Application of deep belief networks for precision mechanism quality inspection

Abstract

Precision mechanism is widely used for various industry applications. Quality inspection for precision mechanism is essential for manufacturers to assure the product leaving factory with expected quality. In this paper, we propose a novel automated fault detection method, named Tilear, based on a Deep Belief Network (DBN) auto-encoder. DBN is a probabilistic generative model, composed by stacked Restricted Boltzmann Machines. With its RBMlayer- wise training methods, DBN can perform fast inference and extract high level feature of the inputs. By unfolding the stacked RBMs symmetrically, a DBN auto-encoder is constructed to reconstruct the inputs as closely as possible. Based on the DBN auto-encoder, Tilear is structured in two parts: training and decision-making. During training, Tilear is trained with the signals only from good samples, which enables the trained DBN auto-encoder only know how to reconstruct signals of good samples. In the decision-making part, comparing the recorded signal from test sample and the Tilear reconstructed signal, allows to measure how well a recording from a test sample matches the DBN auto-encoder model learned from good samples. A reliable decision could be made. We perform experiments on two different precision mechanisms: precision electromotors and greasing control units. The feasibility of Tilear was demonstrated first. Additionally, performance of Tilear on the acquired electromotor dataset was compared with the state-of-the-art machine learning based fault detection technique, support vector machine (SVM). First result indicates that Tilear excels the SVM in terms of the Area Under the Curve (AUC) obtained from the Receiver Operating Characteristics (ROC) curve plot: 0.960 achieved by Tilear, while 0.941 by SVM.