• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• 2006.11a
• /
• pp.281-284
• /
• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV (Unmanned Aerial Vehicle) which has been developed by KARI (Korea Aerospace Research Institute). The measurement parameters of Smart UAV propulsion system are gas generator rotational speed, power turbine rotational speed, exhaust gas temperature and torque. But two measurement such as compressor exit pressure and compressor turbine exit temperature were added because they were difficult each component diagnostics using the default measurement parameter. The performance parameters for the estimate of component performance degradation degree are flow capacities and efficiencies for compressor, compressor turbine and power turbine. Database for network learning and test was constructed using a gas turbine performance simulation program. From application results for diagnostics of the PW206C turboshaft engine using the learned networks, it was confirmed that the proposed diagnostics could detect well the single fault types such as compressor fouling and compressor turbine erosion.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.10a
• /
• pp.224-227
• /
• 2015

Data transmission via the car driver's tethered smart phone may have a volume-dependent billing in case car driver' phone transmits data in real-time to the remote data center. The on-board diagnosis data generated are temporary stored locally to mobile remote diagnosis application on the car driver's phone, and then transmit to the data center later when car driver connects to the Internet. To increase the easiest of using the remote vehicle application without blocking other tasks to be executing on the cloud, node.js stands as a suitable candidate for handling tasks of data storage on the cloud via mobile network. We demonstrate the effectiveness of the proposed architecture by simulating a preliminary case study of an android application responsible of real time analysis by using a vehicle-to- smart phones applications interface approach that considers the smart phones to act as a remote user which passes driver inputs and delivers output from external applications. In this paper, we propose a study on development of Remote Vehicle fault diagnostic system features web server architecture based event loop approach using node.js platform, and wireless communication to handle vehicle diagnostics data to a data center.

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

• Journal of KSNVE
• /
• v.10 no.2
• /
• pp.331-337
• /
• 2000

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. Many research has been conducted to manipulate field vibration signal data for diagnosing the fault of designated machinery. As the pattern recognition tool of that signal, neural network which use usually back-propagation algorithm was used in the diagnosis of rotating machinery. In this paper, self-organizing feature map(SOFM) which is unsupervised learning algorithm is used in the abnormal defect diagnosis of rotating machinery and then learning vector quantization(LVQ) which is supervised learning algorithm is used to improve the quality of the classifier decision regions.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.2192-2197
• /
• 2005

This paper describes how to diagnose SBR plant equipment using time-series data mining. It shows the equipment diagnostics based upon vibration signals that are acquired from each device for process control. Data transform techniques including two data preprocessing skills and data mining methods were employed in the data analysis. The proposed method is not only suitable for SBR equipment, but is also suitable for other industrial devices. The experimental results performed on a lab-scale SBR plant show a good equipment-management performance.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2003.05a
• /
• pp.932-937
• /
• 2003

When oil pressure of damper is lost or relative stiffness of spring drops in vibration system, it can be fatally dangerous situation. A fault diagnosis method for vibration system using Support Vector Machine(SVM)is suggested in the paper. SVM is used to classify input data or applied to function regression. System status can be classified by judging input data based on optimal separable hyperplane obtained using SVM which learns normal and abnormal status. It is learned from the relationship of system state variables in term of spring, mass and damper. Normal and abnormal status are learned using phase plane as in put space, then the learned SVM is used to construct algorithm to predict the system status quantitatively

• 제어로봇시스템학회:학술대회논문집
• /
• 1991.10b
• /
• pp.1705-1711
• /
• 1991

On-line fault detection and diagnosis has an increasing interest in a chemical process industry, especially for a process control and automation. The chemical process needs an intelligent operation-aided workstation which can do such tasks as process monitoring, fault detection, fault diagnosis and action guidance in semiautomatic mode. These tasks can increase the performance of a process operation and give merits in economics, safety and reliability. Aiming these tasks, series of researches have been done in our lab. Main results from these researches are building appropriate knowledge representation models and a diagnosis mechanism for fault detection and diagnosis in a chemical process. The knowledge representation schemes developed in our previous research, the symptom tree model and the fault-consequence digraph, showed the effectiveness and the usefulness in a real-time application, of the process diagnosis, especially in large and complex plants. However in our previous approach, the diagnosis speed is its demerit in spite of its merits of high resolution, mainly due to using two knowledge models complementarily. In our current study, new knowledge representation scheme is developed which integrates the previous two knowledge models, the symptom tree and the fault-consequence digraph, into one. This new model is constructed using a material balance, energy balance, momentum balance and equipment constraints. Controller related constraints are included in this new model, which possesses merits of the two previous models. This new integrated model will be tested and verified by the real-time application in a BTX process or a crude unit process. The reliability and flexibility will be greatly enhanced compared to the previous model in spite of the low diagnosis speed. Nexpert Object for the expert system shell and SUN4 workstation for the hardware platform are used. TCP/IP for a communication protocol and interfacing to a dynamic simulator, SPEEDUP, for a dynamic data generation are being studied.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2013.10a
• /
• pp.318-319
• /
• 2013

• Journal of Electrical Engineering and Technology
• /
• v.2 no.3
• /
• pp.353-357
• /
• 2007

The bearing diagnostics method is presented in this paper using fuzzy inference based on vibration data. Both time-domain and frequency-domain features are used as input data for bearing fault detection. The Adaptive Network based Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA) have been proposed to select the fuzzy model input and output parameters. Training results give the optimized fuzzy inference system for bearing diagnosis based on measured vibration data. The result is also tested with other sets of bearing data to illustrate the reliability of the chosen model.

• 제어로봇시스템학회:학술대회논문집
• /
• 1987.10a
• /
• pp.836-841
• /
• 1987

Many kinds of conditioning monitoring technique have been studied, so this study has investigated the possibility of checking the trend in the fault diagnosis of ball bearing, one of the elements of rotating machine, by applying the cepstral analysis method using the adaptive noise cancelling (ANC) method. And computer simulation is conducted in oder to identify obviously the physical meaning of ANC. The optimal adaptation gain in adaptive filter is estimated, the performance of ANC according to the change of the signal to noise ratio and convergence of LMS algorithm is considered by simulation. It is verified that cepstral analysis using ANC method is more effective than the conventional cepstral analysis method in bearing fault diagnosis.