Information Fusion and Semi-Supervised Deep Learning Scheme for Diagnosing Gear Faults in Induction Machine Systems - Razavi-Far, R. (IEEE Xplore, 2020)
There has been an increasing interest in the design of intelligent diagnostic systems for industrial applications. The key requirement in the design of practical diagnostic systems is the ability for decision making in high-dimensional feature spaces, where the prior knowledge about the system states in terms of labels is very limited. Moreover, the problem of diagnosing simultaneous defects is rarely addressed on real industrial applications. This paper aims to develop a semi-supervised deeplearning scheme for diagnosing multiple defects including simultaneous ones in a gearbox directly connected to an induction machine shaft. This scheme consists of two main modules: information fusion and decision making. The former integrates captured multiple sensory streams into a very high dimensional feature space. The latter uses a semisupervised deep learning procedure to minimize the human interaction during the training and maximize the diagnostic efﬁciency. This scheme facilitates learning and diagnosing defects under harshest conditions 1) where only a few number of labeled samples are collected together with a large number of unlabeled samples, and 2) in a very highdimensional feature space. Several state-of-the-art semisupervised and supervised learners have also been included in the scheme, enabling a comparative experiment for diagnosing simultaneous defects.