  Data-driven approaches to automated machine condition monitoring are gaining
popularity due to advancements made in sensing technologies and computing
algorithms. This paper proposes the use of a deep learning model, based on Long
Short-Term Memory (LSTM) architecture for a recurrent neural network (RNN)
which captures long term dependencies for modeling sequential data. In the
context of estimating cutting tool wear amounts, this LSTM based RNN approach
utilizes a system transition and system observation function based on a
minimally intrusive vibration sensor signal located near the workpiece
fixtures. By applying an LSTM based RNN, the method helps to avoid building an
analytic model for specific tool wear machine degradation, overcoming the
assumptions made by Hidden Markov Models, Kalman filter, and Particle filter
based approaches. The proposed approach is tested using experiments performed
on a milling machine. We have demonstrated one-step and two-step look ahead
cutting tool state prediction using online indirect measurements obtained from
vibration signals. Additionally, the study also estimates remaining useful life
(RUL) of a machine cutting tool insert through generative RNN. The experimental
results show that our approach, applying the LSTM to model system observation
and transition function is able to outperform the functions modeled with a
simple RNN.
