• Transactions of the Korean Society of Automotive Engineers
• /
• v.17 no.5
• /
• pp.26-30
• /
• 2009

In this paper, a robust model based fault detection for varying temperature is proposed, To develop a robust force estimation model, it needs temperature information because the force sensor's output is affected by a temperature variation. If an EPB system does not include a temperature sensor, the model has a much larger error than an EPB system with a built-in temperature sensor. Therefore, the temperature is estimated by using Ohm's law. The force model is applied with a motor current, battery voltage, operation mode, and the estimated temperature to detect a force sensor's abnormal signal fault. The residual is calculated by comparing the value of the measured force and the estimated force. Fault information is collected by using the output of the evaluated residual with the adaptive thresholds. A proposed robust model based fault detection for varying temperature was verified by HILS (Hardware in the Loop Simulation).

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.23 no.4
• /
• pp.62-71
• /
• 2009

In this paper, we propose a fault diagnosis algorithm for BLDC motors by principle component analysis (PCA) and implement a real-time fault diagnosis system for BLDC motors. To verify the proposed diagnosis algorithm, various faulty data are acquired by Lab VIEW program from experimental system. We extract a fault feature using principle component analysis after preprocessing and then finally the fault diagnosis is performed by Euclidean similarity. Also, we embed the PCA algorithm and k-NN classification algorithm into a digital signal processor. From various experiments, we found that the proposed algorithm can be used as a powerful technique to classify the several fault signals acquired from BLDC motors.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.60 no.4
• /
• pp.805-810
• /
• 2011

This paper deals with the method and algorithm used to find fault locations in gas insulated transmission line. The method uses UHF sensors and digital oscilloscope to detect discharge signals emitted to the outside through insulating spacer in the event of breakdown inside GIL. UHF sensors are the external type and installed at outside of insulating spacers of GIL. And we used wavelet signal processing to analyze the discharge signals and confirm the exact fault location findings in the GIL test line. This method can overcome demerit of TDR(Time Domain Reflectometer) method having been applied to detect fault location for conventional underground transmission lines, and Ground Fault Sensors used in conventional GIS systems. TDR method requires high level of specialty and experience in analyzing the measured signals. Ground fault sensors are installed inside GIL and can be destroyed by high transient voltage. This paper's method can simplify the fault location process and minimize the damage of sensors. In addition, this method can estimate the fault location only by the time difference when discharge signals are arrived to detecting sensors at the ends of GIL sections without reasons of breakdown. To test the performance of our method, we installed sensors at the ends of test line of GIL(84m) and sensed discharge signals occurred in GIL, energized with AC voltage generator up to 700kV.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.41 no.11
• /
• pp.735-742
• /
• 2017

In this study, an effective model-based sensor fault detection methodology that can detect and isolate PEM temperature sensors fault is introduced. In fuel cell vehicle operation process, the stack temperature affects durability of a fuel cell. Thus, it is important for fault algorithm to detect the fault signals. The major objective of sensor fault detection is to guarantee the healthy operations of the fuel cell system and to prevent the stack from high temperature and low temperature. For the residual implementation, parity equation based on the state space is used to detect the sensors fault as stack temperature and coolant inlet temperature, and residual is compared with the healthy temperature signals. Then the residuals are evaluated by various fault scenarios that detect the presence of the sensor fault. In the result, the designed in this study fault algorithm can detect the fault signal.

• 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 Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.539-542
• /
• 2003

A motor is the workhorse of our industry. The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. Different internal motor faults (e.g., inter-turn short circuits, broken bearings, broken rotor bars) along with external motor faults (e.g., phase failure, mechanical overload, blocked rotor) are expected to happen sooner or later. This paper introduces the fault detection technique of induction motors based upon the stator current. The fault motors have rotor bar broken or rotor unbalance defect, respectively. The stator currents are measured by the current meters and stored by the time domain. The time domain is not suitable to represent the current signals, so the frequency domain is applied to display the signals. The Fourier Transformer is used for the conversion of the signal. After the conversion of the signals, the features of the signals have to be extracted by the signal processing methods like a wavelet analysis, a spectrum analysis, etc. The discovered features are entered to the pattern classification model such as a neural network model, a polynomial neural network, a fuzzy inference model, etc. This paper describes the fault detection results that use wavelet decomposition. The wavelet analysis is very useful method for the time and frequency domain each. Also it is powerful method to detect the features in the signals.

• Journal of the Institute of Convergence Signal Processing
• /
• v.20 no.2
• /
• pp.78-83
• /
• 2019

Sounds based machine fault diagnosis recovers all the studies that aim to detect automatically abnormal sound on machines using the acoustic emission by these machines. Conventional methods that use mathematical models have been found inaccurate because of the complexity of the industry machinery systems and the obvious existence of nonlinear factors such as noises. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose here an automatic fault diagnosis method of hand drills using discrete wavelet transform(DWT) and pattern recognition techniques such as artificial neural networks(ANN). We first conduct a filtering analysis based on DWT. The power spectral density(PSD) is performed on the wavelet subband except for the highest and lowest low frequency subband. The PSD of the wavelet coefficients are extracted as our features for classifier based on ANN the pattern recognition part. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.

• Journal of Advanced Navigation Technology
• /
• v.17 no.5
• /
• pp.560-567
• /
• 2013

In this paper, it is proposed the fault detection method of the ASIC, without the Test Requirement Document(TRD), extracting internal logic circuit and analyzed the function of the ASIC using the multipurpose development program and simulation. If there don't have the TRD, it is impossible to analyze the operation of the circuit and find out the fault detection in any chip. Therefore, we make the TRD based on the analyzed logic data of the ASIC, and diagnose of the ASIC circuit at the gate level through the signal control of I/O pins using the Dynamic Pattern signal. According to the experimental results of the proposed method, we is confirmed the good performance of the fault detection capabilities which applied to the non-memory circuit.

• Journal of the Institute of Convergence Signal Processing
• /
• v.21 no.3
• /
• pp.121-126
• /
• 2020

Sound-based machine fault diagnosis is the automatic detection of abnormal sound in the acoustic emission signals of the machines. Conventional methods of using mathematical models were difficult to diagnose machine failure due to the complexity of the industry machinery system and the existence of nonlinear factors such as noises. Therefore, we want to solve the problem of machine fault diagnosis as a deep learning-based image classification problem. In the paper, we propose a CNN-based automatic machine fault diagnosis method using Spectrogram images. The proposed method uses STFT to effectively extract feature vectors from frequencies generated by machine defects, and the feature vectors detected by STFT were converted into spectrogram images and classified by CNN by machine status. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.

• Proceedings of the KIEE Conference
• /
• 2008.04a
• /
• pp.228-229
• /
• 2008

This paper presents stochastic methodology based fault diction and diagnosis algorithm for induction motor systems. First, we construct probability distribution model from healthy motors and then probability distribution for faulty motors is recursively calculated by means of the proposed probability estimation. We measure motor current with hall sensors as system state. The estimated probability is compared to the model to generate a residue signal which is utilized for fault detection and diagnosis, that is, where a fault is occurred. We carry out real-time induction motor experiment to evaluate efficiency and reliability of the proposed approach.