• 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 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.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.39 no.2
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• pp.199-206
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• 2015

In this study, a new diagnosis method which can predict the working states of a pipe or its element in realtime is proposed by using an artificial neural network. The displacement data of an inspection element of a piping system are obtained by the use of PIV (particle image velocimetry), and are used for teaching a neural network. The measurement system consists of a camera, a light source and a host computer in which the artificial neural network is installed. In order to validate the constructed monitoring system, performance test was attempted for two kinds of mobile phone of which vibration modes are known. Three values of acceleration (minimum, maximum, mean) were tested for teaching the neural network. It was verified that mean values were appropriate to be used for monitoring data. The constructed diagnosis system could monitor the operation condition of a gas pipe.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.265-266
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• 2006

In this paper, the ECG monitoring system on mobile platform was proposed, which is very useful to gather, storage and diagnose ECG signal. The existing ECG monitoring system is for indoor environment but this system is for outdoor environment, especially for automobile system. The developed system consisted of data logger using microprocessor and data server fur diagnosis ECG signal. We develop the data acquisition system hardware and data monitoring system for ECG signal.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.9
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• pp.413-422
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• 2002

In a power plant, disturbance detection and diagnosis are massive and complex problems. Once a disturbance occurs, it can be either persistent, self cleared, cleared by the automatic controllers or propagated into another disturbance until it subsides in a new equilibrium or a stable state. In addition to the Physical complexity of the power plant structure itself, these dynamic behaviors of the disturbances further complicate the fault monitoring and diagnosis tasks. A data structure called a disturbance interrelation analysis graph(DIAG) is proposed in this paper, trying to capture, organize and better utilize the vast and interrelated knowledge required for power plant disturbance detection and diagnosis. The DIAG is a multi-layer directed AND/OR graph composed of 4 layers. Each layer includes vertices that represent components, disturbances, conditions and sensors respectively With the implementation of the DIAG, disturbances and their relationships can be conveniently represented and traced with modularized operations. All the cascaded disturbances following an initial triggering disturbance can be diagnosed in the context of that initial disturbance instead of diagnosing each of them as an individual disturbance. DIAG is applied to a typical cooling water system of a thermal power plant and its effectiveness is also demonstrated.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.645-648
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• 2003

Induction motors are a critical component of industrial processes. Sudden failures of such machines can cause the heavy economical losses and the deterioration of system reliability. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and the diagnosis of system are considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method is used for induction motor fault diagnosis. This method analyses the motor's supply current. since this diagnoses faults of the motor. The diagnostic algorithm is based on the principal component analysis(PCA), and the diagnosis system is programmed by using LabVIEW and MATLAB.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.5
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• pp.110-123
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• 1994

The development of automatic production systems have required inteligent diagnostic and monitoring function to repair system failure and reduce production loss by the failure. In order to perform accurate functions of intelligent system, inferencing about total system failure and fault analysis due to each mechanical component failures are required. Also the solution about repair and maintenance can be suggested from these analysis results. As an essential component of mechanical system, a bearing system is investigated to define the failure behavior. The bearing failure is caused by lubricant system failure, metallurgical defficiency, mechanical condition(vibration, overloading, misalignment) and environmental effect. This study described roller bearing fault train due to stress variation and metallurgical defficiency from lubricant failure by using FTA.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.23 no.4
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• pp.333-337
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• 2010

This paper introduces an overall system design of a real-time monitoring system that monitors condition of urban railway AC/DC transformers, disconnecting switches, circuit breakers, regulators, and GIS (gas insulated switchgear). For effective diagnosis of urban railway facilities we developed optimized sensors and application technology. The proposed system is combined with wireless technology, implementing remote real-time monitoring of urban railway facilities. The derived real-time monitoring system for power facilities in urban railway substations will be applied not only to diagnose low voltage DC (direct current) railways but also to diagnose design of small power facilities of skyscrapers and apartments.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.132-137
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• 2004

On-Board Diagnostic(OBD) systems are in most cars and light trucks on the load today. During the 1970's and early 1980's manufacturers started using electronic means to control engine functions and diagnose engine problems. The CARB's diagnostic requirements to meet EPA emission standards have been designated as OBD with a goal of monitoring all of the emissions-related components, as well as the chassis, body, accessory devices and the diagnostic control network of the vehicle for proper operation. In this paper, we present a remote measurement system for the wireless monitoring of diagnosis signal and sensors output signals of ECU adopted KWP2000, united the OBD communication protocol, on OBD-compliant vehicle using the wirless communication technique of Bluetooth. In order to measure the ECU signals, the interface circuit is designed to communicate ECU and designed terminal wirelessly according to the ISO, SAE regulation of communication protocol standard. A microprocessor S3C3410X is used for communicating ECU signals. The embedded system's software is programmed to measure the ECU signals using the ARM compiler and ANCI C based on MicroC/OS kernel to communicate between bluetooth modules using bluetooth stack. The diagnostic system is developed using Visual C++ MFC and protocol stack of bluetooth for Windows environment. The self-diagnosis and sensor output signals of ECU is able to monitor using PC with bluetooth board connected in serial port of PC. The algorithms for measuring the ECU sensor output and self-diagnostic signals are verified to monitor ECU state. At the same time, the information to fix the vehicle's problem can be shown on the developed monitoring software. The possibility for remote measurement of self-diagnosis and sensor signals of ECU adopted KWP2000 in embedded system verified through the developed systems and algorithms.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2222-2224
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• 2002

Induction motors are critical components of many industrial machines and are frequently integrated in commercial equipment. The heavy economical losses and the deterioration of system reliability might be caused by the failure of induction motors in industrial field. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and diagnosis of system is considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method are used for induction motor fault diagnosis. This method analyzes the motors supply current. since this diagnoses faults of the motor. The diagnostic algorithm is based on the artificial neural network, and the diagnosis system is programmed by using LabVIEW and MATLAB.