• Journal of Sensor Science and Technology
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• v.27 no.4
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• pp.254-258
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• 2018

A Wi-Fi signal network (WSN) system is introduced in this paper. This system consists of several data-transmitting sensor modules and a data-receiving server. Each sensor module and the server contain a unique intranet IP address. A piezoelectric accelerometer with a bandwidth of 12 kHz, a 24-bit analog-digital converter with a sampling rate of 15.625 kS/s, a 32-bit microprocessor unit, and a 1-Mbps Wi-Fi module are used in the data-transmitting sensor module. A 300-Mbps router and a PC are used in the server. The system is verified using an accelerometer calibrator. The voltage output from the sensor is converted into 24-bit digital data and transmitted via the Wi-Fi module. These data are received by a Wi-Fi router connected to a PC. The input frequencies of the accelerometer calibrator (320 Hz, 640 Hz, and 1280 Hz) are used in the data transfer verification. The received data are compared to the data retrieved directly from the analog-to-digital converter used in the sensor module. The comparison shows that the developed system represents the original data considerably well. Theoretically, the system can acquire vibration signals from 600 sensor modules at an accelerometer bandwidth of 15.625 kHz. However, delay exists owing to software processes, multiplexing between sensor modules, and the use of non-real time operating system. Hence, it is recommended that this system may be used to acquire vibration signals with up to 10 kHz, which is approximately 70% of the theoretical maximum speed of the system. The system can be upgraded using parts with higher performance

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1761-1766
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• 2008

This paper deals with a novel autocalibration method of three-axis micromachined accelerometers applied to a new golf swing diagnosis equipment for golfers. This diagnosis equipment can help golfers monitor and anlalyze their swing posture and therefore modify their swing action to get better score and enjoy their lives through golf. The micromachined accelerometers to get information of the motion are the essential part of the putting club to measure the three-axis acceleration as accurately as possible. This paper presents an efficient autocalibration algorithm to find the offset and sensitivity of accelerometers by only using the static measurement data at six different positions. The experimetnal results on the developed putters show the validity of the proposed algorithm for the new smart putter.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.891-894
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• 2007

This study presents fault diagnosis of low speed bearing using support vector machine (SVM). The data used in the experiment was acquired using acoustic emission (AE) sensor and accelerometer. The aim of this study is to compare the performance of fault diagnosis based on AE signal and vibration signal with same load and speed. A low speed test rig was developed to simulate various defects with shaft speeds as low as 10 rpm under several loading conditions. In this study, component analysis was also performed to extract the feature and reduce the dimensionality of original data feature. Moreover, the classification for fault diagnosis was also conducted using original data feature without feature extraction. The result shows that extracted feature from AE sensor gave better performance in faults classification.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.2134-2143
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• 2018

This work introduces a fault diagnosis method for transformer based on self-powered radio frequency identification (RFID) sensor tag and improved Hilbert-Huang transform (HHT). Consisted by RFID tag chip, power management circuit, MCU and accelerometer, the developed RFID sensor tag is used to acquire and wirelessly transmit the vibration signal. A customized power management including solar panel, low dropout (LDO) voltage regulator, supercapacitor and corresponding charging circuit is presented to guarantee constant DC power for the sensor tag. An improved band restricted empirical mode decomposition (BREMD) which is optimized by quantum-behaved particle swarm optimization (QPSO) algorithm is proposed to deal with the raw vibration signal. Compared with traditional methods, this improved BREMD method shows great superiority in reducing mode aliasing. Then, a promising fault diagnosis approach on the basis of Hilbert marginal spectrum variations is brought up. The measured results show that the presented power management circuit can generate 2.5V DC voltage for the rest of the sensor tag. The developed sensor tag can achieve a reliable communication distance of 17.8m in the test environment. Furthermore, the measurement results indicate the promising performance of fault diagnosis for transformer.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1993.10a
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• pp.103-106
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• 1993

This paper describes the modeling of winding vibration for a variation of load and temperature. The structural changes in transformer windings due to heat cause the change of vibration patterns. The vibration signals were detected by the accelerometer on the transformer windings. The real values were compared with estimated value using least-squares method, vibration model was cstablished and with this model, error compared with original signal was less than -50[db]. These results could be applied to diagnosis of incipient failures of the power transformers.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.5
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• pp.21-27
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• 2008

