• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.17 no.5
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• pp.444-451
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• 2005

The fault detection and diagnosis technology may be applied in order to decrease the energy consumption and the maintenance cost of an air-conditioning system. In this study, partial faults for fans, coils, dampers, and sensors of a constant volume air handling unit were considered. A fuzzy algorithm was developed to detect and diagnose these faults. Diagnostic results by the fuzzy algorithm were compared with those by the model reference algorithm. The fuzzy algorithm showed better results in diagnostic accuracies.

• Journal of IKEEE
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• v.7 no.1 s.12
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• pp.107-118
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• 2003

In this paper, dynamic control model is formulated by considering the geometrical structure of the deaerator storage tank in nuclear power plant and input-output flow rate at steady state, and we describe fault detection and diagnosis (FDD) scheme based on the adaptive estimator. The performance and effectiveness of the proposed FDD scheme are evaluated by applying real operating data obtained from the YOUNGKWANG 3 & 4 FSAR.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.14 no.7
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• pp.552-559
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• 2002

The fault detection and diagnosis (FDD) technology may be applied in order to decrease the energy consumption and the maintenance cost of the air conditioning system. In this study, rule bases and curve fitting models were used to detect faults in an air handling unit. Gradually progressed faults, such as the fan speed degradation, the coil water leakage, the humidifier nozzle clogging, the sensor degradation and the damper stoppage, were applied to the developed FBD system. Simulation results show good detections and diagnoses of these faults. Therefore, this method may be effectively used for the fault detection and diagnosis of the air handling unit.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.2
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• pp.144-152
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• 2011

This paper presents a new scheme for fault detection and isolation in the satellite system. The purpose of this paper is to develop a fault detection, isolation and diagnosis algorithm based on the bank of interacting multiple model (IMM) filter for both total and partial faults in a satellite attitude control system (ACS). In this paper, IMM are utilized for detection and diagnosis of anticipated actuator faults in a satellite ACS. Other fault detection, isolation (FDI) schemes using conventional IMM are compared with the proposed FDI scheme. The FDI procedure is developed in two stages. In the first stage, 11 EKFs actuator fault models are designed to detect wherever actuator faults occur. In the second stage of the FDI scheme, two filters are designed to identify the fault type which is either the total or partial fault. An important feature of the proposed FDI scheme can decrease fault isolation time and figure out not only fault detection and isolation but also fault type identification.

• Proceedings of the SAREK Conference
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• 2008.06a
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• pp.282-287
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• 2008

Identification of steady-state is the first step in developing a fault detection and diagnosis (FDD) system. In a complete FDD system, the steady-state detector will be included as a module in a self-learning algorithm which enables the working system's reference model to "tune" itself to its particular installation. In this study, a steady-state detector of a residential air conditioner based on moving windows was designed. Seven representing measurements were selected as key features for steady-state detection. The optimized moving window size and the feature thresholds was suggested through startup transient test and no-fault steady-state test. Performance of the steady-state detector was verified during indoor load change test. From the research, the general methodology to design a moving window steady-state detector was provided for vapor compression applications.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.691-697
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• 2006

Induction motors are critical components in industrial process. So there are many research in the condition based maintenance, online monitoring system, and fault detection. This paper presents a scheme on the detection and diagnosis of the three-phase squirrel induction motor under unbalanced voltage, broken rotor bar, and a combination of these two faults. Actually one fault happen in operation, it influence other component in motor or cause another faults. Accordingly it is useful to diagnose and detect a combination fault in induction motor as well as each fault. The proposed fault detection and diagnosis algorithm is based on the stator currents from the squirrel induction motor and simulated with the aid of Matlab Simulink.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.10 s.175
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• pp.80-89
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• 2005

In this paper, time-frequency analysis using wavelet packet transform and advanced-MDSA (Multiple Dimensional Spectral Analysis) which based on wavelet packet transform is applied fur fault source identification and diagnosis of early detection of fault non-stationary sound/vibration signals. This method is analyzing the signal in the plane of instantaneous time and instantaneous frequency. The results of ordinary coherence function, which obtained by wavelet packet analysis, showed the possibility of early fault detection by analysis at the instantaneous time. So, by checking the coherence function trend, it is possible to detect which signal contains the major fault signal and to know how much the system is damaged. Finally, It is impossible to monitor the system is damaged or undamaged by using conventional method, because crest factor is almost constant under the range of magnitude of fault signal as its approach to normal signal. However instantaneous coherence function showed that a little change of fault signal is possible to monitor the system condition. And it is possible to predict the maintenance time by condition based maintenance for any stationary or non-stationary signals.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.4
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• pp.443-449
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• 2010

Current testing is an effective method which offers higher fault detection and diagnosis capabilities than voltage testing. Since current testing requires much longer testing time than voltage testing, it is important to note that a fault is untestable if the two nodes have same values at all times. In this paper, we present an object oriented fault detection scheme for various fault models using current testing. Experimental results for ISCAS benchmark circuits show the effectiveness of the proposed method in reducing the number of faults and its usefulness in various fault models.

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

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1765-1772
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• 2015

The integrity of the gas insulated switchgear (GIS) is vital to the safety of an entire power grid. However, there are some limitations on the techniques of detecting and diagnosing partial discharge (PD) induced by insulation defects in GIS. This paper proposes a joint electro-chemical detection method to resolve the problems of incomplete PD data source and also investigates a new unique fault diagnosis method to enhance the reliability of data processing. By employing ultra-high frequency method for online monitoring and the chemical method for detecting SF6 decomposition offline, the acquired data can form a more complete interpretation of PD signals. By utilizing DS evidence theory, the diagnostic results with tests on the four typical defects show the validity of the new fault diagnosis system. With higher accuracy and lower computation cost, the present research provides a promising way to make a more accurate decision in practical application.