• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.13 no.9
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• pp.703-712
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• 2003

This paper suggests a fuzzy expert system for fault diagnosis of rotating machinery, based on modulated databases. In the proposed system, alarm and trip levels are set based on ISO, considering operating condition, machinery type and maintenance history. Input signals for diagnosis, such as sub-and super-harmonic components of vibration and mean value, are normalized from 0 to 1 under the threshold level and otherwise equal to one so that chronic faults slightly below the threshold level can be monitored. The database for diagnosis consists of two modules: the well-known Sohre's chart module and if-then type rules. The Sohre's chart is utilized for the most common problems of high-speed turbomachinery, while the rule-based module, which was collected from many papers and reports, is for diagnosing peculiar faults according to the type of machinery. To infer the results from two modules, a fuzzy operation of Yager sum was adopted. Using a simulator constructed in laboratory, experimental verification was performed for the cases of unbalance and resonance which were intended. The experimental results show that the proposed fuzzy expert system has feasibility in practical diagnosis of rotating machinery.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.303-312
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• 2003

This paper suggests a fuzzy expert system for fault diagnosis of rotating machinery, based on modulated databases. In the proposed system, alarm and trip levels are set based on ISO, considering operating condition, machinery type and maintenance history. Input signals for diagnosis, such as sub- and super-harmonic components of vibration and mean value, are normalized from 0 to 1 under the threshold level and otherwise equal to one so that chronic faults slightly below the threshold level can be monitored. The database for diagnosis consists of two modules: the well-known Sohre's chart module and if-then type rules. The Sohre's chart is utilized for the most common problems of high-speed turbomachinery, while the rule-based module, which was collected from many papers and reports, is for diagnosing peculiar faults according to the type of machinery. To infer the results from two modules, a fuzzy operation of Yager sum was adopted. Using a simulator constructed in laboratory, experimental verification was performed for the cases of resonance and housing looseness which were intended. The experimental results show that the proposed fuzzy expert system has feasibility in practical diagnosis of rotating machinery.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.1
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• pp.42-48
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• 2003

In general many engineers can diagnose the fault condition using the abnormal ones among data monitored from a diesel engine, but they don't need the system modelling or identification for the work. They check the abnormal data and the relationship and then catch the fault condition of the engine. This paper proposes the construction of a fault diagnosis engine through malfunction data gained from the data fault detection system of neural networks for diesel generator engine, and the rule inference method to induce the rule for fuzzy inference from the malfunction data of diesel engine like a site engineer with a fuzzy system. The proposed fault diagnosis system is constructed in the sense of the Malfunction Diagnosis Engine(MDE) and Hierarchy of Malfunction Hypotheses(HMH). The system is concerned with the rule reduction method of knowledge base for related data among the various interactive data.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.24-26
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• 2005

At present, KS-1000 which is one of a commercial measurement instrument for motor fault diagnosis has been used in industrial field. The measurement system of KS-1000 is composed of three part : harmonic acquisition, signal processing by KS-1000 algorithm, diagnosis for motor fault. First of all, voltage signal taken from harmonic sensor is analysed for frequency by KS-1000 algorithm. Then, based on the result values of analysis skilled expert makes a judgment about whether motor system is the abnormality or degradation state. But the expert system such a motor fault diagnosis is very difficult to bring the expectable results by mathematical modeling due to the complexity of judgment process. In this reason, we propose an automation system using fuzzy model based on genetic algorithm(GA) that builded a qualitative model of a system without priori knowledge about a system provided numerical input output data.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.5
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• pp.66-75
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• 1997

A fault diagnosis system for a variable speed rotating shaft probably demands a huge database, which makes it diffcult to be realized. This stuydy presents an effective method of fault diagnosis for variable speed rotating shafts. The proposed method is based upon a fuzzy reasoning and it includes a stepwize strategy to reduce the size of database in a diagnosis system. A computer program is developed to show the procedure of the diagnosis, and four cases of faults are applied to the program to illustarate the effectiveness of the proposed method. The propsed method is found to be useful in reducing the size of database from observation of the data files of the dianosis system. The case studies show that the proposed method can be useful for the diagnosis of variable speed rotating shafts.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.1
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• pp.45-53
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• 1997

This paper presents hybrid expert system for diagnosis of electric power transformer faults. The expert system diagnose and detect faults in oil-filled power transformers based on dissolved gas analysis. As the preprocessing stage, fuzzy information theory is used to manage the uncertainty in transformer fault diagnosis using dissolved gas analysis. The Kohonen neural network takes the interim results by applying fuzzy informations theory as inputs, and performs the transformer fault diagnosis. The Proposed system tested gas records of power transformers from Korea Electric Power Corporation to verify the diagnosis performance of transformer faults.

• The KIPS Transactions:PartB
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• v.11B no.7 s.96
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• pp.855-860
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• 2004

This paper describes knowledge base optimization of an intelligent diagnosis system based on fuzzy relational products(IDS-DAAP) for the diseases with acute abdominal Pain. The knowledge base of IDS-DAAP is composed of the fuzzy rules and the fuzzy membership functions. The author here proposes an advanced intelligent diagnosis system (A-lDS-DAAP) in which the fuzzy rule generation algorithm is applied. Comparing with previous IDS-DAAP and IDS-DAAP-NN, a modified approach with A-IDS-DAAP shows that it improves the diagnosis rate and reduces the time to diagnose.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.912-921
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• 2015

In this paper, we introduce fuzzy inference-based real-time deterioration diagnosis system with the aid of infrared thermal imaging camera. In the proposed system, the infrared thermal imaging camera monitors diagnostic field in real time and then checks state of deterioration at the same time. Temperature and variation of temperature obtained from the infrared thermal imaging camera variation are used as input variables. In addition to perform more efficient diagnosis, fuzzy inference algorithm is applied to the proposed system, and fuzzy rule is defined by If-then form and is expressed as lookup-table. While triangular membership function is used to estimate fuzzy set of input variables, that of output variable has singleton membership function. At last, state of deterioration in the present is determined based on output obtained through defuzzification. Experimental data acquired from deterioration generator and electric machinery are used in order to evaluate performance of the proposed system. And simulator is realized in order to confirm real-time state of diagnostic field

• Journal of Advanced Marine Engineering and Technology
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• v.18 no.3
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• pp.85-93
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• 1994

A new diagnosis method is developed in this paper, in which the fuzzy set theory is introduced to diagnose the defects of rolling element bearings. The selection of membership function and the fuzzy operation model are discussed in detail here. The system is successfully used for various defects diagnosis of rolling element bearings.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.6
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• pp.457-464
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• 2004

Hard Disk Drive (HDD) test system is an equipment for the final test of HDD product by iterative read/write/seek test. This paper proposes an expert system for the fault diagnosis of HDD test systems. The purposed expert system is composed with two cascade inference, fuzzy logic and conventional binary logic. The fuzzy logic determines the possibility of the system fault using the test history data, then, the binary logic inferences the fault location of the test system. The proposed expert system is tested in SAMSUNG HDD production line, KUMI, KOREA, and shows satisfactory results.