Information Center of the First Hospital of Jilin University, Changchun 130021, Jilin, China.
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Comput Intell Neurosci. 2021 Sep 4;2021:6092461. doi: 10.1155/2021/6092461. eCollection 2021.
In recent years, high-precision medical equipment, especially large-scale medical imaging equipment, is usually composed of circuit, water, light, and other structures. Its structure is cumbersome and complex, so it is difficult to detect and diagnose the health status of medical imaging equipment. Based on the vibration signal of mechanical equipment, a PLSR-DNN hybrid network model for health prediction of medical equipment is proposed by using partial least squares regression (PLSR) algorithm and deep neural networks (DNNs). At the same time, in the diagnosis of medical imaging equipment fault, the paper proposes to use rough set to screen the fault factors and then use BP neural network to classify and identify the fault and analyzes the practical application effect of the two technologies. The results show that the PLSR-DNN hybrid network model for health prediction of medical imaging equipment is basically consistent with the actual health value of medical equipment; medical imaging equipment fault diagnosis technology is based on rough set and BP neural network. In the test set, the sensitivity, specificity, and accuracy of medical imaging equipment fault identification are 75.0%, 83.3%, and 85.0%. The above results show that the proposed health prediction method and fault diagnosis method of medical imaging equipment have good performance in health prediction and fault diagnosis of medical equipment.
近年来，高精度医疗设备，特别是大型医学影像设备，通常由电路、水路、光路等结构组成。其结构繁琐复杂，难以检测和诊断医学影像设备的健康状况。本文基于机械设备的振动信号，利用偏最小二乘回归（PLSR）算法和深度神经网络（DNNs），提出了一种用于医疗设备健康预测的 PLSR-DNN 混合网络模型。同时，在医学影像设备故障诊断中，提出利用粗糙集筛选故障因素，再利用 BP 神经网络进行分类识别，并分析了两种技术的实际应用效果。结果表明，用于医学影像设备健康预测的 PLSR-DNN 混合网络模型与医疗设备的实际健康值基本一致；基于粗糙集和 BP 神经网络的医学影像设备故障诊断技术在测试集中，医学影像设备故障识别的灵敏度、特异性和准确率分别为 75.0%、83.3%和 85.0%。以上结果表明，所提出的医学影像设备健康预测方法和故障诊断方法在医疗设备的健康预测和故障诊断方面具有良好的性能。
