• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.1114-1117
• /
• 1993

The paper presents an effective method for finding the propagation structure of the real origin of a system malfunction. It uses a combined system model consisting of Structural Model (SM) in the form of Fuzzy Directed Graph and Behavior Model (BM) as a set of Fuzzy Relational Equations $A\;{\circ}\;R\;=\;B$. Here a specially proposed fuzzy inference technique is checked and investigated. Finally a test example for fault diagnosis of an industrial system is given and analyzed.

• Journal of Mechanical Science and Technology
• /
• v.14 no.7
• /
• pp.773-781
• /
• 2000

The focus of this study is the development of a credible fault detection system of the cylindrical plunge grinding process. The acoustic emission (AE) signals generated during machining were analyzed to determine the relationship between grinding-related faults and characteristics of changes in signals. Furthermore, a neural network, which has excellent ability in pattern classification, was applied to the diagnosis system. The neural network was optimized with a momentum coefficient, a learning rate, and a structure of the hidden layer in the iterative learning process. The success rates of fault detection were verified.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.27 no.1
• /
• pp.10-19
• /
• 1990

A fault tolerant control system, in which a digital back-up controller system is added on the existing analog control system, is developed for enhancing reliability of boiler control system in power plant. The digital back-up controller system(DBCS) has a multi-processor structure with capabilities of fault diagnosis, back-up control, self test, and graphic monitoring. Specifically, switching mechanism composed of expandable modules is designed so that back-up controller takes over any faulty control loops and the number of back-up control loops is determined as that of simultaneous faults. A process simulator that simulates the boiler analog control system is developed for safety test and performance evaluation prior to real plant application. DBCS is installed at the Ulsan thermal power plant, and fault tolerant control performance is assured under the faults that some controller modules are pulled out.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2000.05a
• /
• pp.639-642
• /
• 2000

Chemical facility system have dangerous elements that can injure the human like an explosion and a fire, gas poisoning by a leakage of the harmful chemical material. In addition to a vibration of the machine occurs the leakage. Therefore, the chemical factory requires for periodic monitoring of the vibration. But, until now, the operator has executed a monitoring of the machine by the senses. So, the diagnostic expert system by which the operator can judge easily and expertly a condition of the machine is developed. This paper describes the structure of diagnostic system and the diagnostic algorithm using fuzzy inference

• Journal of the Korean Society of Safety
• /
• v.15 no.2
• /
• pp.36-45
• /
• 2000

Automated production system is composed of many complicated techniques and it become a very difficult task to control, monitor and diagnose this compound system. Moreover, it is required to develop an effective diagnosing technique and reduce the diagnosing time while operating the system in parallel under many faults occurring concurrently. This study develops a Modular Artificial Neural Network(MANN) which can perform a diagnosing function of multiple faults with the following steps: 1) Modularizing a complicated system into subsystems. 2) Formulating a hierarchical structure by dividing the subsystem into many detailed elements. 3) Planting an artificial neural network into hierarchical module. The system developed is implemented on workstation platform with $X-Windows^{(r)}$ which provides multi-process, multi-tasking and IPC facilities for visualization of transaction, by applying the software written in $ANSI-C^{(r)}$ together with $MOTIF^{(r)}$ on the fault diagnosis of PI feedback controller reactor. It can be used as a simple stepping stone towards a perfect multiple diagnosing system covering with various industrial applications, and further provides an economical approach to prevent a disastrous failure of huge complicated systems.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2006.11a
• /
• pp.591-596
• /
• 2006

This study is base on the fault detection and diagnosis when a crack is happened a structure. A crack in a structure will affect the modal parameters. We are searched a percentage of changes in the natural frequencies according to changes of location and propagation of the crack using the Rayleigh's energy method. This method is presented to identify the presence of a crack and its location. The study is carried out both theoretically and experimentally and the results are presented in this paper. The location of the crack is also moved from the fixed end to the free end along its length. The changes in natural frequencies are observed from theoretically study, due to the presence of the crack at different locations and depths, and the percentage change in frequency values are calculated. These results are confirmed by the experiments. And then, a difference between a cracked beam and uncracked beam observed using the bispectrum as high-order spectrum.

• KIPS Transactions on Computer and Communication Systems
• /
• v.3 no.5
• /
• pp.129-140
• /
• 2014

BIST(Built-in self test) is to detect various faults of the existing memory and BIRA(Built-in redundancy analysis) is to repair detected faults by allotting spare. Also, BISR(Built-in self repair) which integrates BIST with BIRA, can enhance the whole memory's yield. However, the previous methods were suggested for RAM and are difficult to diagnose disturbance that is NAND-type flash memory's intrinsic fault when used for the NAND-type flash memory with different characteristics from RAM's memory structure. Therefore, this paper suggests a BISD(Built-in self diagnosis) to detect disturbance occurring in the NAND-type flash memory and to diagnose the location of fault, and BISR to repair faulty blocks.

• Journal of the Korean Society for Precision Engineering
• /
• v.14 no.1
• /
• pp.108-118
• /
• 1997

The heterarchical structure provides a more attractive solution to the conventional hierarchical structure as the density and level of distrubution of computing resources in manufacturing system expands. Tracing the evolution of control structures for automated manufacturing systems, this paper discusses the design principles for heterarchical system to reduce complexity, increase extendability, flexible configurability and suggests a good example of real-time adaptation using the concept of intelligent agent of manufac- turing entities and fault diagmosis system.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.9 no.4
• /
• pp.108-116
• /
• 2000

Undesired trouble such as chatter vibration and burning on the ground surface appears frequently in the cylindrical plunge grinding process. Establishment of a credible fault diagnostic system for the grinding process is the major purpose of this study. Power signals generated during the grinding operation were sampled and analyzed to determine the relationship between grinding troubles and behavior of signal changes. In addition, a neural network was optimized with a momentum coefficient a learning rate, and a structure of the hidden layer through the iterative learning process. Based on the established system, success rates of the trouble recognition were verified.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.20 no.1
• /
• pp.163-169
• /
• 2020

Recently, research on automation and unmanned operation of machines in the industrial field has been conducted with the advent of AI, Big data, and the IoT, which are the core technologies of the Fourth Industrial Revolution. The machines for these automation processes are controlled based on the data collected from the sensors attached to them, and further, the processes are managed. Conventionally, the abnormalities of sensors are periodically checked and managed. However, due to various environmental factors and situations in the industrial field, there are cases where the inspection due to the failure is not missed or failures are not detected to prevent damage due to sensor failure. In addition, even if a failure occurs, it is not immediately detected, which worsens the process loss. Therefore, in order to prevent damage caused by such a sudden sensor failure, it is necessary to identify the failure of the sensor in an embedded system in real-time and to diagnose the failure and determine the type for a quick response. In this paper, a deep neural network-based fault diagnosis system is designed and implemented using Raspberry Pi to classify typical sensor fault types such as erratic fault, hard-over fault, spike fault, and stuck fault. In order to diagnose sensor failure, the network is constructed using Google's proposed Inverted residual block structure of MobilieNetV2. The proposed scheme reduces memory usage and improves the performance of the conventional CNN technique to classify sensor faults.