• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.05a
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• pp.643-646
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• 2000

As many industrial systems become more complex, it becomes extremely difficult to diagnose the cause of failures. This paper presents a failure diagnosis approach based on discrete event system theory. In particular, the approach is a hybrid of event-based and state-based ones leading to a simpler failure diagnoser with supervisory control capability. The design procedure is presented along with a pump-valve system as an example.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.05a
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• pp.647-650
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• 2000

For the failure diagnosis of industrial system like various manufacturing systems, power plants and etc, many failure diagnosis approaches are considered. Here we are focus on the DES approach for failure diagnosis. We treats of failure recovery problem that is euly not mentioned in DES approach. The procedure to design a recoverable diagnoser is presented. And the recoverability, necessary and sufficient condition fur recoverability are defined. Then we make the high-level diagnoser to reduce the state size of recoverable diagnsoer. Finally, a pump-valve system example is presented.

• Journal of Drive and Control
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• v.18 no.1
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• pp.24-30
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• 2021

There is an increasing interest in condition-based maintenance for the prevention of economic loss due to failure. Moreover, immense research is being carried out in related technologies in the field of construction machinery. In particular, data-based failure diagnosis methods that employ AI (machine & deep learning) algorithms are in the spotlight. In this study, we have focused on the failure diagnosis and mode classification of reduction gear of excavator's travel device by using the AI algorithm. In addition, a remote monitoring system has been developed that can monitor the status of the reduction gear by using the developed diagnosis algorithm. The failure diagnosis algorithm was performed in the process of data acquisition of normal and abnormal under various operating conditions, data processing and analysis by the wavelet transformation, and learning. The developed algorithm was verified based on three-evaluation conditions. Finally, we have built a system that can check the status of the reduction gear of travel devices on the web using the Edge platform, which is embedded with the failure diagnosis algorithm and cloud.

• Journal of the Korean Society of Safety
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• v.20 no.3 s.71
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• pp.1-8
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• 2005

The industrial induction motor is widely used in the rotating electrical machine for the transmission of power. It is very reliable equipment, but it could lead to the loss of production and lift when failure occurs. Therefore, the failure data is acquired and analyzed by attaching an exclusive instrument to existing induction motor. However, these instruments could lead to side effects, increasing the production costs, because they are very expensive. The purpose of this study is the development of an induction motor bearing failure diagnosis system constructed using LabVIEW which can be supplied the kernelled function, process monitoring and current signature analysis. In addition, the availability and reasonability of the constructed system was examined for an induction motor with failure defects in outer raceway and ball bearing. From the results, it shows that failure diagnosis system constructed is useful for real-time monitoring with detection of bearing defects over the web.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.2
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• pp.263-268
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• 2021

This paper statistically analyzes the time required for each failure cause and describes a diagnostic method for 159 reports of failure analysis of MV cables that occurred in the distribution system of KEPCO over the past 18 years. In addition, the manufacturer's failure rate compared to 100C-km was calculated using 381 cases of MV cable deterioration failure between 2008 and 2020. It is hoped that this paper will help those in charge of maintaining underground facilities at the business office to use the failure rate to prioritize facility diagnosis.

• Journal of Aerospace System Engineering
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• v.16 no.6
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• pp.129-137
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• 2022

Recently, the technology of aircraft drivers has been transitioning from the existing hydraulically-focused mechanical system to an all-electric one due to the high precision and ease of maintenance of electric drivers. Consequently, the failure of an aircraft's electric motor can have fatal consequences. To ensure aircraft safety, efficient and timely fault diagnosis methods are required prompting the active pursuit of research into fault diagnosis technology. This paper introduces and analyses the failure types and failure diagnosis technology trends of permanent magnet synchronous motors among electric motors.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.35-38
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• 1995

We proposed in this paper a systematic way for analyzing discrete event dynamic systems to classify faults and failures quantitatively and to find tolerable fault event sequences embedded in the system. An automated failure diagnosis scheme with respect to the nominal normal operating event sequences and the supervisory control problem for tolerable fault event sequences is presented. Moreover the supervisor failure diagnosis problem with respect to the tolerable fault event sequences is considered. Finally, a plasma etching system example is presented.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.57-64
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• 2024

In the industrial field, robots are used to increase productivity by replacing labors with dangerous, difficult, and hard tasks. However, failures of individual industrial robots in the entire production process may cause product defects or malfunctions, and may cause dangerous disasters in the case of manufacturing parts used in automobiles and aircrafts. Although requirements for early diagnosis of industrial robot failures are steadily increasing, there are many limitations in early detection. This paper introduces methods for diagnosing robot failures using sound-based data and deep learning. This paper also analyzes, compares, and evaluates the performance of failure diagnosis using various deep learning technologies. Furthermore, in order to improve the performance of the fault diagnosis system using deep learning technology, we propose a method to increase the accuracy of fault diagnosis based on an inference window. When adopting the inference window of deep learning, the accuracy of the failure diagnosis was increased up to 94%.

• Advances in Computational Design
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• v.8 no.3
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• pp.255-271
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• 2023

The gear drive of a combat aircraft engine is responsible for power transmission to the different accessories necessary for the engine's operation. Incorrect power transmission can occur due to the presence of failure modes in the gears like bending fatigue, pitting, adhesive wear, scuffing, abrasive wear and polished wear etc. Fault diagnosis of the gear drive is necessary to get an early indication of failure of the gears. The present research is to develop an algorithm using different vibration signal processing techniques on industrial vibration acquisition systems to establish gear fault diagnosis architecture. The signal processing techniques have been used to extract various feature vectors in the development of the fault diagnosis architecture. An open-source dataset of other gear fault conditions is used to validate the developed architecture. The results is a basis for development of artificial intelligence based expert systems for gear fault diagnosis of a combat aircraft engine.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.822-829
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• 2022

According to existing study into the remote fault diagnosis procedure, the current diagnostic approach has an imperfect decision model, which only supports communication in a close distance. An Internet of Things (IoT)-based remote fault diagnostic approach for wind power equipment is created to address this issue and expand the communication distance of fault diagnosis. Specifically, a decision model for active power coordination is built with the mechanical energy storage of power generation equipment with a remote diagnosis mode set by decision tree algorithms. These models help calculate the failure frequency of bearings in power generation equipment, summarize the characteristics of failure types and detect the operation status of wind power equipment through IoT. In addition, they can also generate the point inspection data and evaluate the equipment status. The findings demonstrate that the average communication distances of the designed remote diagnosis method and the other two remote diagnosis methods are 587.46 m, 435.61 m, and 454.32 m, respectively, indicating its application value.