• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• 2007.11a
• /
• pp.345-349
• /
• 2007

In this study a fuzzy trend monitoring method for detecting the engine mechanical faults was proposed through analyzing performance trends of measurement data. The trend monitoring is an engine conditioning method which can find engine faults by monitoring important measuring parameters such as fuel flow, exhaust gas temperatures, rotational speeds, vibration. etc. Using engine condition data set as a input which generated by linear regression analysis of real engine instrument data, an application of fuzzy logic in diagnostics estimate a cause of fault in each components.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.51 no.9
• /
• pp.413-422
• /
• 2002

In a power plant, disturbance detection and diagnosis are massive and complex problems. Once a disturbance occurs, it can be either persistent, self cleared, cleared by the automatic controllers or propagated into another disturbance until it subsides in a new equilibrium or a stable state. In addition to the Physical complexity of the power plant structure itself, these dynamic behaviors of the disturbances further complicate the fault monitoring and diagnosis tasks. A data structure called a disturbance interrelation analysis graph(DIAG) is proposed in this paper, trying to capture, organize and better utilize the vast and interrelated knowledge required for power plant disturbance detection and diagnosis. The DIAG is a multi-layer directed AND/OR graph composed of 4 layers. Each layer includes vertices that represent components, disturbances, conditions and sensors respectively With the implementation of the DIAG, disturbances and their relationships can be conveniently represented and traced with modularized operations. All the cascaded disturbances following an initial triggering disturbance can be diagnosed in the context of that initial disturbance instead of diagnosing each of them as an individual disturbance. DIAG is applied to a typical cooling water system of a thermal power plant and its effectiveness is also demonstrated.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2000.06a
• /
• pp.1127-1132
• /
• 2000

Impulsive acoustic and vibration signals within rotating machinery are often induced by irregular impacting. The detection of these impulses can be useful for fault diagnosis purposes. Recently there has been an increasing trend towards the use of higher order statistics for fault detection within mechanical systems based on the observation that impulsive signals tend to increase the kurtosis values. This paper considers the use of the third and fourth order Wigner moment spectra, called the Wigner bi- and tri- spectra receptively, for analysing such signals. Expressions for the auto-and cross-terms in these distributions are presented and discussed. It is shown that the Wigner trispectrum is a more suitable analysis tool and it performance is compared to its second order counterpart for detecting impulsive signals. These methods are also applied to measured data sets from an industrial gearbox.

• Proceedings of the KIEE Conference
• /
• 2006.04a
• /
• pp.300-302
• /
• 2006

Middleware offers function that user application program can transmit data independently of network device. Connection management about network connection of module is important for normal service of module base personal robot. Unpredictable network disconnection is influenced to whole robot performance in module base personal robot. For this, Middleware must be offer two important function. The first is function of error detection and reporting about abnormal network disconnection. Therefore, middleware need method for network error detection and module management to consider special quality that each network device has. The second is the function recovering that makes the regular service possible. When the module closed from connection reconnects, as this service reports connection state of the corresponding module, the personal robot resumes the existing service. In this paper proposed method of network connection management for to support fault tolerant about network error of network module based personal robot.

• Proceedings of the KIEE Conference
• /
• 1995.07b
• /
• pp.465-467
• /
• 1995

High impedance fault can not be easily detected by conventional method. But if it would not be detected and cleared quickly, it can result in fires, and electric shock. In this paper, ANN, which has learning capability, is used for high impedance fault detection. The potential of the neural network approach is demonstrated by simulation using KEPCO's measured data. Among ANN models used in this paper, CPN shows better result than BPN in respect of convergence and reliability.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.50 no.5
• /
• pp.211-217
• /
• 2001

This paper proposes a new fault detection criterion logic that extracts the features of magnetizing inrush and internal faults by making use of Daubechies Wavelet Transform which analyzes distinct features. To prove the effectiveness of proposed method, the paper constructs power system model including power transformer by using EMTP, and collects data through simulation using various fault inception angle and magnetizing inrush. The conclusions implemented by the C program and the Wavemenu of MATLAB Toolbos are more effective and simpler to distinguish inter-turn faults from magnetizing inrush states.

• Proceedings of the KIEE Conference
• /
• 1999.07g
• /
• pp.3206-3208
• /
• 1999

This paper describes the remote fault monitoring system for tower parking facilities. This system consists of central station, remote monitoring equipments and communication equipments. The central station is developed under client-server architecture which composed a DB server, a fault detection client, a status collection client and a A/S client. And the remote monitoring systems are connected to central station by LAN using RAS(Remote Access Service) which is constructed PSTN(Public Switched Telephone Network). This system offers real-time fault detection and status data acquisition of tower parking system.

• Proceedings of the KIEE Conference
• /
• 1996.11a
• /
• pp.450-453
• /
• 1996

This research presents a new method of detecting and diagnosing faults of a power plant. Detection of characteristic wave patterns from multichannel instrumentation signals forms the basis of the proposed approach. The dynamics of 500MW drum-type boiler (Boryung coal-fired plant unit #1 and #2) and its control systems are modeled and simulated to generate diverse operation patterns and fault situations and to utilize them for the development of the fault detection algorithms. The results of the boiler system modeling and simulations show a fairly high agreement when compared with some of the actual plant performance test data.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.251-266
• /
• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• 2008.03a
• /
• pp.187-194
• /
• 2008

Because the types and severities of most engine faults are various and complex, it is not easy that the conventional model based fault detection approach like the GPA(Gas Path Analysis) method can monitor all engine fault conditions. Therefore this study proposed newly a diagnostic algorithm for isolating and diagnosing effectively the faulted components of the smart UAV propulsion system, which has been developed by KARI(Korea Aerospace Research Institute), using the fuzzy logic and the neural network algorithms. A precise performance model should be needed to perform the model-based diagnostics. The based engine performance model was developed using SIMULINK. For the work and mass flow matching between components of the steady-state simulation, the state-flow library was applied. The proposed steady-state performance model can simulate off-design point performance at various flight conditions and part loads, and in order to evaluate the steady-state performance model their simulation results were compared with manufacturer's performance deck data. According to comparison results, it was confirm that the steady-state model well agreed with the deck data within 3% in all flight envelop. The diagnosis procedure of the proposed diagnostic system has the following steps. Firstly after obtaining database of fault patterns through performance simulation, then secondly the diagnostic system was trained by the FFBP networks. Thirdly after analyzing the trend of the measuring parameters due to fault patterns, then fourthly faulted components were isolated using the fuzzy logic. Finally magnitudes of the detected faults were obtained by the trained neural networks. Because the detected faults have almost same as degradation values of the implanted fault pattern, it was confirmed that the proposed diagnostic system can detect well the engine faults.