Failure diagnoses on large diesel engine are commonly detected when a deviation or fluctuation in its temperature, pressure, vibration or noise set parameter limits arises. These parameters can be easily monitored and can provide information of the engine’s present state depending on external environment and operating conditions. On the other hand, long term monitoring and condition management can be interfaced into the engine’s existing operating system. The approach is seen to keep track of monitored machines’ status using resonance and vibration amplitude. In particular, these signals will be able to identify complex vibration characteristic pertaining to such as engine torque output and support mounts. In this paper, a basic research for large diesel engine diagnosis was carried-out. The failure diagnosis collects and monitors the vibration state time history by using various vibration signals with reference to ISO 13373-1. Further, this monitoring system in the field of large diesel engines has not been applied practically and the results of this study are presented herein.

In the wire constituting the LIN, which is one of the vehicle communication devices, disconnection of the wire or contact resistance of the circuit occurs due to vibration and aging of the vehicle. This affects the entire communication network and may have a significant impact on the safe driving of the vehicle as information is not transmitted. In this study, a LIN BUS circuit simulator was built on its own like a real car and measured with an automotive oscilloscope instead of a common method of measuring and diagnosing a circuit with a multimeter in the event of a LIN BUS circuit failure. Referring to the experimental results, it will be possible to diagnose faults in circuits efficiently and quickly.

In this paper, each controller's unique ID and PICO oscilloscope were used to measure the voltage waveform of each CAN communication line, and compare and analyze the serial decoding results. Using the voltage change level of the CAN communication line, it was possible to check whether the CAN-High line and the Low line were disconnected. And it was possible to infer the circuit disconnection point between the controller and the controller only with the unique ID information of each controller. And when the CAN-High circuit was disconnected, the voltage of the high line was measured at the same voltage level as the Low line.

Industrial Motors diagnostic equipment is highly dependent on the automation system, so if there are defects in the automation equipment, it can only rely on the operator’s intuitive judgment.To help with intuitive judgment, Park’s Vactor Approach(PVA) represents the current signal as a pattern of circles, so it can tell if a fault occurs when the circle is distorted. However, the failure to judge the degree of distortion of the circle pattern is the basis of the fault, so it will face difficulties. In this paper, in order to compare the faults of PVA, the period of d-axis current of PVA pulsation was mastered, so that two phase differences occurred in the same signal source. Through experiments, it is confirmed that this is a 90 degree cross formation of PVA, which is convenient for judging from the vision that there is no fault, thus helping the operator to make intuitive judgment.

The maintenance part of the CAN Bus Networks failure is very difficult due to the difficulty of the inspection and the variety of the cause of the failure. In this study, first, diagnosis process is classified into diagnosis process of non-communication in total and diagnostic process of partial communication failure. Second, based on the past fault code, we analyze the fault by analyzing the nearest fault by analyzing the faults through the array and diagram. And, if the past failure codes can not be analyzed, the CAN logger is used to collect data. After that, it is confirmed that the fault diagnosis can be performed effectively by analyzing and checking it. Through this, troubleshooting by the diagnostic process is suggested and the direction of troubleshooting that the field technicians can use for diagnosis and inspection of CAN Bus Networks is presented.

In order to reduce damages to major railroad components, which have the potential to cause interruptions to railroad services and safety accidents and to generate unnecessary maintenance costs, the development of rolling stock maintenance technology is switching from preventive maintenance based on the inspection period to predictive maintenance technology, led by advanced countries. Furthermore, to enhance trust in accordance with the speedup of system and reduce maintenances cost simultaneously, the demand for fault diagnosis and prognostic health management technology is increasing. The objective of this paper is to propose a highly reliable learning model using various machine learning algorithms that can be applied to critical rolling stock components. This paper presents a model for railway rolling stock component fault diagnosis and conducts a mechanical failure diagnosis of motor components by applying the machine learning technique in order to ensure efficient maintenance support along with a data preprocessing plan for component fault diagnosis. This paper first defines a failure diagnosis model for rolling stock components. Function-based algorithms ANFIS and SMO were used as machine learning techniques for generating the failure diagnosis model. Two tree-based algorithms, RadomForest and CART, were also employed. In order to evaluate the performance of the algorithms to be used for diagnosing failures in motors as a critical railroad component, an experiment was carried out on 2 data sets with different classes (includes 6 classes and 3 class levels). According to the results of the experiment, the random forest algorithm, a tree-based machine learning technique, showed the best performance.

