• 한국안전학회지
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• 제20권3호
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• pp.19-26
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• 2005

There are three reasoning method in fault diagnosis process. The shallow reasoning is based on the experiential knowledge and deep reasoning is based on physical model. Hybrid reasoning is mixing two type reasoning. This study describes about fault train embodiment of screw type air compressor that is used widely in industrial facilities by using various experimental method and shallow reasoning. We investigate macroscopic failure cause of air compressor through naked eye observation and then microscopic failure cause by various experimental method. We composed fault train with fault knowledge based on empirical data and scientific data that is acquired through several experiments. It is possible to analysis system reliability and failure rate with these fault train.

• 터널과지하공간
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• 제30권4호
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• pp.320-334
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• 2020

본 수치해석연구에서는 국제공동연구프로젝트 DECOVALEX2019의 Task B의 일환으로 PFC3D를 기반으로한 수리역학연계모델을 개발하여 스위스 Mont Terri 지하연구시설에서 수행된 단층의 유체주입으로 인한 슬립시험을 모사하였다. 이를통해, 개발한 PFC3D 수리역학연계모델이 가진 한계점과 향후 보완할 점을 검토하고자 하였다. PFC3D를 기반으로한 3차원 입자결합모델 내 공극-유동통로모델을 생성하였으며 이를 사용하여 Mont Terri Step 2 단층내 유체주입실험을 모사하였다. 모델링결과 단층대를 따라 주입유체의 유동에 의한 단층대의 변형을 확인하였지만, 관측정에서의 시간에 따른 수압변화는 현장측정치와 부분적으로 일치하는 경향을 확인하였다. 현장측정 관측수압은 초기 유체주입 압력증가에 거의 변화를 보이지 않고 주입수압이 최대치에 도달할때쯤 급격한 증가를 보이는반면, 모델링에서는 주입압력이 증가함에 따라 관측수압도 부드럽게 증가하는 경향을 보였다. 이러한 부분적으로 일치하는 결과의 원인으로는 Mont Terri 현장의 단층을 모사하는 방법에 기인하는 것으로 판단하다. PFC3D에서는 단층을 손상대와 코어균열의 조합으로 모사하였고 단층대의 두께가 약 2 m로 주입유체가 단층대를 통해 유동하도록 모사하였기에 현장에서의 주입유체의 단층내 유동보다 그 유동범위가 크게 모사되었다고 판단한다. 또한, 현장단층에서와 같이 단층내부에 존재하는 충진물질로 인해 단층내 수리유동이 제한되어 국부적으로 과잉공급수압이 형성될 수 있는 기재를 모사하지 못한 점 또한 모델링 결과와 현장측정결과가 부분적으로 일치하는 원인일 수 있다. 단층변형의 경우는 모델링결과와 현장측정결과 유사한 수준으로 일치하는 결과를 확인하였다. 수치모델을 변형하여 단층대의 두께를 감소시키고 단층내 충진 물질의 비균질적인분포를 모사할 수 있는 방법론에 대한 후속 연구를 통해 PFC3D 수리역학연계모델의 유체주입으로 인한 단층활성화 연구로의 적용성을 향상시키는 것을 제안하고 한다.

• 한국자동차공학회논문집
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• 제14권5호
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• pp.1-8
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• 2006

The Steer-By-Wire(SBW) system replaces complex mechanical linkages of the current steering system with electric motors, sensors, and electronic control units. However, the SBW system should guarantee its safety and reliability before commercialization, and therefore, a reliable and robust fault-tolerant technology has to be implemented. This paper proposes a fault-tolerant control algorithm for the SBW system. Based on careful analysis on propagation effects of sensor faults, a reliable fault-tolerant control strategy has been developed. The fault-tolerant controller consists of a fault detection part that monitors and detects faults in the steering wheel and road wheel sensors, and a reconfiguration part that switches to normal sensor signal based on fault detection information. It has been demonstrated by simulation that the proposed algorithm detects sensor faults accurately and enables reliable steering control under various dynamic fault situations.

• KEPCO Journal on Electric Power and Energy
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• 제4권2호
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• pp.89-99
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• 2018

Downtime and malfunction of industrial rotor machines represents a crucial cost burden and productivity loss. Fault diagnosis of this equipment has recently been carried out to detect their fault(s) and cause(s) by using fault classification methods. However, these methods are of limited use in detecting rotor faults because of their hypersensitivity to unexpected and different equipment conditions individually. These limitations tend to affect the accuracy of fault classification since fault-related features calculated from vibration signal are moved to other regions or changed. To improve the limited diagnosis accuracy of existing methods, we propose a new approach for fault diagnosis of rotor machines based on the model generated by supervised learning. Our work is based on feature residual values from vibration signals as fault indices. Our diagnostic model is a robust and flexible process that, once learned from historical data only one time, allows it to apply to different target systems without optimization of algorithms. The performance of the proposed method was evaluated by comparing its results with conventional methods for fault diagnosis of rotor machines. The experimental results show that the proposed method can be used to achieve better fault diagnosis, even when applied to systems with different normal-state signals, scales, and structures, without tuning or the use of a complementary algorithm. The effectiveness of the method was assessed by simulation using various rotor machine models.

