基于神经网络算法的故障检测技术

针对复杂的机电产品内部构件状态检测这一工程难题,本文介绍了一种自动在线检测系统.该系统采用X射线对产品成像,运用数字图像处理技术对射线图像进行预处理,由神经网络算法进行故障诊断.故障识别模型采用了改进的BP神经网络算法,以正常装配状态时的多幅图像经预处理后作为学习样本训练BP神经网络.检测时一般只需拍摄两幅不同方位的图像,经预处理后输入神经网络与样本图像进行比较判断,即可识别出关键元器件的状态.该系统将数字射线成像技术和图像处理技术相结合,并在故障识别算法中采用了神经网络算法,提高了产品故障的检测速度和可靠性,在工业无损检测领域具有一定的实用性.

Fault detecting technology based on neural network algorithm

The paper describes an automatic on_line detecting system which can detect inner parts of complex electromechanical products. In the system,digital image processing technology is used to preprocess X_ray images of the products, and neural network algorithm is applied to diagnose faults. The fault recognition model adopts an improved back_propagating neural network, which is trained by a series of standard X_ray images of correctly assembled products.During the process of detection, two images of objects in different directions are capable of acquiring the status of the key parts. After comparing the features of the two preprocessed images and standard images,the network can estimate different types of faults of the key parts. The detecting system combines digital radiography technology with digital image processing, and applies the back_propagating neural network algorithm in the fault recognition process. The system improves the speed and reliability of fault detection and has practicability in the field of industrial nondestructive detection.