Health Evaluation and Fault Diagnosis of Medical Imaging Equipment Based on Neural Network Algorithm

Abstract

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.

Explainable Intelligent Fault Diagnosis for Nonlinear Dynamic Systems: From Unsupervised to Supervised Learning

“…Due to the increasing demands of safe and economic operations, it is playing an essential role in system performance evaluation and maintenance [1]. Fault diagnosis has a broad spectrum of applications, for instance, ranging from chemical processes [8], electrical systems [9], intelligent transportation [10], aerospace engineering [11] to medical imaging [12].…”

Graded Evaluation of Health Status of Hydraulic System with Variable Operating Conditions Based on Parameter Identification

“…To implement the medical device health status evaluation, ref. [29] proposed a partial least squares regression (PLSR) algorithm combined with deep neural networks (DNNs). Briefly, the knowledge-based method involves massive amounts of data for network training.…”

Assessment and prediction of railway station equipment health status based on graph neural network

“…For the problem of equipment health assessment and prediction, some experts and scholars have conducted indepth studies using traditional machine learning methods, such as dynamic Bayesian networks [1,2], support vector machine regression [3][4][5], hidden Markov models [6][7][8][9], pattern recognition [10], fuzzy sets and rough sets [11][12][13], and so on. In-depth understanding of the mechanism of equipment health status for different application environments can effectively build a relational model through the health indicators of equipment performance degradation to achieve the evaluation and prediction of equipment health status.…”