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

• Information Systems Review
• /
• v.12 no.3
• /
• pp.1-17
• /
• 2010

IT Systems based on Global Single Instance (GSI) can manage a corporation's internal information, resources and assets effectively and raise business efficiency through consolidation of their business process and productivity. But, It has also dangerous factor that IT system fault failure can cause a state of paralysis of a business itself, followed by huge loss of money. Many of studies have been conducted about fault-tolerance based on using redundant component. The concept of fault tolerance is rather simple but, designing and adopting fault-tolerance system is not easy due to uncertainty of a type and frequency of faults. So, Operational fault management that working after developed IT system is important more and more along with technical fault management. This study proposes the fault management process that including a pre-estimation method using TRM (Technical Reference Model) check point and event rule engine. And also proposes a effect of fault-free process through built fault management system to representative company of Hi-tech industry. After adopting fault-free process, a number of failure decreased by 46%, a failure time decreased by 56% and the Opportunity loss costs decreased by 77%.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.19 no.7
• /
• pp.726-735
• /
• 2009

In this paper, we propose an approach to signal model-based fault detection and diagnosis system for induction motors. The current fault detection techniques used in the industry are limit checking techniques, which are simple but cannot predict the types of faults and the initiation of the faults. The system consists of two consecutive processes: fault detection process and fault diagnosis process. In the fault detection process, the system extracts the significant features from sound signals using combination of variance, cross-correlation and wavelet. Consequently, the pattern classification technique is applied to the fault diagnosis process to recognize the system faults based on faulty symptoms. The sounds generated from different kinds of typical motor's faults such as motor unbalance, bearing misalignment and bearing loose are examined. We propose two approaches for fault detection and diagnosis system that are waveletand-variance-based and wavelet-and-crosscorrelation-based approaches. The results of our experiment show more than 95 and 78 percent accuracy for fault classification, respectively.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10a
• /
• pp.283-286
• /
• 1996

This study suggests a new methodology for the fault diagnosis based on the signed digraph in developing the fault diagnosis system of a boiler plant. The suggested methodology uses the new model, fault-effect tree. The SDG has the advantage, which is simple and graphical to represent the causal relationship between process variables, and therefore is easy to understand. However, it cannot handle the broken path cases arisen from data uncertainty as it assumes consistent path. The FET is based on the SDG to utilize the advantages of the SDG, and also covers the above problem. The proposed FET model is constructed by clustering of measured variables, decomposing knowledge base and searching the fault propagation path from the possible faults. The search is performed automatically. The fault diagnosis system for a boiler plant, ENDS was constructed using the expert system shell G2 and the advantages of the presented method were confirmed through case studies.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.61 no.12
• /
• pp.1964-1969
• /
• 2012

This paper presents to verify the selective grounding fault protective relaying technique for the ungrounded DC traction power supply system. This system selectively blocks fault section when grounding fault occurred. In order to perform this verification, field test facilities have been installed on Oesam substation and Worldcup-Stadium substation, and field test process has been suggested. Also, selective grounding fault protective relaying components and rail voltage reduction device have been tested with the various trial examinations. In order to compare and evaluate performance of the selective grounding fault protective relaying function, field test system was modeled and the system fault simulation results were compared and evaluated with the field test result. Performance of selective grounding fault protective relaying function was evaluated with the above-mentioned process, and the fact that the system recognizes fault section irrespective of insulation between rail and ground and fault resistance from grounding fault.

• The KIPS Transactions:PartA
• /
• v.11A no.5
• /
• pp.313-318
• /
• 2004

In this paper, we suggest a checkpoint and recovery facility for the fault-tolerable process which is expected to be executed for a long time. The basic concept of the suggested facility is to allow the process to be executed continuously, when the process was stopped due to a System fault, by storing the execution status of the process periodically and recovering the execution status prior to the fault was occurred. In the suggested facility, it does not need to modify the source code for the fault-tolerable process. It was designed for the user to specify directly the file name and the checkpoint frequency, and two system calls(save, recover) were added. finally, it was implemented on the Linux environment(kernel 2.4.18) for checking the feasibility.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.14 no.1
• /
• pp.22-28
• /
• 2021

