• /
• pp.1657-1662
• /
• 2017

Rotor bar faults in induction machines, which are a part of main distribution of power system, can even stop the entire system by causing contact between a stator and a rotor. There are two methods of diagnosing rotor bar faults in induction motors, online and offline tests, and existing diagnosis methods have many limitations which can lead to misdiagnosis. This paper proposes a potable rotor bar faults diagnosis system based on single phase rotation test, one of offline test methods, which detects rotor bar faults through impedance interpretation by exciting AC current in a stator winding. The test was conducted on a motor of 0.4kW in the laboratory and a motor of 1500kW in industry field.

• Journal of Institute of Control, Robotics and Systems
• /
• v.3 no.3
• /
• pp.244-251
• /
• 1997

In this paper, we propose a fault diagnosis algorithm to detect and isolate multiple faults in a system. The proposed fault diagnosis algorithm is based on a multiple fault classifier which consists of two ART2 NN(adaptive resonance theory2 neural network) modules and the algorithm is composed of three main parts - parameter estimation, fault detection and isolation. When a change in the system occurs, estimated parameters go through a transition zone in which residuals between the system output and the estimated output cross the threshold, and in this zone, estimated parameters are transferred to the multiple faults classifier for fault isolation. From the computer simulation results, it is verified that when the proposed diagnosis algorithm is performed successfully, it detects and isolates faults in the position control system of a DC motor.

• Proceedings of the IEEK Conference
• /
• 2002.06a
• /
• pp.283-286
• /
• 2002

For the satisfying performance of a oil lubricator, design of a oil controller for the system which meets the required specifications and its supporting hardware that keep their functioning is important. Among the hardware of a control system, oil system are most vulnerable to malfunction. Thus it is necessary to keep track of accurate and reliable oil readings for good fusion oil lubricator performance. In case of oil lubricator ,data loss, ssr trigger error faults, they are detected by examining the data system output values and the major values of the system, and then the faults are recognized by the analysis of symptoms of faults. If necessary electronic -sensor values are synthesized according to the types of faults, and then they are used for the controller instead of the raw data. In this paper, a fast-32bit cpu micorprocessor applied to the control of flexible link system with the sensor fault problems in the error module fer exact positioning to show the applicability. It is shown that the fusion oil lubricator can provide a satisfactory loop performance even when the sensor faults occure

• Proceedings of the KSR Conference
• /
• 2006.11b
• /
• pp.144-151
• /
• 2006

Sound generated from a moving vehicle often carries information on the condition of vehicle, for example, whether it has faults or not, where the fault exists. The latter is possible especially by MFAH(moving frame acoustic holography) and beamforming method. MFAH is applicable to the sound source of pure tone or narrow band noise. For the beamforming method, we have to know what kind of wave the sound source radiates, for example, plane wave or spherical wave. That is, whether the above methods are applicable depends on the characteristics of sound source. To apply these methods to the fault detection, we have to know the characteristics of wave from faults. In this research, a machine diagnosis technique based on the above holographic approaches is introduced to find the position of faults. The signal due to faults is modeled based on the fact that the faults radiate impulsive noise, and analyzed in time and frequency domain. The way how MFAH and beamforming method can be used is introduced to find the position of source.

• 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.

• Journal of Electrical Engineering and Technology
• /
• v.5 no.1
• /
• pp.140-145
• /
• 2010

The monitoring and the diagnosis of the faults in induction motors starting from the stator current are very interesting, since it is an accessible and measurable quantity. The spectral analysis of the stator current makes it possible to highlight the characteristic frequencies of the faults but in a wide frequency range depending on half the sampling frequency, making it very difficult to monitor on-line the faults. In order to facilitate the use of the relevant frequencies of machine faults we proposed the extraction of the frequency components using two methods, namely, the amplitude and the instantaneous frequency. The theoretical bases of these methods were presented and the results were validated on a test bench with an induction motor of 5.5 kw.

• Journal of information and communication convergence engineering
• /
• v.3 no.1
• /
• pp.28-32
• /
• 2005

In this paper, we propose a novel test method to effectively detect hard and soft faults in CMOS 2-stage op-amps. The proposed method uses a very high frequency sinusoidal signal that exceeds unit gain bandwidth to maximize the fault effects. Since the proposed test method doesn't require any complex algorithms to generate the test pattern and uses only a single test pattern to detect all target faults, therefore test costs can be much reduced. The area overhead is also very small because the CUT is converted to a unit gain amplifier. Using HSPICE simulation, the results indicated a high degree of fault coverage for hard and soft faults in CMOS 2-stage op-amps. To verify this proposed method, we fabricated a CMOS op-amp that contained various short and open faults through the Hyundai 0.65-um 2-poly 2-metal CMOS process. Experimental results for the fabricated chip have shown that the proposed test method can effectively detect hard and soft faults in CMOS op-amps.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.65 no.7
• /
• pp.1186-1192
• /
• 2016

This paper proposes techniques to diagnose short-circuit faults of both the diodes and power FET in switching mode power supply (SMPS) by using a simple analog tester. The diodes in full-bridge rectifier, power FET, switching transformer, and some sensors are modelled with resistor. The total resistance value measured at the input terminal of a SMPS is analyzed when the short-circuit faults of diodes in a full bridge rectifier or power FET are occurred. The short-circuit faults of one or two diodes in a full bridge rectifier, power FET, and both the diodes in a full bridge rectifier and power FET can be detected by a range of total resistance, which is measured by the analog tester. Through experiments, the theoretical analysis for total resistance under short-circuit faults can be verified.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2004.11a
• /
• pp.1093-1096
• /
• 2004

Bearings are elementary machinery component which loads and do rotating motion. Excessive loads or many other reasons can cause incipient faults to be created and grown in each component. Moreover, it happens that incipient faults which were caused by manufacturing or assembling process' errors of the bearings are created. Finding the incipient faults as early as possible is necessary to the bearings in severe condition: high speed or frequently varying load condition, etc. How early we can detect the faults has to do with how the detection algorithm finds the fault information from measured signal. Fortunately, the bearing fault signal makes periodic impulse train. This information allows us to find the faults regardless how much noise contaminates the signal. This paper shows the basic signal processing idea and experimental results that demonstrate how good the method is.