Despite recent studies on development of pulse diagnosis systems and needs for commercializing them, the reproducibility is one of the most controversial issues as ever. Because the pulse pressure value, which is one of the important parameters to evaluate reproducibility, is very vulnerable to moving artifacts, the reproducibility can not be obtained easily. In this paper, we suggested a moving artefacts detection system for a pulse diagnosis system so that a pulse diagnosis system can be robust to theses kinds of artefacts by excluding the contaminated parts from the pulse wave signal to be analyzed. This moving artifacts detection system was designed to consist of a three-axis accelerometer, an electromyography amplifier and a two-axis tilt sensor. To assess the suitability of the system, we examined the characteristics of each sensor's output signals with regard to the three specific motions such as extension, flexion and rotation. And, we also examined the each sensor's response to the high-frequency and low-frequency moving artifacts while the pulse wave signal was acquired from a pressure sensor for the pulse diagnosis. From these results, we could find that the response to subject's motions would be reflected in electromyography signal first, in accelerometer signals and in tilt sensor sequently. And, the facts that a stable pulse wave can be acquired in two seconds after high frequency or low frequency motions ended, were also found. Consequently, based on these findings, we set up some rules on the moving artifacts detection and designed an algorithm which is fit for our moving artifacts detection system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1433-1437
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• 2005

Until now, strain gage technique and accelerometer for the diagnosis safety of constructions are used widely. However, the limits of these methods are revealed. But Electronic Speckle Pattern Interferometry(ESPI) that uses Pulse Laser is noncontact, whole-field, real-time measuring method also dull to disturbance and can achieve test result in a very short time. It has various strong point in spot application, swift establishment, and dynamic conduct analysis for the entire field of Laser illuminate. This author analyzed vibration characteristic of using the Pulse ESPI System, the diagnosis safety of bridges, to simplify the analysis of the dynamic conduct of a large construction.

• Journal of the Korean Society for Railway
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• v.2 no.2
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• pp.13-20
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• 1999

The vibration signals of driving parts of electric train are distorted its signal patterns due to the impact components, which occurs when wheel passes rail joints. An elimination method of the impact components is investigated using adaptive signal processing technique in this study The result shows that adaptive interference canceling method seems to be more effective than line enhancement technique. The application of adaptive interference canceling method to the signal measured at bogie shows that the extractions of the signals of driving parts of traction motor, reduction gear, and axle bearing are successful. Therefore, only the signals of bogie, which is the place to attach an accelerometer easily, is sufficient for the fault diagnosis and the safety evaluation of electric train. Also, adaptive interference canceling method can be applicable to evaluate the performance of vibration isolation between bogie and car body and to investigate the characteristics of indoor sound.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.489-492
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• 2008

Nowadays in order to estimate safety diagnosis of bridge, a lot of data like static and dynamic displacement, accelerometer, wind velocity and so on are demanded. When it comes to measure these data, cabled sensor is essential equipment. But cabled sensors have also inefficient factors. From this point of view, considering practical aspects of using these expensive equipments which have been used to examine safety diagnosis, measuring by cabled sensors is restrictive in some respect. Recently to improve theses problems, Wireless sensor system was introduced. But this system can't perform intelligent reaction because database of this system is just based on internet. In this paper, the intelligent bridge safety management model which can be installed easily, measured at all times and dealing intelligently with various situations is developed to improve these problems.

• Journal of IKEEE
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• v.23 no.4
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• pp.1273-1279
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• 2019

Though sleep polysomnography (PSG) is considered as a golden rule for medical diagnosis of sleep disorder, it is essential to find alternative diagnosis methods due to its cost and time constraints. Recently, as the popularity of wearable health devices, there are many research trials to replace conventional actigraphy to consumer grade devices. However, these devices are very limited in their use due to the accessibility of the data and algorithms. In this paper, we showed the predictive model for sleep stages classified by American Academy of Sleep Medicine (AASM) standard and we proposed the estimation of sleep cycle by comparing sensor data and power spectrums of δ wave and θ wave. The sleep stage prediction for 31 subjects showed an accuracy of 85.26%. Also, we showed the possibility that proposed algorithm can find the sleep cycle of REM sleep and NREM sleep.