• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2708-2714
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• 2009

Due to poor linguistic communication skills of sucklings and infants, crying mostly is only means of communication to express their body conditions and desires. We, therefore, developed an infant auscultation system which detects which part of the body has a pathological problem, by analysing infant's crying sound patterns. Specifically, in this paper, we accomplished an auscultation system for congenital heart diseases detection by performing pitch, intensity and spectrum analysis of the crying sounds between the normal infants group and the congenital heart diseases group. With this system, we can diagnose congenital heart diseases of infants with poor communication capacity, and, in the near future, can build a home care diagnosis system based on crying sound analysis technologies through additional experiments on medical data.

• Smart Structures and Systems
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• v.13 no.3
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• pp.453-471
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• 2014

The fault diagnosis of rolling element bearings has drawn considerable research attention in recent years because these fundamental elements frequently suffer failures that could result in unexpected machine breakdowns. Artificial intelligence algorithms such as artificial neural networks (ANNs) and support vector machines (SVMs) have been widely investigated to identify various faults. However, as the useful life of a bearing deteriorates, identifying early bearing faults and evaluating their sizes of development are necessary for timely maintenance actions to prevent accidents. This study proposes a new two-layer structure consisting of support vector regression machines (SVRMs) to recognize bearing fault patterns and track the fault sizes. The statistical parameters used to track the fault evolutions are first extracted to condense original vibration signals into a few compact features. The extracted features are then used to train the proposed two-layer SVRMs structure. Once these parameters of the proposed two-layer SVRMs structure are determined, the features extracted from other vibration signals can be used to predict the unknown bearing health conditions. The effectiveness of the proposed method is validated by experimental datasets collected from a test rig. The results demonstrate that the proposed method is highly accurate in differentiating between fault patterns and determining their fault severities. Further, comparisons are performed to show that the proposed method is better than some existing methods.

• Journal of Sensor Science and Technology
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• v.25 no.5
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• pp.371-376
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• 2016

A self-powered piezoelectric energy harvester was developed for the application in wireless sensor node. The energy harvester was evaluated with power generation characteristics for the wireless sensor node for structural diagnosis of the electric power system. The self-powered wireless sensor node was set to measure temperature, vibration frequency of the electric power system. A piezoelectric harvester composed of 7 uni-morph cantilevers (functionalized as 6 generators and 1 vibration sensor) was connected to be an array and revealed to produce significantly high output power of approximately 10 mW at 120 Hz under 3.4 g((1 g = $9.8m/sec^2$). The wireless sensor node could work as the electric power generated by the developed piezoelectric harvester.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.7
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• pp.555-561
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• 2014

Acoustic Emission technique is widely applied to develop the early fault detection system, and the problem about a signal processing method for AE signal is mainly focused on. In the signal processing method, envelope analysis is a useful method to evaluate the bearing problems and wavelet transform is a powerful method to detect faults occurred on rotating machinery. However, exact method for AE signal is not developed yet for the rotating machinery diagnosis. Therefore, in this paper two methods which are processed by Hilbert transform and DET for feature extraction. In addition, we evaluate the classification performance with varying the parameter from 2 to 15 for feature selection DET, 0.01 to 1.0 for the RBF kernel function of SVR, and the proposed algorithm achieved 94 % classification of averaged accuracy with the parameter of the RBF 0.08, 12 feature selection.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

• Journal of Navigation and Port Research
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• v.33 no.10
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• pp.671-678
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• 2009

Recently, there have been some researches in the equipment fault detection based on shock and vibration information. Most research of them is based on shock and vibration monitoring to determine the equipment fault or not. Different with engine fault detection based on shock and vibration information we focus on detection of engine for boat and system control. First, it use the duplicated-checking method for shock and vibration information to determine the engine fault or not. If there is a fault happened, we use the integral to determine the error engine shock wave width and detect the fault area. On the other hand, we use the engine trend analysis and standard of safety engine to implement the shock and vibration information database. Our simulation results show that the probability of engine fault determination is 98% and the probability of engine fault detection is 72%

• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.141-147
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• 2011

This paper proposes an efficient two-stage fault prediction algorithm for fault detection and diagnosis of induction motors. In the first phase, we use a linear predictive coding (LPC) method to extract fault patterns. In the second phase, we use a dynamic time warping (DTW) method to match fault patterns. Experiment results using eight vibration data, which were collected from an induction motor of normal fault states with sampling frequency of 8 kHz and sampling time of 2.2 second, showed that our proposed fault prediction algorithm provides about 45% better accuracy than a conventional fault diagnosis algorithm. In addition, we implemented and tested the proposed fault prediction algorithm on a testbed system including TI's TMS320F2812 DSP that we developed.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.2
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• pp.205-210
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• 2014

Recently, pattern recognition methods have been widely used by researchers for fault diagnoses of mechanical systems. A pattern recognition method determines the soundness of a mechanical system by detecting variations in the system's vibration characteristics. Hidden Markov models (HMMs) and artificial neural networks (ANNs) have recently been used as pattern recognition methods in various fields. In this study, a HMM-ANN hybrid method for the fault diagnosis of a mechanical system is introduced, and a rotating wind turbine blade with a crack is selected for fault diagnosis. The existence, location, and depth of said crack are identified in this research. For improving the diagnostic accuracy of the method in spite of the presence of noise, a moment with a few specific frequencies is applied to the structure.

• Earthquakes and Structures
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• v.17 no.2
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• pp.175-189
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• 2019

The aim of the study is to show the results of complex shaking table experimental investigation focused on the response of two models of cylindrical steel tanks under mining tremors and moderate earthquakes, including the aspects of diagnosis of structural damage. Firstly, the impact and the sweep-sine tests have been carried out, so as to determine the dynamic properties of models filled with different levels of liquid. Then, the models have been subjected to seismic and paraseismic excitations. Finally, one fully filled structure has been tested after introducing two different types of damages, so as to verify the method of damage diagnosis. The results of the impact and the sweep-sine tests show that filling the models with liquid leads to substantial reduction in natural frequencies, due to gradually increasing overall mass. Moreover, the results of sweep-sine tests clearly indicate that the increase in the liquid level results in significant increase in the damping structural ratio, which is the effect of damping properties of liquid due to its sloshing. The results of seismic and paraseismic tests indicate that filling the tank with liquid leads initially to considerable reduction in values of acceleration (damping effect of liquid sloshing); however, beyond a certain level of water filling, this regularity is inverted and acceleration values increase (effect of increasing total mass of the structure). Moreover, comparison of the responses under mining tremors and moderate earthquakes indicate that the power amplification factor of the mining tremors may be larger than the seismic power amplification factor. Finally, the results of damage diagnosis of fully filled steel tank model indicate that the forms of the Fourier spectra, together with the frequency and power spectral density values, can be directly related to the specific type of structural damage. They show a decrease in the natural frequencies for the model with unscrewed support bolts (global type of damage), while cutting the welds (local type of damage) has resulted in significant increase in values of the power spectral density for higher vibration modes.

• 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