• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.11
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• pp.1111-1117
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• 2011

This paper proposed a fault detection and diagnosis (FDD) algorithm using nonlinear regression models, focusing especially on heat exchanger faults. This research concerned four working modes: those with no fault, evaporator fault, condenser fault, and evaporator and condenser faults. This research used no fault mode data to create an FDD algorithm. Using the no fault mode data, correlation functions for predicting the degree of superheat or subcool of heat exchangers (an evaporator and a condenser) were derived. Each correlation function has five inputs and one output. Based on these correlation functions, it is possible to predict the degree of superheat or subcool of each heat exchanger under various working conditions. The FDD algorithm was developed by comparing the predicted value and the simulation value. The FDD algorithm works well in all four working modes.

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

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.12
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• pp.1351-1358
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• 2011

To approximate the threshold of the fault detection and diagnosis (FDD) system, validation of the measurements is mandatory. Naturally, the system shows uncertainties due to measuring sensors - mostly thermocouples or RTDs - and due to repeatability. The uncertainty of a thermocouple comes from natural variation or a drift of the thermocouple measurement. Considering the natural variation behaves like zero-mean white noise, its natural variation can be characterized closely by the steady-state standard deviation. However, residuals between measurements and no-fault references in FDD systems show a statistical distribution with various uncertainties. In this paper, steady-state variations of measurement residuals were investigated by utilizing built-in temperature sensors in a heat pump for the model development and the final application.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.29 no.11 s.242
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• pp.1220-1228
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• 2005

Fault detection and diagnostic (FDD) systems have the potential to reduce equipment downtime, service costs, and utility costs. In this study, model based algorithm and fuzzy algorithm were used to detect and diagnose various fault at System multi-air conditioner. various fault include the Refrigerant Low charging, Fouling of Indoor Heat Exchanger, Fouling of Outdoor Heat Exchanger A experimental verification was conducted in the 6HP System multi-air conditioner on an 8-floor building. Test results showed diagnosis result about 78 $\~$ 90$\%$ for given faults. This Study lays the foundation fur future work on develope the real-time fault detection and diagnosis system for the System multi-air conditioner.

• The Transactions of the Korean Institute of Power Electronics
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• v.13 no.6
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• pp.430-438
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• 2008

Recently, permanent magnet synchronous motor are applied to various applications such as electric vehicle, aerospace, medical service and military applications due to several outstanding characteristics. Because of the importance of high reliable operation in these areas, many research related to the fault detection and diagnosis of inverter system are conducted. In this paper, new simple fault detection method of voltage source inverter for permanent magnet synchronous motor is proposed. The feasibility of the proposed method are improved by simulation and experiment. By the simulation and experiments, rapid detection characteristic of the proposed method has been proved without any additional voltage sensor.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.16 no.9
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• pp.872-880
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• 2004

The fault detection and diagnosis technology may be applied in order to decrease the energy consumption and the maintenance cost of the air-conditioning system. In this study, an air handling unit fault test apparatus was built and fault diagnosis algorithms were applied to diagnose various faults of an air handling unit. Test results showed the good diagnosis for applied faults. Therefore, these algorithms may be effectively used to develope the real time fault detection and diagnosis system for the air handling unit.

• Proceedings of the KSME Conference
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• 2008.11b
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• pp.2070-2075
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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.

• 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.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.17 no.1
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• pp.47-55
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• 2005

An experimental study has been peformed to develop a scheme for fault detection and diagnosis(FDD) in a vapor compression refrigeration system. This study is to analyze fault effect on the system performance and to find efficient diagnosis rules for easy determination of abnormal system operation. The refrigeration system was operated with a variable speed compressor to modulate cooling capacity. The FDD system was designed to consider transient load conditions. Four major faults were considered, and each fault was detected over wide operating load range by separating the system response to the load change. Rule-based method was used to diagnose and classify the system faults. From the experimental results, COP degradation due to the faults in a variable speed system is severer than that in a constant speed system. The method developed in this study can be used in the fault detection of refrigeration systems with a variable speed compressor.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.10
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• pp.983-990
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• 2008

This paper presents the fault detection and diagnosis(FDD) algorithm to enhance reliability of a longitudinal controller for an autonomous All-Terrain Vehicle(ATV). The FDD is designed to monitor and identify faults which may occur in distributed hardware used for longitudinal control, e.g., DSPs, CAN, sensors, and actuators. The proposed FDD is an integrated approach of decentralized and centralized FDD. While the former is processed in a DSP and suitable to detect faults in a single hardware, it is sensitive to noise and disturbance. On the other hand, the latter is performed via communication and it detects and diagnoses faults through analyzing concurrent performances of multiple hardware modules, but it is limited to isolate faults specifically in terms of components in the single hardware. To compensate for disadvantages of each FDD approach, two layered structure including both decentralized and centralized FDD is proposed and it allows us to make more robust fault detection and more specific fault isolation. The effectiveness of the proposed method will be validated experimentally.