• Proceedings of the KIEE Conference
• /
• 2015.07a
• /
• pp.81-82
• /
• 2015

Recently, fault detection and diagnosis techniques have been significantly considered to reduce possible economic loss due to faulty in photovoltaic power systems. This paper presents a new fault location diagnosis method for photovoltaic power systems. The proposed algorithm compares the output voltage generated from a photovoltaic array to the outputs of its neighboring arrays. This concept is realized by obtaining error voltages among all arrays, which are simply defined by deviation between its neighboring arrays. We accomplish a real-time experiment to demonstrate reliability of the proposed fault location diagnosis by using a 60W photovoltaic power system test-bed.

• Proceedings of the IEEK Conference
• /
• summer
• /
• pp.371-375
• /
• 2004

Fault diagnosis is one of the most studied problems in process engineering. Recently, great research interest has been devoted to approaches that use classification methods to detect faults. This paper presents an application of a newly developed classification method - support vector machines - for fault diagnosis in an industrial case. A real set of operation data of a motor pump was used to train and test the support vector machines. The experiment results show that the support vector machines give higher correct detection rate of faults in comparison to rule-based diagnostics. In addition, the studied method can work with fewer training instances, what is important for online diagnostics.

• International Journal of Air-Conditioning and Refrigeration
• /
• v.16 no.4
• /
• pp.137-144
• /
• 2008

This study deals with on-line fault detection and diagnosis(FDD) for heat exchangers of a variable speed refrigeration system(VSRS) based on current information. The current residual which is the difference between real detected current from current sensors and estimated current from no fault model was utilized to diagnose faults of the heat exchangers. Comparing to the conventional FDD of constant refrigeration system based on temperature and pressure information, the suggested FDD method shows better robustness to the VSRS which has a feedback control loop. Moreover the suggested method can be expected more precise and faster diagnosis of faults about heat exchangers. Throughout some experiments, the validity of the method was verified.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.34 no.4
• /
• pp.333-339
• /
• 2010

Identification of a steady state is the first step in developing a fault detection and diagnosis (FDD) system of a heat pump. 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 representative measurements were selected as key features for steady-state detection. The optimized moving-window size and the feature thresholds were decided on the basis of a startup-transient test and no-fault steady-state test. Performance of the steady-state detector was verified during an indoor load-change test. In this study, a general methodology for designing a moving-window steady-state detector for applications involving vapor compression has been established.

• Journal of the Korean Institute of Gas
• /
• v.28 no.2
• /
• pp.38-46
• /
• 2024

The city gas pipeline network is characterized by being widely distributed and hierarchically connected in a complex manner over a wide area. In order to monitor the status of the widely distributed network pressures with high precision, Multi-block PCA(MBPCA) is recommended. However, while MBPCA has excellent performance in identifying faulty sensors as the number of sensors increases, the fault detection performance deteriorates, and also there is a problem that the model needs to be updated entirely even if minor changes occur. In this study, we developed fault detectability index and fault identificability index to determine the effectiveness of MBPCA application block by block. Based on these indices, we distinguished MBPCA and PCA blocks and developed a fault detection and diagnostic system for the city gas pipeline network of Haean Energy Co., Ltd., and were able to solve the problems that arise when there are many sensors.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.125.4-125
• /
• 2001

The problem of recongurable ight control is investigated, focusing on model reference adaptive control(MRAC) through imprecise fault diagnosis. The method integrates the fault detection and isolation(FDI) scheme with the model reference adaptive control, and can be implemented on-line and in real-time. The algorithm can cope with the fast varying parameters. The Simulation results demonstrate the ability of reconguration to maintain the stability and acceptable performance after a failure.

• Proceedings of the KIEE Conference
• /
• 1998.07b
• /
• pp.420-422
• /
• 1998

All Fault Detection and Diagnosis(FDD) methods utilize classification techniques. The objective of this study was to demonstrate the application of classification techniques to the problem of diagnosing faults in data generated by a variable-air-volume(VAV) air-handling unit(AHU) simulation model and to describe the characteristics of the techniques considered. Artificial neural network classifier and fuzzy clustering classifier were considered for fault diagnostics.

• Journal of the Institute of Convergence Signal Processing
• /
• v.6 no.3
• /
• pp.143-147
• /
• 2005

Monitoring and diagnosis of the operating machines are very important for safety operation and maintenance in the industrial fields. These machines are most rotating machines and the diagnosis of the machines has been researched for long time. We can easily see the faulted signal of the rotating machines from the changes of the signals in frequency. The Linear Predictive Coding(LPC) is introduced for signal analysis in frequency domain. In this paper, we propose fault detection and diagnosis method using the Linear Predictive Coding(LPC) and residual signal energy. We applied our method to the induction motors depending on various status of faulted condition and could obtain good results.

• International Journal of Control, Automation, and Systems
• /
• v.1 no.3
• /
• pp.308-314
• /
• 2003

This paper presents a serial communication based fault diagnosis scheme for a brushless DC (BLDC) motor using parameter estimation and Bayes classifier. The presented scheme consists of a smart network board, and a fault detection and isolation (FDI) master. The smart network board is installed near the BLDC motor drive system to acquire motor data and transmit motor data to the FDI-master via serial communication channel. The FDI-master estimates BLDC motor resistance to detect symptom of faults, and assign symptom to fault type using Bayes classifier. In this scheme, since communication time delay has a serious effect on performance, periodic and fixed communication protocol is designed. Hence, the delay time is priory known. By experiment result, presented scheme was verified.

• Proceedings of the Korean Society of Marine Engineers Conference
• /
• 2002.05a
• /
• pp.245-250
• /
• 2002

The operational data of diesel generator engine is two kind of discrete signal and analog signal. We can find the fault information from analog data measured for every sampling time if it is invested the changing rate or direction of data. This paper propose the Malfunction Diagnosis Engine(MDE) using the commercial data mining tool and show the data Process and fault finding method with the data collected from generator engine of the ship.