• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.321-325
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• 2005

These days, it is needed that more stability and reliability of Diesel engine. So it is essential that a systematic and comprehensive fault diagnosis analysis technology. this technology makes fault diagnosis analysis system more efficient. Expert System is required to make fault diagnosis analysis system. In this paper, fault and early diagnosis system is implemented to use Expert System development tools.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.571-578
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• 2018

The deteriorated pipline often causes the unexpected leakage and crack. Negligence and late maintenance leads the enormous damage for gas and water resource. This paper proposes early fault detection and diagnosis algorithm for pipeline using acoustic emission (AE) signals. Early fault detection method for pipeline compares the frequency amplitude of the spectrum to that of the spectrum in normal condition. Larger amplitude of the spectrum indicates abnormal condition. Early fault diagnosis algorithm uses support vector machines (SVM), which is trained for normal and abnormal conditions to diagnose the measured AE signal from the target pipeline. In the experiment, a pipeline testbed is constructed similarly to real industrial pipeline. Normal, 5mm cracked, 10mm holed pipelines are installed and tested in this study. The proposed fault detection and diagnosis technique is validated as an efficient approach to detect early faulty condition of pipeline.

• Advances in Computational Design
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• v.8 no.3
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• pp.255-271
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• 2023

The gear drive of a combat aircraft engine is responsible for power transmission to the different accessories necessary for the engine's operation. Incorrect power transmission can occur due to the presence of failure modes in the gears like bending fatigue, pitting, adhesive wear, scuffing, abrasive wear and polished wear etc. Fault diagnosis of the gear drive is necessary to get an early indication of failure of the gears. The present research is to develop an algorithm using different vibration signal processing techniques on industrial vibration acquisition systems to establish gear fault diagnosis architecture. The signal processing techniques have been used to extract various feature vectors in the development of the fault diagnosis architecture. An open-source dataset of other gear fault conditions is used to validate the developed architecture. The results is a basis for development of artificial intelligence based expert systems for gear fault diagnosis of a combat aircraft engine.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.10 s.175
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• pp.80-89
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• 2005

In this paper, time-frequency analysis using wavelet packet transform and advanced-MDSA (Multiple Dimensional Spectral Analysis) which based on wavelet packet transform is applied fur fault source identification and diagnosis of early detection of fault non-stationary sound/vibration signals. This method is analyzing the signal in the plane of instantaneous time and instantaneous frequency. The results of ordinary coherence function, which obtained by wavelet packet analysis, showed the possibility of early fault detection by analysis at the instantaneous time. So, by checking the coherence function trend, it is possible to detect which signal contains the major fault signal and to know how much the system is damaged. Finally, It is impossible to monitor the system is damaged or undamaged by using conventional method, because crest factor is almost constant under the range of magnitude of fault signal as its approach to normal signal. However instantaneous coherence function showed that a little change of fault signal is possible to monitor the system condition. And it is possible to predict the maintenance time by condition based maintenance for any stationary or non-stationary signals.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.5
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• pp.612-619
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• 2013

This paper proposes an early diagnosis technique for the stator-turn fault (STF) in an interior permanent magnet (IPM)-type brushless DC (BLDC) motor using the impedance parameter. We have analyzed the varying characteristics owing to the STF through various experiments and the finite element method (FEM). As a result, we have presented a simple method for fault detection. This technique can be applied without requiring a fast Fourier transform (FFT) and the calculation of the negative-sequence impedance. The fault detection system works on the basis of the comparison the measured impedance with the database impedance. The variations in the characteristics owing to the STF as well as the proposed technique have been verified through the simulation and experiment.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.57-64
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• 2024

In the industrial field, robots are used to increase productivity by replacing labors with dangerous, difficult, and hard tasks. However, failures of individual industrial robots in the entire production process may cause product defects or malfunctions, and may cause dangerous disasters in the case of manufacturing parts used in automobiles and aircrafts. Although requirements for early diagnosis of industrial robot failures are steadily increasing, there are many limitations in early detection. This paper introduces methods for diagnosing robot failures using sound-based data and deep learning. This paper also analyzes, compares, and evaluates the performance of failure diagnosis using various deep learning technologies. Furthermore, in order to improve the performance of the fault diagnosis system using deep learning technology, we propose a method to increase the accuracy of fault diagnosis based on an inference window. When adopting the inference window of deep learning, the accuracy of the failure diagnosis was increased up to 94%.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.1114-1118
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• 2000

Impulsive sound and vibration signals in gear are often associated with their faults. Thus these impulsive sound and vibration signals can be used as indicators in the diagnosis of gear fault. The early detection of impulsive signal due to gear fault prevents from complete failure in gear. However it is often difficult to make objective measurement of impulsive signals because of background noise signals. In order to ease the detection of impulsive signals embedded in background noise, we enhance the impulsive signals using adaptive signal processing.

• Tribology and Lubricants
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• v.14 no.3
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• pp.74-80
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• 1998

The morphologies of the wear particles are directly indicative of wear process occuring in the machine. The analysis of wear particle morphology can therefore provide very early detection of a fault and can also ofen facilitate a dignosis. For this work, the neural network was applied to identify friction coefficient through four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) of wear debris generated from the machine. The averages of these parameters were used as inputs to the network. It is shown that collect identification of friction coefficient depends on the ranges of these shape parameters learned. The various kinds of the wear debris had a different pattern characteristics and recognized relation between the friction condition and materials very well by neural network. We discuss how the network determines difference in wear debris feature, and this approach can be applied for machine condition monitoring and fault diagnosis.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.2
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• pp.72-77
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• 2011

In this paper, we propose effective diagnosis algorithm for hub bearing fault in driving vehicle using acceleration signal and wheel speed signal measured in hub bearing unit or knuckle. This algorithm consists of differential, envelope and power spectrum method. We developed diagnosis system for realizing proposed algorithm. This system consists of input device including acceleration sensor and wheel speed sensor, calculation device using Digital Signal Processor (DSP) and display device using Personal Digital Assistant (PDA). Using this diagnosis system, a driver can see hub bearing fault(flaking) from the vibration in driving vehicle. With early repairing, he can keep good ride feeling and prevent accident of vehicle resulting from hub bearing fault.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.549-555
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• 2003

The technology of machine condition monitoring is used effectively to detect the machine faults at an early stage using different machine quantities, such as current, voltage, temperature and vibration. Induction motors are most widely used to drive pumps, compressors and fans in industrial drives. This paper presents approach to data fusion using Dempster-Shafer theory because only one technique has uncertainty. So we can obtain advanced accuracy of the machine fault diagnosis. Vibration and current quantities are applied to diagnose three-phase induction motor.