• International Journal of Aeronautical and Space Sciences
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• v.9 no.1
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• pp.100-110
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• 2008

It is not easy to monitor and identify all engine faults and conditions using conventional fault detection approaches like the GPA (Gas Path Analysis) method due to the nature and complexity of the faults. This study therefore focuses on a model based diagnostic method using Neural Network algorithms proposed for fault detection on a turbo shaft engine (PW 206C) selected as the power plant for a tilt rotor type unmanned aerial vehicle (Smart UAV). The model based diagnosis should be performed by a precise performance model. However component maps for the performance model were not provided by the engine manufacturer. Therefore they were generated by a new component map generation method, namely hybrid method using system identification and genetic algorithms that identifies inversely component characteristics from limited performance deck data provided by the engine manufacturer. Performance simulations at different operating conditions were performed on the PW206C turbo shaft engine using SIMULINK. In order to train the proposed BPNN (Back Propagation Neural Network), performance data sets obtained from performance analysis results using various implanted component degradations were used. The trained NN system could reasonably detect the faulted components including the fault pattern and quantity of the study engine at various operating conditions.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2007.04a
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• pp.245-249
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• 2007

In this study a model-based diagnostic method using the Neural Network was proposed for PW206C turbo shaft engine and performance model was developed by SIMULINK. Fault and test database to build the NN was obtained at various off-design operating range such as flight altitude, flight Mach number and gas generator rotational speed variation. According to the fault detection analysis results, it was confirmed that the proposed fault detection method could find well the fault of compressor, compressor turbine and power turbine at on-design point as well as off-design point conditions.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.767-774
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• 2011

In recent years, wind turbine system has been considered as the most efficient renewable energy source. Wind turbine system is a complex system which is composed of blade, generator and inverter systems. Recently, lots of researches on fault detection and diagnosis of wind turbine system have been done. Most of them are related with the fault diagnosis of mechanical elements using bivration signal. In this work, a new type of inverter fault detection and diagnstic algorithm is proposed. Furthermore, extensive simulation studies and practical experiments are carried out to verify the proposed algorithm.

• Journal of the Korean Society of Propulsion Engineers
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• v.11 no.3
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• pp.29-34
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• 2007

In this study a model-based diagnostic method using the Neural Network was proposed for PW206C turbo shaft engine and performance model was developed by SIMULINK. Fault and test database to build the NN was obtained at various off-design operating range such as flight altitude, flight Mach number and gas generator rotational speed variation. According to the fault detection analysis results, it was confirmed that the proposed fault detection method could find well the fault of compressor, compressor turbine and power turbine at on-design point as well as off-design point conditions.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.663-668
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• 2024

This paper applies statistical analysis methods in the time domain to the fault diagnosis of wafer transfer robots, and proposes a methodology to discern the critical characteristics of vibration and torque signals. Subsequently, principal component analysis (PCA) is applied to diminish the data's dimensionality, followed by the development of a fault diagnosis algorithm utilizing Euclidean distance and Hotelling's T-square statistics. The algorithm establishes decision boundaries to categorize failure states based on the observed data. Our findings indicate that data classification incorporating velocity parameters enhances diagnostic accuracy. This approach serves to enhance the precision and efficacy of fault diagnosis.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.445-454
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• 2023

Recently, various fault diagnosis studies are being conducted utilizing data from collaborative robots. Existing studies performing fault diagnosis on collaborative robots use static data collected based on the assumed operation of predefined devices. Therefore, the fault diagnosis model has a limitation of increasing dependency on the learned data patterns. Additionally, there is a limitation in that a diagnosis reflecting the characteristics of collaborative robots operating with multiple joints could not be conducted due to experiments using a single motor. This paper proposes an LSTM diagnostic model that can overcome these two limitations. The proposed method selects representative normal patterns using the correlation analysis of vibration and current data in single-axis and multi-axis work environments, and generates residual patterns through differences from the normal representative patterns. An LSTM model that can perform gear wear diagnosis for each axis is created using the generated residual patterns as inputs. This fault diagnosis model can not only reduce the dependence on the model's learning data patterns through representative patterns for each operation, but also diagnose faults occurring during multi-axis operation. Finally, reflecting both internal and external data characteristics, the fault diagnosis performance was improved, showing a high diagnostic performance of 98.57%.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.2041_2042
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• 2009

The diagnostic/prognostic problems for condition based maintenance or Prognostics and Health Management has been used. Primary objectives of diagnosis/prognosis are maximizing system availability and minimizing downtime from fault isolation through more effective troubleshooting efforts. Diagnosis aims to detect the onset of failures to improve system performance and reduce life cycle cost by reducing the failure time. The prognosis can reduce operational and support total ownership cost and improve safety of machinery and complex systems. In this Paper, a fault diagnosis methodology has been described using a monopropellant propulsion system model as a test bench.

• Journal of Electrical Engineering and Technology
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• v.2 no.4
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• pp.461-467
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• 2007

The paper describes a way to diagnose machine condition based on the expert system. In this paper, an expert system-decision tree is built and experimented to diagnose and to detect machine defects. The main objective of this study is to provide a simple way to monitor machine status by synthesizing the knowledge and experiences on the diagnostic case histories of the rotating machinery. A traditional decision tree has been constructed using vibration-based inputs. Some case studies are provided to illustrate the application and advantages of the decision tree system for machine fault diagnosis.

• The Transactions of the Korean Institute of Power Electronics
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• v.18 no.2
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• pp.199-204
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• 2013

Recently, the demand for fuel efficient electric vehicles (EVs) and hybrid electric vehicles (HEVs) has been growing globally. Due to the increased number of switching devices in the electrified vehicles, the probability of the semiconductor device failure is much higher than in other application areas. A sudden failure in one of the power switches and insufficient power management ability in the systems not only decreases system performance, but also leads to critical safety problems. In this paper, novel switch open circuit fault detection method is proposed, and the proposed approach is verified by experiments.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.979-984
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• 2004

The ball bearing having faults generally shows, nonlinear vibration characteristics. For the effective method of fault diagnosis on bail bearing, non-linear diagnostic methods can be used. In this paper, the correlation dimension analysis based on nonlinear timeseries was applied to diagnose the faults of ball bearing. The correlation dimension analysis shows some Intrinsic information of underlying dynamical systems, and clear the classification of the fault of ball bearing.