• Journal of KSNVE
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• v.6 no.2
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• pp.205-214
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• 1996

Active noise control(ANC) requires the full capability of a modern digital signal processing module. This paper describes the digital signal processing unit which is designed for ANC of noise fields in passenger car. System hardware is designed to allow software controlled versatility as well as fully qutomatic operation. The developed system is provided with the ability to be self-operated except the case of upload/download of data and program between the personal computer and the system memory. Experimental results are presented to demonstrate ANC performance of noise fields in lightly damped enclosure and passenger car.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2011.04a
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• pp.582-587
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• 2011

Acoustic Emission(AE) is widely used for early detection of faults for rotating machinery in these days because of its high sensitivity. AE signal has to need for transferring to low frequency range for the spectrum analysis included the fault mechanism. In transferring process, we lose a lot of fault information caused by unusable signal processing method. Discrete Wavelet Transform(DWT) is a method of signal processing for AE signatures, but the pattern of its mother function is not optimized with AE signals. So, we can lose the fault information when we want to use the DWT for AE signal. Therefore, in this paper, we will propose a novel pattern for DWT mother function, which is optimized with AE signals. And it will be applied to compare the results of DWT by daubechie and novel pattern.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.9
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• pp.702-708
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• 2002

The spectrum of impulse response signal from an impulse hammer testing is widely used to obtain frequency response function(FRF). However the FRFs obtained from impact hammer testing have not only leakage errors but also finite record length errors when the record length for the signal processing is not sufficiently long. The errors cannot be removed with the conventional signal analyzer which treats the signals as if they are always steady and periodic. Since the response signals generated by the impact hammer are transient and have damping, they are undoubtedly non-periodic. It is inevitable that the signals be acquired for limited recording time, which causes the errors. This paper makes clear the relation between the errors of FRF and the length of recording time. A new method is suggested to reduce the errors of FRF in this paper. Several numerical examples for 1-dof model are carried out to show the property of the errors and the validity of the proposed method.

• The KIPS Transactions:PartB
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• v.18B no.3
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• pp.147-156
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• 2011

This paper proposes a 3-stage (preprocessing, feature extraction, and classification) fault detection and classification algorithm for induction motors. In the first stage, a low-pass filter is used to remove noise components in the fault signal. In the second stage, a discrete cosine transform (DCT) and a statistical method are used to extract features of the fault signal. Finally, a back propagation neural network (BPNN) method is applied to classify the fault signal. To evaluate the performance of the proposed algorithm, we used one second long normal/abnormal vibration signals of an induction motor sampled at 8kHz. Experimental results showed that the proposed algorithm achieves about 100% accuracy in fault classification, and it provides 50% improved accuracy when compared to the existing fault detection algorithm using a cross-covariance method. In a real-world data acquisition environment, unnecessary noise components are usually included to the real signal. Thus, we conducted an additional simulation to evaluate how well the proposed algorithm classifies the fault signals in a circumstance where a white Gaussian noise is inserted into the fault signals. The simulation results showed that the proposed algorithm achieves over 98% accuracy in fault classification. Moreover, we developed a testbed system including a TI's DSP (digital signal processor) to implement and verify the functionality of the proposed algorithm.

• Journal of Broadcast Engineering
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• v.8 no.1
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• pp.37-44
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• 2003

The standard approach to signal resampling is to fit the original image to a continuous model and resample the function at a desired rate. We used the compact B-spline function as the continuous model which produces less oscillatory behavior than other tails functions. In the case of nonuniform resampling based on a B-spline model, the digital signal is fitted to a spline model, and then the fitted signal is resampled at a space varying rate determined by the transformation function. It is simple to implement but may suffer from artifacts due to data loss. The main purpose of this paper is the derivation of optimal nonuniform resampling algorithm. For the optimal nonuniform formulation, the resampled signal is represented by a combination of shift varying splines determined by the transformation function. This optimal nonuniform resampling algorithm can be verified from the experiments that It produces less errors.

• Journal of the Society of Naval Architects of Korea
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• v.41 no.4
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• pp.77-84
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• 2004

Active control is regarded as one of the most efficient and economic countermeasures to reduce excessive vibration of ship superstructure. However, it is difficult to find its practical application in real ships in spite that many studies on such systems have been done. In this study, for the practical use of an active control system to reduce ship superstructure vibration, we have developed an active vibration compensator consisting of a mechanical actuator having compact size and expected lifetime over 20 years, its control panel including exclusive signal processing and computing board, sensors to detect phase and vibration, and its operation software providing various user-interface functions. From the performance verification test of the system at a 5,500 TEU container carrier, we have confirmed the system could reduce ship superstructure vibration of a harmonic component of main engine rotating frequency up to 0.1 mm/s.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.5
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• pp.829-833
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• 2007

In this paper, the RCF method is applied to analyze small signal stability of power systems including GTO controlled parallel FACTS equipments such as STATCOM. To apply the RCF method in power system small signal stability problems, state transition equations of generator, controllers and STATCOM are presented. In eigenvalue analysis of power systems, STATCOM is modelled as the equivalents voltage source model and the PWM switching circuit model. As a result of simulation, the RCF method is very powerful to calculate the oscillation modes exactly after the switching operations, and useful to analyze the small signal stability of power systems with periodically operated switching devices such as STATCOM.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.718-723
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• 2008

Bearing is one of the important mechanical elements used in various industrial equipments. Most of failures occurred during the equipment operation result from bearing defects and breakages. Therefore, monitoring of bearings is essential in preventing equipment breakdowns and reducing unexpected loss. The purpose of this paper is to present an online monitoring method to predict bearing states using vibration signals. Bearing vibrations, which are collected as a form of profile signal, are first analyzed by a discrete wavelet transform. Next, some statistical features are obtained from the resultant wavelet coefficients. In order to select significant ones among them, analysis of variance (ANOVA) is employed in this paper. Statistical features screened in this way are used as input variables to support vector machine (SVM). An hierarchical SVM tree is proposed for dealing with multi-class problems. The result of numerical experiments shows that the proposed SVM tree has a competent performance for classifying bearing fault states.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.6
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• pp.551-558
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• 2013

A support vector machine (SVM) is a very powerful classification algorithm when a set of training data, each marked as belonging to one of several categories, is given. Therefore, SVM techniques have been used as one of the diagnostic tools in machine learning as well as in pattern recognition. In this paper, we present the results of classifying ball bearing fault types and severities using SVM with an optimized feature set based on the minimum distance rule. A feature set as an input for SVM includes twelve time-domain and nine frequency-domain features that are extracted from the measured vibration signals and their decomposed details and approximations with discrete wavelet transform. The vibration signals were obtained from a test rig to simulate various bearing fault conditions.