영상장치 센서 데이터 QC에 관한 연구

A study on imaging device sensor data QC

  • 윤동민 (상지대학교 소방공학과) ;
  • 이재영 (상지대학교 도시계획부동산학과) ;
  • 박성식 (상지대학교 도시계획부동산학과) ;
  • 전용한 (상지대학교 소방공학과)
  • Dong-Min Yun (Department of Fire Protection Engineering, Sangji University) ;
  • Jae-Yeong Lee (Department of Urban Planning and Real Estate, Sangji University) ;
  • Sung-Sik Park (Department of Urban Planning and Real Estate, Sangji University) ;
  • Yong-Han Jeon (Department of Fire Protection Engineering, Sangji University)
  • 투고 : 2022.12.09
  • 심사 : 2022.12.31
  • 발행 : 2022.12.31

초록

Currently, Korea is an aging society and is expected to become a super-aged society in about four years. X-ray devices are widely used for early diagnosis in hospitals, and many X-ray technologies are being developed. The development of X-ray device technology is important, but it is also important to increase the reliability of the device through accurate data management. Sensor nodes such as temperature, voltage, and current of the diagnosis device may malfunction or transmit inaccurate data due to various causes such as failure or power outage. Therefore, in this study, the temperature, tube voltage, and tube current data related to each sensor and detection circuit of the diagnostic X-ray imaging device were measured and analyzed. Based on QC data, device failure prediction and diagnosis algorithms were designed and performed. The fault diagnosis algorithm can configure a simulator capable of setting user parameter values, displaying sensor output graphs, and displaying signs of sensor abnormalities, and can check the detection results when each sensor is operating normally and when the sensor is abnormal. It is judged that efficient device management and diagnosis is possible because it monitors abnormal data values (temperature, voltage, current) in real time and automatically diagnoses failures by feeding back the abnormal values detected at each stage. Although this algorithm cannot predict all failures related to temperature, voltage, and current of diagnostic X-ray imaging devices, it can detect temperature rise, bouncing values, device physical limits, input/output values, and radiation-related anomalies. exposure. If a value exceeding the maximum variation value of each data occurs, it is judged that it will be possible to check and respond in preparation for device failure. If a device's sensor fails, unexpected accidents may occur, increasing costs and risks, and regular maintenance cannot cope with all errors or failures. Therefore, since real-time maintenance through continuous data monitoring is possible, reliability improvement, maintenance cost reduction, and efficient management of equipment are expected to be possible.

키워드

과제정보

이 논문은 2020년도 상지대학교 교내 연구비 지원에 의한 것임.

참고문헌

  1. Shin, J.N., Lee, Y.J. "Investigation of Tube Voltage Range using Dose Comparison based on Effective Detector Exposure Index in Chest Radiography", The Korea Society of Radiology, Vol. 15, No. 2, pp. 139-145, 2021
  2. Han, S.T., Kang, H.C., Kim, D.H. "A Study of Data Quality Management and Data Integration", The Korean Data Analysis Society, 2004, Vol. 6, No. 5, pp. 1225-1240, 2004Department of Labor, "Bureau of Labor Statistics", pp. 117-120, 1997.
  3. Kim, H.J., Lee, H.S., Choi, B.J., Kim, Y.H. "Machine Learning-based Quality Control and Error Correction Using Homogeneous Temporal Data Collected by IoT Sensors", Korea Convergence Society, Vol. 10, No. 4, pp. 17-23, 2019
  4. Lee, S.C., Shin, W.S. "A Resource Allocation Model for Data QC Activities Using Cost of Quality", Korean Institute Of Industrial Engineers, Vol. 24, No. 2, pp. 128-138, 2011
  5. Choi, H.R., Lee, S.W., Kim, Y.A., Lee, J.O., Ko, H., Kim, H.C. "The Necessity and Case Analysis of Bigdata Quality Control in Medical Institution", KOREA BIGDATA SOCIETY, Vol. 2, No. 2, pp. 67-74, 2017 https://doi.org/10.36498/kbigdt.2017.2.2.67
  6. Lee, J.H., Kim, J.S. "A study on the construction of the quality prediction model by artificial neural intelligence through integrated learning of CAE-based data and experimental data in the injection molding process", J. Korea Society of Die & Mold Engineering, Vol. 15, No. 4, pp. 24-31, 2021