• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.90-93
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• 2014

In this paper, we have developed a new fault detection method based on vibration signal for rotor machinery. Generally, many methods related to detection of rotor fault exist and more advanced methods are continuously developing past several years. However, there are some problems with existing methods. Oftentimes, the accuracy of fault detection is affected by vibration signal change due to change of operating environment since the diagnostic model for rotor machinery is built by the data obtained from the system. To settle a this problems, we build a rotor diagnostic model by using feature residual based on vibration signal. To prove the algorithm's performance, a comparison between proposed method and the most used method on the rotor machinery was conducted. The experimental results demonstrate that the new approach can enhance and keeps the accuracy of fault detection exactly although the algorithm was applied to various systems.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.7
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• pp.813-822
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• 2012

Abnormal operating sounds radiated from a moving transfer robot in LCD (liquid crystal display) product lines have been used for the fault detection line of a robot instead of other source signals such as vibrations, acoustic emissions, and electrical signals. Its advantage as a source signal makes it possible to monitor the status of multiple faults by using only a microphone, despite a relatively low sensitivity. The wavelet packet transform for feature extraction and the artificial neural network for fault classification are employed. It can be observed that the abnormal operating sound is sufficiently useful as a source signal for the fault diagnosis of mechanical components as well as other source signals.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.9
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• pp.72-79
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• 2004

A method for thruster fault diagnosis for launch vehicle upper stage was developed. In order to protect the launch vehicle against the occurrence of faults, it is necessary to detect and identify the fault, as well as to reconfigure the controller of the vehicle. Considering the upper stage launch vehicle using reaction control system, an analytical method was adopted in order to detect the fault occurred in thruster. The fault detection scheme can be applied to the system regardless of the form of thruster fault occurred - leakage or lock-out. Results from processor-in-the-loop simulation are provided to demonstrate the validity of this fault detection and isolation scheme for the upper stage launch vehicle.

• Transactions on Electrical and Electronic Materials
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• v.6 no.6
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• pp.262-271
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• 2005

In order to achieve timely and accurate fault detection of plasma etching process, neural network based time series modeling has been applied to reactive ion etching (RIE) using two different in-situ plasma-monitoring sensors called optical emission spectroscopy (OES) and residual gas analyzer (RGA). Four different subsystems of RIE (such as RF power, chamber pressure, and two gas flows) were considered as potential sources of fault, and multiple degrees of faults were tested. OES and RGA data were simultaneously collected while the etching of benzocyclobutene (BCB) in a $SF_6/O_2$ plasma was taking place. To simulate established TSNNs as incipient fault detectors, each TSNN was trained to learn the parameters at t, t+T, ... , and t+4T. This prediction scheme could effectively compensate run-time-delay (RTD) caused by data preprocessing and computation. Satisfying results are presented in this paper, and it turned out that OES is more sensitive to RF power and RGA is to chamber pressure and gas flows. Therefore, the combination of these two sensors is recommended for better fault detection, and they show a potential to the applications of not only incipient fault detection but also incipient real-time diagnosis.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.59 no.3
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• pp.275-288
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• 2010

In order to improve the quality of electricity in large scale power systems, stability of power system has to be achieved. This can be done by the means of preventative diagnosis of power equipments and protection, monitoring and control of the power system. Since the recent adoption of digital controllers, an improvement in stability was observed; in particular, IED, which contained self-diagnostic abilities such as fault tolerance, allowed for automatic recovery via redundancy or switching-over functions should there be faults with the equipments. Furthermore, communication lines have been hugely simplified, thus adding to the improvement in stability significantly. Taking these error reports and forecasting emergency reports and by effectively responding to them in the overiding controlling systems, high levels of system stability can be obtained. Power Management System that is being applied to automated power sub-stations, takes the IEC61850 international standard as its specification. In this paper, additional research into achieving stability of already developed PMS system and also the stability of the overall system was carried out, and the results of development of communication servers, which play a pivotal role in connecting systems, are stated.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.2
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• pp.283-288
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• 2015

The possible erroneous output data of the sensor nodes can cause the performance limit or the degradation of the reliability in the whole wireless sensor networks(WSN). In this paper, we propose a new sensor node scheme with multiple sensors and a new fault diagnostic algorithm. The algorithm can increase the reliability of the whole WSNs by utilizing measurements of the multiple sensors on the node and by determining the validity of the date by comparing the value of each sensor. It can increase the cost and complexity of the node, but is suitable for the area where the high reliability is critical.

