Remaining Useful Life Prediction of Machining Tools by 1D-CNN LSTM Network
Contributions
use a 1D-CNN LSTM network architecture for machining tools RUL prediction
Problem Addressing
Traditional machine learning algorithms are sometimes difficult to extract hidden information that characterizes the degradation process of the tool.
deep learning methods tend to have better effects, as it has powerful adaptive learning and anti-noise ability, and it can automatically extract deep
features, which is more versatile than traditional machine learning methods.
Why CNN-LSTM?
CNN has a its capacity to automatically extract features and LSTM can effectively mine the hidden information in time series.
In fact, we can combine CNN’s high-dimensional feature extraction capacity and LSTM’s advantage on time series problems. After CNN extracts
features, we input them into the LSTM for training, then some improvements in accuracy and speed can be achieved.
For time-series problems, one dimensional convolutional neural network (1D-CNN) is more suitable than common convolution neural network. One of the
characteristics of the 1D-CNN is that for time-series data, the receptive field moves only in the direction of time, so the local inter-variable correlation can be extracted.
Some knowledge points learned
- Each convolutional layer consists of several convolutional units whose parameters are optimized by backpropagation algorithms.
- Pooling can effectively reduce the amount of data and increse the calculation speed.
- Each unit of RNN is a simple chain structure, it processes the input sequence {x1,x2,…,xT} sequentially to construct a corresponding sequence of hidden states {h1,h2,…,hT}.
- The main purpose of the dropout layer is to reduce over-fitting.
Ideas
- compare the results of 1D-CNN,LSTM and 1D-CNN LSTM in own work.
- write own paper according to this reference(part of introduction and network description)
- Refer to the chart in the article