• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.7
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• pp.556-560
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• 2008

As higher reliability and accuracy on production facilities are required to detect incipient faults, a diagnostic system for predictive maintenance of the facility is highly recommended. In this paper, we present a study on the application of vibration signals to diagnose faults for a Rotating Machinery using the Mahalanobis Distance-Taguchi System. RMS, Crest Factor and Kurtosis that is known as the Statistical Methods and the spectrum analysis are used to diagnose faults as parameters of Mahalanobis distance.

• Proceedings of the KIPE Conference
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• 2001.12a
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• pp.190-193
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• 2001

This paper describes the reduction of vibration and position error of Linear Pulse Motor(LPM) with the control method of PWM duty ratio by using Motion Controller in LPM. The LPM is operated on micro-stepping drive, that the linear scale is sequently returned the position signals of a micro-meter And next micro-step is controlled with the scale factor when position error is occurred. The scale factor is experimently acquired.

• Journal of Power Electronics
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• v.8 no.1
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• pp.101-107
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• 2008

In this paper, the features extracted from vibration time signals are used to detect the bearing fault condition. The decision tree is applied to diagnose the bearing status, which has the benefits of being an expert system that is based on knowledge history and is simple to understand. This paper also suggests a genetic algorithm (GA) as a method to reduce the number of features. In order to show the potentials of this method in both aspects of accuracy and simplicity, the reduced-feature decision tree is compared with the non reduced-feature decision tree and the PCA-based decision tree.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.455-460
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• 2007

A self-sensing circuit for piezo inkjet has been designed in order to monitor the operating condition during printing. In order to verify the circuit, both ink droplet images from strobe LED and vibration signals from the laser vibrometer were measured and compared with self-sensing signal. Experimental results show that self-sensing signal was effective in detecting the pressure wave change due to the bubble trapped in inkjet printhead.

• Journal of Mechanical Science and Technology
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• v.19 no.3
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• pp.846-859
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• 2005

Support vector machines (SVMs) have become one of the most popular approaches to learning from examples and have many potential applications in science and engineering. However, their applications in fault diagnosis of rotating machinery are rather limited. Most of the published papers focus on some special fault diagnoses. This study covers the overall diagnosis procedures on most of the faults experienced in rotating machinery and examines the performance of different SVMs strategies. The excellent characteristics of SVMs are demonstrated by comparing the results obtained by artificial neural networks (ANNs) using vibration signals of a fault simulator.

• Transactions of the Korean Society of Automotive Engineers
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• v.15 no.5
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• pp.56-62
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• 2007

In order to compare subjective and objective assessments, a passenger car was driven at several speeds over several road profiles. To measure the acceleration signals experienced by the seated subject who provided an subjective assessment, four triaxial translational accelerometers and one triaxial gyro sensor were mounted on the steering wheel and on the passenger seat and floor, respectively. Correlations were determined between the measured accelerations and the subjective assessments of 3 expert subjects and 9 general subjects using psychophysical power law.

• International Journal of Safety
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• v.3 no.1
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• pp.10-14
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• 2004

Apar The dynamic characteristics of the current collection system are evaluated during a test run. Signals from accelerometers attached to the pantograph assembly are acquired through a measurement system and analyzed. It is found that the train speed significantly influences the magnitude and frequency characteristics of the pantograph motion. The major frequency components of interest are found to be frequency components originating from the motion of the train along the catenary as well as the several resonance frequencies of the structural vibration of the pantograph. The contact force is also calculated by assuming the pantograph panhead as a rigid structure.

• Smart Structures and Systems
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• v.6 no.4
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• pp.389-403
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• 2010

By using image recognition technology, this paper presents a new fault diagnosis method for rotating machinery with artificial immune algorithm. This method focuses on the vibration state parameter image. The main contribution of this paper is as follows: firstly, 3-D spectrum is created with raw vibrating signals. Secondly, feature information in the state parameter image of rotating machinery is extracted by using Wavelet Packet transformation. Finally, artificial immune algorithm is adopted to diagnose rotating machinery fault. On the modeling of 600MW turbine experimental bench, rotor's normal rate, fault of unbalance, misalignment and bearing pedestal looseness are being examined. It's demonstrated from the diagnosis example of rotating machinery that the proposed method can improve the accuracy rate and diagnosis system robust quality effectively.

• Smart Structures and Systems
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• v.12 no.6
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• pp.679-694
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• 2013

In this paper the application of the Interpolation Damage Detection Method to the numerical model of a suspension bridge instrumented with a network of Micro-Electro-Mechanical System sensors is presented. The method, which, in its present formulation, belongs to Level II damage identification method, can identify the presence and the location of damage from responses recorded on the structure before and after a seismic damaging event. The application of the method does not require knowledge of the modal properties of the structure nor a numerical model of it. Emphasis is placed herein on the influence of recorded signals noise on the reliability of the results given by the Interpolation Damage Detection Method. The response of a suspension bridge to seismic excitation is computed from a numerical model and artificially corrupted with random noise characteristic of two families of Micro-Electro-Mechanical System accelerometers. The reliability of the results is checked for different damage scenarios.

• Earthquakes and Structures
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• v.15 no.3
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• pp.263-273
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• 2018

Operational modal analysis is being widely used in aerospace, mechanical and civil engineering. Common research fields include optimal design and rehabilitation under dynamic loads, structural health monitoring, modification and control of dynamic response and analytical model updating. In many practical cases, influence of noise contamination in the recorded data makes it difficult to identify the modal parameters accurately. In this paper, an improved frequency domain method called Enhanced Least Square Complex Frequency (eLSCF) is developed to extract modal parameters from noisy recorded data. The proposed method makes the use of pre-defined approximate mode shape vectors to refine the cross-power spectral density matrix and extract fundamental frequency for the mode of interest. The efficiency of the proposed method is illustrated using an example five story shear frame loaded by random excitation and different noise signals.