• Nuclear Engineering and Technology
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• v.49 no.5
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• pp.896-904
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• 2017

Several Generation IV nuclear reactor concepts have goals for optimizing investment recovery through phased introduction of multiple units on a common site with shared facilities and/or reconfigurable energy conversion systems. Additionally, small modular reactors are suitable for remote deployment to support highly localized microgrids in isolated, underdeveloped regions. The long-term economic viability of these advanced reactor plants depends on significant reductions in plant operations and maintenance costs. To accomplish these goals, intelligent control and diagnostic capabilities are needed to provide nearly autonomous operations with anticipatory maintenance. A nearly autonomous control system should enable automatic operation of a nuclear power plant while adapting to equipment faults and other upsets. It needs to have many intelligent capabilities, such as diagnosis, simulation, analysis, planning, reconfigurability, self-validation, and decision. These capabilities have been the subject of research for many years, but an autonomous control system for nuclear power generation remains as-yet an unrealized goal. This article describes a functional framework for intelligent, autonomous control that can facilitate the integration of control, diagnostic, and decision-making capabilities to satisfy the operational and performance goals of power plants based on multimodular advanced reactors.

• Journal of applied mathematics & informatics
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• v.41 no.6
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• pp.1257-1274
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• 2023

Rotating machines heavily rely on an intricate network of interconnected sub-components, with bearing failures accounting for a substantial proportion (40% to 90%) of all such failures. To address this issue, intelligent algorithms have been developed to evaluate vibrational signals and accurately detect faults, thereby reducing the reliance on expert knowledge and lowering maintenance costs. Within the field of machine learning, Artificial Immune Systems (AIS) have exhibited notable potential, with applications ranging from malware detection in computer systems to fault detection in bearings, which is the primary focus of this study. In pursuit of this objective, we propose a novel procedure for detecting novel instances of anomalies in varying operating conditions, utilizing only the signals derived from the healthy state of the analyzed machine. Our approach incorporates AIS augmented by Dynamic Time Warping (DTW). The experimental outcomes demonstrate that the AIS-DTW method yields a considerable improvement in anomaly detection rates (up to 53.83%) compared to the conventional AIS. In summary, our findings indicate that our method represents a significant advancement in enhancing the resilience of AIS-based novelty detection, thereby bolstering the reliability of rotating machines and reducing the need for expertise in bearing fault detection.

• Journal of the Korean Society of Safety
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• v.38 no.5
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• pp.43-50
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• 2023

With the advent of industrialization, consumers and end-users demand more reliable products. Meeting these demands requires a comprehensive approach, involving tasks such as market information collection, planning, reliable raw material procurement, accurate reliability design, and prediction, including various reliability tests. Moreover, this encompasses aspects like reliability management during manufacturing, operational maintenance, and systematic failure information collection, interpretation, and feedback. Improving product reliability requires prioritizing it from the initial development stage. Failure mode and effect analysis (FMEA) is a widely used method to increase product reliability. In this study, we reanalyzed using the FMEA method and proposed an improved method. Domestic railways lack an accurate measurement method or system for maintenance, so maintenance decisions rely on the opinions of experienced personnel, based on their experience with past faults. However, the current selection method is flawed as it relies on human experience and memory capacity, which are limited and ineffective. Therefore, in this study, we further specify qualitative contents to systematically accumulate failure modes based on the Failure Modes Table and create a standardized form based on the Master FMEA form to newly systematize it.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.9
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• pp.1340-1349
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• 2017

Objective: This study aimed to evaluate the microbiological and cellular milk profile for the diagnosis of subclinical mastitis in female buffaloes and to assess risk factors for predisposition of the disease. Methods: Analyses were carried out by standard plate count (SPC), identification of species and antibiotic resistance, somatic cell count (SCC), electrical electrical conductivity of milk (ECM), and lactoferrin content in milk. Teat cups were swabbed to evaluate risk factors, observing hyperkeratosis, milking vacuum pressure and cleanliness of the site. Hence, 30 female buffaloes were randomly selected (15 from a group in early lactation and 15 in late lactation). Results: The most common bacteria in the microbiological examination were Staphylococcus spp., Streptococcus spp. and Corynebacterium sp. In the antibiotic sensitivity test, 10 (58.82%) of the 17 antibiotics tested were sensitive to all isolates, and resistant bacteria were Streptococcus uberis, Streptococcus dysgalactiae, Streptococcus haemolyticus, and Escherichia coli. It was observed that positive samples in the microbiological examination showed total bacterial count between $9.10{\times}10^3$ to $6.94{\times}10^6$ colony forming units/mL, SCC between 42,000 to 4,320,000 cells/mL and ECM ranging from 1.85 to 7.40 mS/cm. It was also found that the teat cups had high microbial counts indicating poor hygiene, and even faults in the cleanliness of the animals' waiting room were observed. It is concluded that values of SCC above 537,000 cells/mL and ECM above 3.0 mS/mL are indications of mammary gland infection for this herd; however, the association of these values with a microbiological analysis is necessary to more accurately evaluate the health status of mammary glands with subclinical mastitis. Conclusion: Through phenotypic characterization of bacteria involved in the samples, the genera Staphylococcus spp., Streptococcus spp., and Corynebacterimum bovis were the most prevalent in this study. Faults in environment and equipment hygienization are factors that are directly associated with mastitis.