Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis

Abstract

Data-driven approaches for machine tool wear diagnosis and prognosis are gaining attention in the past few years. The goal of our study is to advance the adaptability, flexibility, prediction performance, and prediction horizon for online monitoring and prediction. This paper proposes the use of a recent deep learning method, based on Gated Recurrent Neural Network architecture, including Long Short Term Memory (LSTM), which try to captures long-term dependencies than regular Recurrent Neural Network method for modeling sequential data, and also the mechanism to realize the online diagnosis and prognosis and remaining useful life (RUL) prediction with indirect measurement collected during the manufacturing process. Existing models are usually tool-specific and can hardly be generalized to other scenarios such as for different tools or operating environments. Different from current methods, the proposed model requires no prior knowledge about the system and thus can be generalized to different scenarios and machine tools. With inherent memory units, the proposed model can also capture long-term dependencies while learning from sequential data such as those collected by condition monitoring sensors, which means it can be accommodated to machine tools with varying life and increase the prediction performance. To prove the validity of the proposed approach, we conducted multiple experiments on a milling machine cutting tool and applied the model for online diagnosis and RUL prediction. Without loss of generality, we incorporate a system transition function and system observation function into the neural net and trained it with signal data from a minimally intrusive vibration sensor. The experiment results showed that our LSTM-based model achieved the best overall accuracy among other methods, with a minimal Mean Square Error (MSE) for tool wear prediction and RUL prediction respectively.

Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions

“…The authors of [129] use the PHM dataset generated under highspeed dry milling operation with a three-flute tungsten [101], [127], [128], [106], [117] , [149], [ [129], [159], [160], [161], [158], [157] [129]. Few more publically available datasets like the milling machine tool wear dataset of NUAA_Ideahouse [163], "System-level Manufacturing and Automation Research Testbed" (SMART) at the University of Michigan [164] can be used for the RUL prediction in the future.…”

Intelligent Online Monitoring of Rolling Bearing: Diagnosis and Prognosis

“…Long short-term memory (LSTM) is a type of artificial recurrent neural network (RNN) [ 30 ] used in the field of deep learning. LSTM networks are used for classifying, processing, and making predictions based on time series data in the context of the occurrence of unknown duration delay between important events in a time series.…”

Detecting Portable Executable Malware by Binary Code Using an Artificial Evolutionary Fuzzy LSTM Immune System

“…Generally speaking, the LSTM function is capable of learning order dependence in sequence prediction patterns [34]. Each one of the 3 types of the gate in a LSTM cell, forget gate, input gate, and output gate (M ST , M LT , and M M ), will decide what portion of the older data have to be forgotten, what portion of newer data have to be remembered, and what portion of the memory has to be given out correspondingly [35].…”