Abstract
This paper describes an automatic online detecting system. 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. 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 application in the field of industrial nondestructive detection.
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Jin, R., Gao, K., Chen, Z., Dong, C., Zhang, Y., Xi, L. (2007). Fault Detecting Technology Based on BP Neural Network Algorithm. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4693. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74827-4_25
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DOI: https://doi.org/10.1007/978-3-540-74827-4_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74826-7
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