• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.12
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• pp.1288-1296
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• 2001

A scheme for on-line fault detection and diagnosis of an air-handling unit is presented. The fault detection scheme uses residuals which are generated by comparing each measurement with analytical redundancies computed from the reference models. In this paper, artificial neural networks (ANNs) are used to estimate analytical redundancy and to classify faults. The Lebenburg-Marquardt algorithm is used to train feed forward ANNs that provide estimates of continuous states and diagnosis results. The simulation result demonstrated that the ANNs can effectively detect and diagnose faults in the highly non-linear and complex HVAC systems.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.1
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• pp.75-81
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• 2008

The induction motor is given a great deal of weight on the industry generally. Therefore, the fault of the induction motor may cause the fault to effect another parts or another faults in the whole system as well as in itself. These are accompany with a lose of the reliability in the industrial system. Accordingly to prevent these situation, the scholars have studies the fault diagnosis of the induction motor. In this paper, we proposed the diagnosis system of the induction motor. The method of diagnosis in proposed system is extracted the feature of the current signal by the wavelet transform. These extracted feature is used the automatic discrimination system by the neural network. We experiment the automatic discrimination system using the three faults imitation that often generated in the induction motor. The proposed system have achieved high reliable result with a simple devices about the three faults.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.41 no.5
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• pp.51-60
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• 2004

Due to the improvements in circuit design and manufacturing technique, the complexity of a circuit is growing. Since the complexity of a circuit causes high frequency of faults, it is very important to locate faults for improvement of yield and reduction of production cost. But unfortunately it takes a long time to find sites of defects by e-beam proving if the physical level. A fault diagnosis algorithm in the Sate level has meaning to reduce diagnosis time by limiting fault sites. In this paper, we propose an efficient fault diagnosis algorithm in the logical level. Our method is hybrid fault diagnosis algorithm using a new fault dictionary and additional fault simulation which minimizes memory consumption and simulation time.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.7
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• pp.103-109
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• 2016

Corrective control for asynchronous sequential machines is a novel automatic control theory that compensates illegal behavior or adverse effects of faults in the operation of existent asynchronous machines. In this paper, we propose a scheme of diagnosing and tolerating faults occurring to input channels of corrective control systems. The corrective controller can detect faults occurring in the input channel to the controlled machine, whereas those faults happening in the external input channel cannot be detected. The proposed scheme involves an outer operator which, upon receiving the state feedback, diagnoses a fault and sends an appropriate command signal to the controller for tolerating faults in the external input channel.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.238-245
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• 2016

Inverters are considered the basic building blocks of industrial electrical drive systems that are widely used for various applications; however, the failure of electronic switches mainly affects the constancy of these inverters. For safe and reliable operation of an electrical drive system, faults in power electronic switches must be detected by an efficient system that is capable of identifying the type of faults. In this paper, an open switch fault identification technique for a three-phase inverter is presented. Single, double, and triple switching faults can be diagnosed using this method. The detection mechanism is based on stator current analysis. Discrete wavelet transform (DWT) using Daubechies is performed on the Clarke transformed (-) stator current and features are extracted from the wavelets. An artificial neural network is then used for the detection and identification of faults. To prove the feasibility of this method, a Simulink model of the DWT-based feature extraction scheme using a neural network for the proposed fault detection system in a three-phase inverter with an induction motor is briefly discussed with simulation results. The simulation results show that the designed system can detect faults quite efficiently, with the ability to differentiate between single and multiple switching faults.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.272-275
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• 1992

A novel fault diagnosis method based on likelihood decomposition is proposed for linear stochastic systems described by autoregressive (AR) model. Assuming that at some time instant .tau. the fault of one of the following two types is occurs: innovation fault (actuator fault); and observation fault (sensor fault), the log-likelihood function is decomposed into two components based on the observations before and after .tau., respectively, Then, the type of the fault is determined by comparing the log-likelihoods corresponding two types of faults. Numerical examples demonstrate the usefulness of the proposed diagnosis method.

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.1116-1121
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• 2013

Mechanical imbalances are common mechanical faults in induction motors. Vibration monitoring techniques have been widely used for the diagnosis of mechanical faults in induction motors, but electrical detection methods have been preferred in recent years. For many years, researchers have concentrated on the Motor Current Signature Analysis (MCSA). This paper examines the effect of mechanical imbalances to induction machine electrical parameters. Instantaneous Power Signature Analysis (IPSA) technique used to detect these faults. In the paper, a full analysis of the proposed technique is presented, and experimental results for healthy and faulty motors have been shown and discussed.

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

• Proceedings of the KIPE Conference
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• 2003.11a
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• pp.11-16
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• 2003

The 3-level converter/inverter system is very efficient in the ac motor drives of high voltage and high power application. This paper proposed a simple method to diagnose faults using change of current vector pattern in space vector diagram when the faults occurrence in the 3-level inverter and a control method that can protect system from unbalance of the neutral point voltage according to faults. The validity of the proposed method is demonstrated by the simulation results.

• The Transactions of the Korean Institute of Power Electronics
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• v.14 no.3
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• pp.197-203
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• 2009

This paper presents a novel method to diagnose magnetization faults produced during magnetization process using stator coils of a brushless dc motor with concentrated windings. It is demonstrated that the stator coil magnetization faults can cause a drop of energy efficiency of the brushless dc motor through computer simulations using a magnetization fault model and efficiency test using compressors. The proposed method diagnoses the stator coil magnetization faults by using an inverter during the brushless dc motor driving test after the magnetization process is completed. An experimental study on the brushless dc motor for compressor shows that the stator coil magnetization faults can be detected with high sensitivity.