• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.12 no.3
• /
• pp.8-16
• /
• 2003

Monitoring of the drill wear :md hole quality change is conducted during the drilling process. Cutting force measured by tool dynamometer is a evident feature estimating abnormal state of drilling. One major difficulty in using tool dynamometer is that the work-piece must be mounted on the dynamometer, and thus the machining process is disturbed and discontinuous. Acoustic transducer do not disturb the normal machining process and provide a relatively easy way to monitor a machining process for industrial application. for this advantage, AE signal is used to estimate the abnormal fate. In this study vision system is used to detect flank wear tendency and hole quality, there are many formal factors in hole quality decision circularity, cylindricity, straightness, and so of but these are difficult to measure in on-line monitoring. The movement of hole center and increasement of hole diameter is presented to determine hole quality. As the results of this experiment AE RMS signal and measurements by vision system are shorn the similar tendency as abnormal state of drilling.

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

• Korean Journal of Materials Research
• /
• v.18 no.9
• /
• pp.497-502
• /
• 2008

BGA test sockets failed earlier than the expected life-time due to abnormal signal delay, shown especially at the low temperature ($-50^{\circ}C$). Analysis of failed sockets was conducted by EDX, AES, and XRD. A SnO layer contaminated with C was found to form on the surface of socket pins. The formation of SnO layer was attributed to the repeated Sn transfer from BGA balls to pin surface and instant oxidation of fresh Sn. As a result, contact resistance increased, inducing signal delay. Abnormal signal delay at the low temperature was attributed to the increasing resistivity of Sn oxide with decreasing temperature, as manifested by the resistance measurement of $SnO_2$.

• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.11
• /
• pp.218-226
• /
• 1998

In this study a neural network based expert system designed to diagnose operating status of a rotating spindle system supported by ball bearings was introduced. In order to facilitate practical failure situations, five exemplary abnormal status was fabricated. Out of several possible data source locations, seven most effective spots were chosen and proven to be the most successful in predicting single and multiple abnormalities. Increased signal strength was measured around where abnormality was embedded. Signal mea-surement locations producing high prediction rate were also classified. Even though multiple abnormalities were hard to be decoupled into their individual causes, proposed diagnostic system was somewhat effective in predicting such cases under certain combination of sensor locations. Among several abnormal operating conditions, highest prediction rate can be expected when signal is spoiled by the failure or damage in outer race. Proposed diagnostic system was again proven to be the most effective system in analyzing and ranking the importance of data sources.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2001.11b
• /
• pp.1067-1072
• /
• 2001

In this study, the vibration diagnosis program for onboard machinery has been developed. The developed program includes signal monitoring module, system diagnosis module, and system modification module. The signal monitoring module is to monitor the vibration signal in time and frequency domains. And the system diagnosis module, which is developed by using Neural Network with error back propagation algorithm, can detect the abnormal symptom indicating the malfunction of the machinery onboard ships. The investigations of the developed system are presented through the experiment using Rotor Kit. Abnormal vibration signals are created by adding additional weight, manually misaligning the shaft, and loosening the bolts.

• Journal of Industrial Technology
• /
• v.24 no.A
• /
• pp.83-90
• /
• 2004

Generally, electronic appliances are used on the basis of normal power source supply. The power source inevitably includes the abnormal condition, such as, sudden voltage sagging, power interrupt, and induced noises. As the electronic appliances which include micro-controller-based circuits are being increased recently, the controller circuit sometimes malfunctions by the abnormal condition of the power source. This situation causes serious problems such as hitch of electric appliance, fire and medical instrument glitch, which produces serious situations. In this paper, development of power interrupt tester which is highly suitable for an endurance test device under abnormal power source to microprocessor-based circuits is proposed 89C2051 microcontroller is performed to make power interrupt signal, and software controls peripheral hardwares and built-in functions. Experimental results of this study will offer a good application to electronic appliance maker as a test device of hardware and software debugging use.

• Fire Science and Engineering
• /
• v.28 no.3
• /
• pp.87-91
• /
• 2014

This paper is aimed to find electrical fire danger for analyzing the characteristics of temperature, current and voltage signals for motor on electric fan. In order to attain this purpose, detected were the temperature, current and voltage signals on electric wire with free (normal state) and locked (abnormal state) motor. For voltage and current signals, voltage signal is no big difference with normal and abnormal states and current signal is higher in abnormal state (highest 309 mA) than the normal state (highest 203 mA). In the case of Temperature signal, the temperature distribution of the motor as a whole is different. It is difference in the case of the normal state $4^{\circ}C$ and the abnormal state $18^{\circ}C$. In particular, most of the electric wiring to the motor of the fan is attached to the fixture of motor back. Considering at allowable temperature ($60^{\circ}C$) of the electric wire could be accelerated to insulation deterioration. The results of this study will be effectively used in analyzing for electric fire and developing the preventive devices of electric fan.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.5
• /
• pp.7-13
• /
• 2020

This paper focuses on detecting abnormal patterns of respiration of humans. In this study, a contact-based device was used to acquire both normal and abnormal respiration signals. To this end, this paper reports the development of a monitoring system to investigate the respiratory status of humans in a normal environment. This work aims to classify the respiratory status, i.e., normal and abnormal status, quantitatively. The respiration signal is acquired using a contact-based medical device (BIOBPAC), and noise reduction is carried out before classifying the respiratory status. To reduce noise, a mixed filter that combines the Savitzky-Golay filter and Median filter is applied to the acquired respiration signals. The inter-class distance is maximized, and the intra-class distance is minimized. The proposed algorithm is straightforward and can be applied to a practical environment. In addition, the experimental results are provided to substantiate the proposed approach.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.1
• /
• pp.30-40
• /
• 2022

Electrocardiogram (ECG) signal's shape and characteristic varies through each individual, so it is difficult to classify with one neural network. It is difficult to classify the given data directly, but if corresponding normal beat is given, it is relatively easy and accurate to classify the beat by comparing two beats. In this study, we classify the ECG signal by generating the reference normal beat through the template cluster, and combining with the input ECG signal. It is possible to detect abnormal beats of various individual's records with one neural network by learning and classifying with the imaged ECG beats which are combined with corresponding reference normal beat. Especially, various neural networks, such as GoogLeNet, ResNet, and DarkNet, showed excellent performance when using the comparative learning. Also, we can confirmed that GoogLeNet has 99.72% sensitivity, which is the highest performance of the three neural networks.

• Journal of the Institute of Convergence Signal Processing
• /
• v.19 no.3
• /
• pp.97-103
• /
• 2018

This paper addresses an improved naval combat UPS(Uninterruptable Power Supply) system under abnormal transients. Previously, thermistor and varistor elements were used to cope with transient overvoltage and overcurrent, however the UPS was frequently unavailable because it was vulnerable to abnormal transient voltage generated during system operation. In order to overcome this problem and protect UPS system, this paper proposes an input power cut-off circuit that detects the initial input power and abnormal transient voltage generated during operation, improvement of power control sequence, and a method to prevent malfunction of an inverter and CPU. The UPS system implementing the proposed method was simulated by input power variable test using programmable AC/DC generator, and finally validated its reliability and stability through field tests by mounting on multifunctional console of naval combat system.