A Deep Coupled Network for Health State Assessment of Cutting Tools Based on Fusion of Multisensory Signals

The cutting tool is a key part of a machine system, which plays an important role in modern manufacturing systems. To avoid an unexpected tool failure, it is necessary to carry out health condition assessment of cutting tools. In this paper, a deep coupled restricted Boltzmann machine (DCRBM) is proposed for health state assessment of cutting tools based on fusion of vibration signals and acoustic emission (AE) signals. Because of the complementary of multisensory signals, it is necessary to develop a fusion strategy for the fusion of multisource signals. The proposed DCRBM is symmetric with each side consisting of several hidden layers and one coupled layer, which is constructed by two basic restricted Boltzmann machines with similarity constraints. Vibration signals and AE signals, which are connected with the two sides of DCRBM, respectively, are mapped into a feature space, where similar representations are learned. The parameters of the deep architecture are learned by optimizing the new objective function. Experimental results on fusion of vibration signals and AE signals demonstrate the promising performance of DCRBM for health state assessment of cutting tools compared with other fusion strategies.