The air conditioner is an instrument to make a low degree fresh wind by compress a refrigerant and through the heat exchanger. As change a refrigerant of low degree and low pressure to high degree and high pressure, it is needed to the compressor. The compressor compress a refrigerant by transfer the rotational energy to reciprocating energy. The goal of this research is for easy use to failure diagnosis of an automotive compressor according to vibration characteristics of each failure types of automotive compressor. To do this, we have prepared the normal products and made some detective products which several kinds of detective types to measure and analysis of vibration characteristics. From the results, we have made a close investigation into the vibration characteristics of each kinds of detective type.

모델기반 고장 진단법은 풍력발전기의 상태감지 시스템(Condition monitoring system)에 적용을 목적으로 변동 하중조건하에서 작동하는 기어박스내의 기어의 균열을 진단하기 위해 제시하였다. 두 개의 평기어(Spur gear)로 구성된 간단한 테스트 베드가 위의 접근방법을 검증하기 위해 구축되었고, 기어의 균열은 기어의 뿌리부분에 균열을 인가하여 묘사하였다. 축의 회전속도에 독립적인 타코미터를 기반으로 한 오더분석(Order analysis)을 균열크기 진단에 적용하였고, 테스트 베드의 작동을 시뮬레이션하기 위해 집중변수 모델(Lumped parameter dynamic model)이 사용되었다. 모델에서 균열과 밀접히 관련된 변수는 측정된 신호와 시뮬레이션된 신호 간의 차이를 최소화하는 최적화 기법으로 역추정하였다. 제시한 방법의 유효성을 보이기 위해, 미리 정의된 모델 변수로부터 생성된 시뮬레이션 신호를 테스트-베드로부터 측정된 신호로 가정하고, 제시한 방법을 사용하여 변수를 역추정하였다. 결과는 실제 값과 일치하였고, 이를 통해 알고리즘이 제대로 작동함을 알 수 있었다. 다음 연구에서는 실제 테스트 베드의 실제균열에 적용하고자 한다.

본 논문은 상대적으로 새로운 기법인 Parzen Density Estimation과 Multi-class SVM을 이용한 지능형 고장 탐색과 진단 방법을 제안하고 있다. 본 연구에서는 롤링 베어링을 대상으로 고장을 탐색하고 진단하기 위한 방법을 제안하는데 Parzen Density Estimation과 Multi-class SVM은 고장 클래스를 잘 표현할 수 있다. Parzen Density Estimation은 새로운 패턴 데이터의 거절과 알려진 데

This study deals with the application of knowledge-based engineering and a methodology for the assessment & measurement of reliability, availability, maintainability, and safety of industrial systems using fault-tree representation. A fuzzy methodology for fault-tree evaluation seems to be an alternative solution to overcome the drawbacks of the conventional approach. To improve the quality of results, the membership functions must be approximated based on heuristic considerations. Conventionally, it is not always easy to obtain a system reliability for components with different individual failure probability density functions(p.d.f.), We utilize fuzzy set theory to solve the adequacy of the conventional probability in accounting and processing of built-in uncertainties in the probabilistic data. The purpose of this study is to propose the framework of knowledge-based engineering through integrating the various sources of knowledge involved in a FTA.

This paper describes "the remote automatic diagnosis and repair system which automatically reads the problems such as "out of order" occurred on equipment at customer's equipment from a remote computer center using HTTP(hyper text transfer protocol). It shows the scheme of the network configurations and features of the system. In addition, a way to implement the overall system, the specific functions of unit, and the operational specifications between the center's computer and customer's computer are also presented.also presented.