• 한국멀티미디어학회논문지
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• 제22권11호
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• pp.1233-1241
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• 2019

The goal of the sound-based mechanical fault diagnosis technique is to automatically find abnormal signals in the machine using acoustic emission. Conventional methods of using mathematical models have been found to be inaccurate due to the complexity of industrial mechanical systems and the existence of nonlinear factors such as noise. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose an automatic fault diagnosis method using discrete wavelet transform and power spectrum density using multi pattern recognition. First, we perform DWT-based filtering analysis for noise cancelling and effective feature extraction. Next, the power spectral density(PSD) is performed on each subband of the DWT in order to effectively extract feature vectors of sound. Finally, each PSD data is extracted with the features of the classifier using multi pattern recognition. The results show that the proposed method can not only be used effectively to detect faults as well as apply to various automatic diagnosis system based on sound.

• Journal of Mechanical Science and Technology
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• 제19권5호
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• pp.1095-1106
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• 2005

Recently, the conventional controller of machine-tool has been increasingly replaced by the PC-based open architecture controller, which is independent of the CNC vendor and on which it is possible to implement user-defined application programs. This paper proposes CNC­implemented fault diagnosis and web-based remote services for machine-tool with open architecture CNC. The faults of CNC machine-tool are defined as the operational faults occupied by over $70{\%}$ of all faults. The operational faults are unpredictable as they occur without any warning. Two diagnostic models, the switching function and the step switching function, were proposed in order to diagnose faults efficiently. The faults were automatically diagnosed through the fault diagnosis system using the two diagnostic models. A suitable interface environment between CNC and developed application modules was constructed for the internal function of CNC. In addition, a suitable web environment was constructed for remote services. The web service functions, such as remote monitoring and remote control, were implemented, and their operability was tested through the web. The results obtained through this research could be a model of fault diagnosis and remote servicing for machine-tool with open architecture CNC.

• 설비공학논문집
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• 제22권10호
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• pp.687-696
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• 2010

In this study, FDD algorithm was developed using the normalized distance method and general pattern classifier method that can be applied to constant air volume air handling unit(CAV AHU) system. The simulation model using TRNSYS and EES was developed in order to obtain characteristic data of CAV AHU system under the normal and the faulty operation. Sensitivity analysis of fault detection was carried out with respect to fault progress. When differential pressure of mixed air filter increased by more than about 105 pascal, FDD algorithm was able to detect the fault. The return air temperature is very important measurement parameter controlling cooling capacity. Therefore, it is important to detect measurement error of the return air temperature. Measurement error of the return air temperature sensor can be detected at below $1.2^{\circ}C$ by FDD algorithm. FDD algorithm developed in this study was found to indicate each failure modes accurately.

• Journal of Mechanical Science and Technology
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• 제20권8호
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• pp.1224-1231
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• 2006

The stability and control derivatives of DURUMI-lI UAV using the flight test are obtained. The flight test data is gathered from the normal flight condition (normal mode) and the flight condition assumed as the right elevator fixed (fault mode). Using real-time parameter estimation techniques, applied to Fourier transform regression method, simulates the aircraft motion. From the result, the fault of control surface is to be detected. In this paper, the results of the real- time parameter estimation techniques are compared with the results of the Advanced Aircraft Analysis (AAA). Using the aerodynamic derivatives, it provides the base line of normal/failure for the control surface by using the on-line parameter estimation of Fourier transform regression. In flight, this approach maybe helpful to detect and isolate the fault of primary control surface. It is explained how to perform the flight condition assumed as the right elevator fixed in the flight test. Also, it is mentioned how to switch between the normal flight condition and the assumed fault flight condition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권5호
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• pp.1186-1207
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• 2024

Bearings are one of the main components of mechanical equipment and one of the primary components prone to faults. Therefore, conducting fault diagnosis on bearings is a key issue in mechanical equipment research. Belief rule base (BRB) is essentially an expert system that effectively integrates qualitative and quantitative information, demonstrating excellent performance in fault diagnosis. However, class imbalance often occurs in the diagnosis task, which poses challenges to the diagnosis. Models with interpretability can enhance decision-makers' trust in the output results. However, the randomness in the optimization process can undermine interpretability, thereby reducing the level of trustworthiness in the results. Therefore, a hierarchical BRB model based on extreme gradient boosting (XGBoost) feature selection with interpretability (HFS-IBRB) is proposed in this paper. Utilizing a main BRB alongside multiple sub-BRBs allows for the conversion of a multi-classification challenge into several distinct binary classification tasks, thereby leading to enhanced accuracy. By incorporating interpretability constraints into the model, interpretability is effectively ensured. Finally, the case study of the actual dataset of bearing fault diagnosis demonstrates the ability of the HFS-IBRB model to perform accurate and interpretable diagnosis.

• 한국소음진동공학회논문집
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• 제16권4호
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• pp.404-413
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• 2006

Induction motors are the workhorse of our industry because of their versatility and robustness. The diagnosis of mechanical load and power transmission system failures is usually carried out through mechanical signals such as vibration signatures, acoustic emissions, motor speed envelope. The motor faults including mechanical rotor imbalances, broken rotor bar, bearing failure and eccentricities problems are reflected in electric, electromagnetic and mechanical quantities. The recent research has been directed toward electrical monitoring of the motor with emphasis on inspecting the stator current of the motor, The stator current spectrum has been widely used for fault detection in induction motor systems. The motor current signature analysis is the useful technique to assess machine electrical condition. This paper describes the motor condition detected by the current signatures Paralleled with vibration signatures analysis of induction motors with the roller bearing and the journal bearing type for large vertical pumps in power plant as examples to discuss for motor fault detection and diagnosis.