Recurrent Neural Networks with Long Term Temporal Dependencies in Machine Tool Wear Diagnosis and Prognosis

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.