A non-conventional quality control system to detect surface faults in mechanical front seals

E-quality control: A support vector machines approach

“…Multi-class support vector machines were used for the fault diagnostics of roller bearing using kernel based neighborhood score multi-class support vector machine, where it was shown the multi-class SVM was effective in diagnosing the fault conditions and the results were comparable with binary SVM [14]. Artificial neural networks were used for addressing quality control issue as a non-conventional way to detect surface faults in mechanical front seals, which achieved good results in comparison with the deterministic system which was already implemented [15]. Fuzzy association rules were used to develop an intelligent quality management approach with the research providing a generic methodology with knowledge discovery and the cooperative ability for monitoring the process effectively and efficiently [16].…”

Detection and classification of surface defects of gun barrels using computer vision and machine learning

“…Visual inspection of surface has been carried out on a vast variety of products in past few years like front seals [3] and PU packing [4]. Various segmentation methods for detecting defects have been proposed and implemented successfully [5,6].…”

A Novel Learning Algorithm of Back-Propagation Neural Network

“…Back-propagation neural network (BPNN) is one of mostly used neural networks and has been applied to many fields, such as: pattern classification, pattern recognition, self-adaptive control system, diagnosis of medical aspects or machine faults [1][2][3][4]. Traditional BPNN adopts gradient descent algorithm (GDA) which results in some problems of local minimum in learning, bad convergence, instability and long training time.…”