Electric doors have been applied in urban trains since 2007 and operated for a long time. Recently, the failure of mechanical devices in electric doors have been increasing. The door is a device that is directly related to the safety of passengers. The rivet breakage of a ball/nut assembly may occur to an accident during train operation. In this study, the operating voltage and acceleration data of the door were collected for rivet condition monitoring, and 4 features were extracted in the frequency domain using the acceleration data. The classification performance of the rivet condition according to the axial direction of the acceleration data and 4 kernel functions was evaluated using SVM algorithm. When the X-axis data and Gaussian kernel function were used, the highest classification performance was shown for the electric door’s rivet with 90% accuracy.

In the process of cutting large aircraft parts, the tool may be abnormally worn or damaged due to various factors such as mechanical vibration, disturbances such as chips, and physical properties of the workpiece, which may result in deterioration of the surface quality of the workpiece. Because workpieces used for large aircrafts parts are expensive and require strict processing quality, a maintenance plan is required to minimize the deterioration of the workpiece quality that can be caused by unexpected abnormalities of the tool and take maintenance measures at an earlier stage that does not adversely affect the machining. In this paper, we propose a method to indirectly monitor the tool condition that can affect the machining quality of large aircraft parts through real-time monitoring of the current signal applied to the spindle motor during machining by comparing whether the monitored current shows an abnormal pattern during actual machining by using this as a reference pattern. First, 30 types of tools are used for machining large aircraft parts, and three tools with relatively frequent breakages among these tools were selected as monitoring targets by reflecting the opinions of processing experts in the field. Second, when creating the CNC machining program, the M code, which is a CNC auxiliary function, is inserted at the starting and ending positions of the tool to be monitored using the editing tool, so that monitoring start and end times can be notified. Third, the monitoring program was run with the M code signal notified from the CNC controller by using the DAQ (Data Acquisition) device, and the machine learning algorithms for detecting abnormality of the current signal received in real time could be used to determine whether there was an abnormality. Fourth, through the implementation of the prototype system, the feasibility of the method proposed in this paper was shown and verified through an actual example.

기후변화에 따른 자연재해의 증가하고 있다. 이에 자연재해에 의한 토목구조물의 피해 및 붕괴를 예방하기 위하여 처짐 및 균열을 지속적인 관리가 필요하다. 이에 효과적인 구조물 관리를 위해 광학 이미지 기술이 유지관리 기술에 적용되고 있 다. 하지만 광학이미지 기술은 촬영에 따른 주변 조건의 영향이 크며, 그 때문에 촬영조건에 대한 검증이 필요하다. 이를 위해 본 논문에서 촬영조건으로 자연광, 촬영매수, 촬영거리를 따른 수직변위 추정값의 정확도에 대해 검증하였다. 실험을 통 해 확인한 결과 자연광이 수직변위를 추정하는데 자연광이 가장 큰 영향을 미치는 것을 확인할 수 있었고, 촬영거리 또한 수직변위를 검토하는데 주요한 영향을 미치는 것을 확인할 수 있었다. 본 결과를 통해서 외부환경에서 촬영하는데 활용하여 변위 추정 시 발생하는 오차를 최소화할 수 있으며, 이러한 과정을 통해 구조물 유지관리에 적용할 수 있다.

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