Multivariate processes, such as chemical and mechanical process, power plants are operated in a state where several facilities are complexly connected, the fault of a particular system can also have fatal consequences for the entire process. In addition, since process data is measured in an unstable environment, outlier is likely to be include in the data. Therefore, monitoring technology is essential, which can remove outlier from measured data and detect failures in advance. In this paper, data obtained from dynamic and multivariate process models was used to detect fault in various type of processes. The dynamic process is a simulation of a process with autoregressive property, and the multivariate process is a model that describes a situation when a specific sensor fault. Mahalanobis distance was used to remove outlier contained in the data generated by dynamic process model and multivariate process model, and fault detection was performed using ICA. For comparison, we compared performance with and a conventional single ICA method. The proposed fault detection method improves performance by 0.84%p for bias data and 6.82%p for drift data in the dynamic process. In the case of the multivariate process, the performance was improves by 3.78%p, therefore, the proposed method showed better fault detection performance.

• Journal of IKEEE
• /
• v.25 no.3
• /
• pp.548-555
• /
• 2021

This paper outlines the basic concepts, approaches and research trends of fault diagnosis and fault tolerant control applied to industrial processes, facilities, and motor drives. The main role of fault diagnosis for industrial processes is to create effective indicators to determine the defect status of the process and then take appropriate measures against failures or hazadous accidents. The technologies of fault detection and diagnosis have been developed to determine whether a process has a trend or pattern, or whether a particular process variable is functioning normally. Firstly, data-driven based and model-based techniques were described. Secondly, fault detection and diagnosis techniques for industrial processes are described. Thirdly, passive and active fault tolerant control techniques are considered. Finally, major faults occurring in AC motor drives were listed, described their characteristics and fault diagnosis and fault tolerant control techniques are outlined for this purpose.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.44 no.2
• /
• pp.115-123
• /
• 2021

A lot of sensor and control signals is generated by an industrial controller and related internet-of-things in discrete manufacturing system. The acquired signals are such records indicating whether several process operations have been correctly conducted or not in the system, therefore they are usually composed of binary numbers. For example, once a certain sensor turns on, the corresponding value is changed from 0 to 1, and it means the process is finished the previous operation and ready to conduct next operation. If an actuator starts to move, the corresponding value is changed from 0 to 1 and it indicates the corresponding operation is been conducting. Because traditional fault detection approaches are generally conducted with analog sensor signals and the signals show stationary during normal operation states, it is not simple to identify whether the manufacturing process works properly via conventional fault detection methods. However, digital control signals collected from a programmable logic controller continuously vary during normal process operation in order to show inherent sequence information which indicates the conducting operation tasks. Therefore, in this research, it is proposed to a recurrent neural network-based fault detection approach for considering sequential patterns in normal states of the manufacturing process. Using the constructed long short-term memory based fault detection, it is possible to predict the next control signals and detect faulty states by compared the predicted and real control signals in real-time. We validated and verified the proposed fault detection methods using digital control signals which are collected from a laser marking process, and the method provide good detection performance only using binary values.

• Korean Chemical Engineering Research
• /
• v.58 no.3
• /
• pp.408-415
• /
• 2020

In this study, the AE-SOM method, which combines auto-encoder and self-organizing map, is used to detect and diagnose faults in EVA production process. Then, the fault propagation pathways are identified using Granger causality test. One year and seven months of operation data were obtained to detect faults of the process, and the process variables of the autoclave reactor are mainly analyzed. In the data pretreatment process, the data are standardized and 200 samples of each grade are randomly chosen to obtain a fault detection model. After that, the best matching unit (BMU) of each grade is confirmed by applying AE-SOM. The faults are determined based on each BMU. When a fault is found, the most causative variable of the fault is identified by using a contribution plot, and the fault propagation pathway is identified by Granger causality test. The prognostic of the two shutdowns is detected, and the fault propagation pathway caused by the faulty variable was analyzed.