Abstract

Info

Title:: Fault diagnosis for equipment circuit board based on data drive and no circuit drawing

Author(s):: LIU Xiangjun; CHONG Yinbao; XIAO Jingjing; ZHAO Peng; ZHANG Shihui; Department of Medical Engineering, the Second Affiliated Hospital of Army Medical University, Chongqing 400037, China

Keywords:: Keywords: medical equipment circuit board fault diagnosis long short-term memory

Abstract:: Abstract: Considering the complicated design of medical equipment and the problems of traditional fault diagnosis methods such as the high cost and difficulties of maintenance due to the over-reliance on technical data such as circuit drawings, as well as the personal technical experience of maintenance specialists, an intelligent fault diagnosis method based on data drive and no circuit drawing is proposed. Under the premise of unknown circuit drawing information and the working principle of the circuit board, different fault states of the circuit board were simulated, and the electrical signals of each external interface pin were collected as the original fault data which were then preprocessed and divided into training set and test set. Machine learning method was used to construct an intelligent fault diagnosis model based on a single-layer long short-term memory network. The model training was carried out by Python programming, and the accuracy curve and loss curve of training progress were output. The result showed that the proposed method realized the diagnosis and classification of circuit board faults with high efficiency and strong reliability, and the accuracy reached 89.99%.