• Journal of the Korean Society for Precision Engineering
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• v.14 no.11
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• pp.135-146
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• 1997

This paper describes fault diagnosis method in complex system with hierachical structure similar to human body structure. Complex system is divided into unit, item and component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. Fault diagnosis system can forecast faults in a system and decide from current machine state signal information. Comparing with other diagnosis system for single fault, the developed system deals with multiple fault diagnosis comprising Hierarchical Neural Network(HNN). HNN consists of four level neural network, first level for item fault symptom classification, second level for item fault diagnosis, third level for component symptom classification, forth level for component fault diagnosis. UNIX IPC(Inter Process Communication) is used for implementing HNN wiht multitasking and message transfer between processes in SUN workstation with X-Windows(Motif). We tested HNN at four units, seven items per unit, seven components per item in a complex system. Each one neural newtork operate as a separate process in HNN. The message queue take charge of information exdhange and cooperation between each neural network.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.457-462
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• 1995

This paper describes diagnosis strategy of the Flexible Multiple Fault Diagnosis Module for forecasting faults in system and deciding current machine state form sensor information. Most studydeal with diagnosis control stategy about single fault in a system, this studies deal with multiple fault diagnosis. This strategy is consist of diagnosis control module such as backward tracking expert system shell, various neural network, numerical model to predict machine state and communication module for information exchange and cooperate between each model. This models are used to describe structure, function and behavior of subsystem, complex component and total system. Hierarchical structure is very efficient to represent structural, functional and behavioral knowledge. FT(Fault Tree). ST(Symptom Tree), FCD(Fault Consequence Diagrapy), SGM(State Graph Model) and FFM(Functional Flow Model) are used to represent hierachical structure. In this study, IA(Intelligent Agent) concept is introduced to match FT component and event symbol in diagnosed system and to transfer message between each event process. Proposed diagnosis control module is made of IPC(Inter Process Communication) method under UNIX operating system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.670-675
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• 1994

Yhis paper describes a fault diagnosis simulation of the Real-Time Multiple Fault Dignosis System (RTMFDS) for forcasting faults in a system and deciding current machine state from signal information. Comparing with other diagnosis system for single fault,the system developed deals with multiple fault diagnosis,comprising two main parts. One is a remotesignal generating and transimission terminal and the other is a host system for fault diagnosis. Signal generator generate the random fault signal and the image information, and send this information to host. Host consists of various modules and agents such as Signal Processing Module(SPM) for sinal preprocessing, Performence Monotoring Module(PMM) for subsystem performance monitoring, Trigger Module(TM) for multi-triggering subsystem fault diagnosis, Subsystem Fault Diagnosis Agent(SFDA) for receiving trigger signal, formulating subsystem fault D\ulcornerB and initiating diagnosis, Fault Diagnosis Module(FDM) for simulating component fault with Hierarchical Artificial Neural Network (HANN), numerical models and Hofield network,Result Agent(RA) for receiving simulation result and sending to Treatment solver and Graphic Agent(GA). Each agent represents a separate process in UNIX operating system, information exchange and cooperation between agents was doen by IPC(Inter Process Communication : message queue, semaphore, signal, pipe). Numerical models are used to deseribe structure, function and behavior of total system, subsystems and their components. Hierarchical data structure for diagnosing the fault system is implemented by HANN. Signal generation and transmittion was performed on PC. As a host, SUN workstation with X-Windows(Motif)is used for graphic representation.

• Journal of the Korean Society of Industry Convergence
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• v.7 no.2
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• pp.199-205
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• 2004

This paper describes the fault diagnosis method to order to construct CIM in complex system with hierarchical structure similar to human body structure. Complex system is divided into unit, item and component. For diagnosing this hierarchical complex system, it is necessary to implement a special neural network. Fault diagnosis system can forecast faults in a system and decide from the signal information of current machine state. Comparing with other diagnosis system for a single fault, the developed system deals with multiple fault diagnosis, comprising hierarchical neural network (HNN). HNN consists of four level neural network, i.e. first is fault symptom classification and second fault diagnosis for item, third is symptom classification and forth fault diagnosis for component. UNIX IPC is used for implementing HNN with multitasking and message transfer between processes in SUN workstation with X-Windows (Motif). We tested HNN at four units, seven items per unit, seven components per item in a complex system. Each one neural network represents a separate process in UNIX operating system, information exchanging and cooperating between each neural network was done by message queue.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.5
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• pp.450-457
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• 2004

In this research, the automatic synthesis of knowledge models is proposed. which are the basis of the methods using qualitative models adapted widely in fault diagnosis and hazard evaluation of chemical processes. To provide an easy and fast way to construct accurate causal model of the target process, the Role-Behavior modeling method is developed to represent the knowledge of modularized process units. In this modeling method, Fault-Behavior model and Structure-Role model present the relationship of the internal behaviors and faults in the process units and the relationship between process units respectively. Through the multiple modeling techniques, the knowledge is separated into what is independent of process and dependent on process to provide the extensibility and portability in model building, and possibility in the automatic synthesis. By taking advantage of the Role-Behavior Model, an algorithm is proposed to synthesize the plant-wide causal model, Fault-Causality Graph (FCG) from specific Fault-Behavior models of the each unit process, which are derived from generic Fault-Behavior models and Structure-Role model. To validate the proposed modeling method and algorithm, a system for building FCG model is developed on G2, an expert system development tool. Case study such as CSTR with recycle using the developed system showed that the proposed method and algorithm were remarkably effective in synthesizing the causal knowledge models for diagnosis of chemical processes.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.4
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• pp.331-338
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• 1992

The application of Connectionist expert system using neural network to fault diagnosis of power system is presented and compared with rule-based expert system. Also, the merits of Connectionist model using neural network is presented. In this paper, the neural network for fault diagnosis is hierarchically composed by 3 neural network classes. The whole power system is divided into subsystems, the neural networks (Class II) which take charge of each subsystem and the neural network (Class III) which connects subsystems are composed. Every section of power system is classified into one of the typical sections which can be applied with same diagnosis rules, as line-section, bus-section, transformer-section. For each typical section, only one neural network (Class I) is composed. As the proposed model has hierarchical structure, the great reduction of learning structure is achieved. With parallel distributed processing, we show the possibility of on-line fault diagnosis.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.62-68
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• 1999

FCM(Fuzzy Cognitive Map) is proposed for representing causal reasoning. Its structure allows systematic causal reasoning through a forward inference. Authors have already proposed a diagnostic system based on FCM to utilized to identify the true origin of fault by on-line pattern diagnosis. In FCM based fault diagnosis, Temporal Associative Memories (TAM) recall of FCM is utilized to identify the true origin of fault by on-line pattern match where predicted pattern sequences obtained from TAM recall of fault FCM models are compared with actually observed ones. In engineering processes, the propagation delays are induced by the dynamics of processes and may vary with variables involved. However, disregarding such propagation delays in FCM-based fault diagnosis may lead to erroneous diagnostic results. To solve the problem, a concept of FTCM(Fuzzy Time Cognitive Map) is introduced into FCM-based fault diagnosis in this work. Expecially, translation method of FTCM makes it possible to diagnose the fault for some discrete time. Simulation studies through two-tank system is carried out to verify the effectiveness of the proposed diagnostic scheme.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.2
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• pp.35-44
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• 1998

This paper describes multiple fault diagnosis method in complex system with hierarchical structure. Complex system is divided into subsystem, item and component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. We introduced Modular Artificial Neural Network(MANN) for this purpose. MANN consists of four level neural network, first level for symptom classification, second level for item fault diagnosis, third level for component symptom classification, forth level for component fault diagnosis. Each network is multi layer perceptron with 7 inputs, 30 hidden node and 7 outputs trained by backpropagation. UNIX IPC(Inter Process Communication) is used for implementing MANN with multitasking and message transfer between processes in SUN workstation. We tested MANN in reactor system.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.10
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• pp.89-97
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• 2003

The faults diagnosis of machine tool, which is controlled by CNC(Computer Numerical Control) and PLC(Programmable Logic Controller), is generally based on ladder diagram of PLC because sequential controls for CNC and servo motor are mostly processed in PLC. However, when fault is occurred, a searching of logical relationship for fault reasons is required a lot of diagnosis experiences and times because PLC program has step structure. In this paper, FDS(Fault Diagnosis System) is developed and implemented to machine tool with open architecture controller in order to find the reason of fault fast and correctly. The diagnosed reasons for fault are remote serviced on web through developed RSS(Remote Service System). The operationability and usefulness of developed system are evaluated on specially manufactured machine tool with open architecture CNC. The results of this research can be the model of remote monitoring and fault diagnosis system of machine tool with open architecture CNC.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.7-10
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• 2002

The diagnosis of faults of machine tool, which is controlled by CNC and PLC, is generally based on ladder diagram of PLC. Because sequential controls for CNC and servo motor are mostly processed in PLC. However, when fault is occurred, a searching for logical relation to fault reasons is required a lot of fault experiences and times, because PLC program has step structure. In this paper, FDS(Fault Diagnosis System) is developed and implemented to machine tool with open architecture controller in order to find the reason of fault lastly and correctly. The diagnosed reasons for fault are tele-serviced on web through developed RSS(Remote Service System). The operationability and usefulness of developed system are evaluated on specially manufactured machine tool with open architecture CNC. The results of this research can be the model of remote monitoring and fault diagnosis system of machine tool with open architecture CNC.