https://github.com/bagustris/mhms_deeplearning

https://github.com/bagustris/mhms_deeplearning

Repository

https://github.com/bagustris/MHMS_DEEPLEARNING/blob/master/

# Deep Learning and Its Applications to Machine Health Monitoring

This module contains the code used in our survey paper: [Deep Learning and Its Applications to Machine Health Monitoring](https://www.researchgate.net/publication/311839720_Deep_Learning_and_Its_Applications_to_Machine_Health_Monitoring_A_Survey). It has been accepted by [Mechanical Systems and Signal Processing](https://www.journals.elsevier.com/mechanical-systems-and-signal-processing). Pls find the newest version there. 

## Table of Contents


- [Deep Learning and Its Applications to Machine Health Monitoring: A Survey](#DL-MHMS)
  - [Table of Contents](#table-of-contents)
  - [Data](#data)
  - [Code](#code)
    - [Feature Extraction](#feature-extraction)
    - [Deep Learning Models](#deep-learning-models)
    - [Machine Learning Models](#machine-learning-models)
    - [Main Test](#main-test)




## Data
This folder contains two pickle files, which are extracted features and labels for tool wear sensing experiments. Each pickle file contain x_train, y_train, x_test, y_test. The task is defined as a regression problem.

- data_normal: each data sample is a vector. The features are extracted from the whole time sequences. 
- data_seq: each data sample is a tensor. The features are extracted from windows of the time time sequences. 

Especially, data_seq can be used by LSTM and CNN models. data_normal can be utilized by conventional ML models.

## Code
This folder contains codes for feature extraction, traditional machine learning models, deep learning models and test modules. 

### Feature Extraction
RMS, VAR, MAX, Peak, Skew, Kurt, Wavelet, Spectral Kurt, Spectral Skewness, Spectral Powder features are extracted from the input time series. 

### Deep Learning Models
Based on Keras, autoencoder and its variants, implementations of DBN, LSTM, Bi-directional LSTM and CNN models are provided

### Traditioanl Machine Learning Models
SVR with two kernels (linear and rbf), Random Forest and Neural Network are provided.

### Main Test
To replicate the results reported in paper (python 2.7)
```
pip install -r requirement 
python main_test.py
```
The results will be stored in output.log. In addition, a python notebook file is provided to parse the raw log file for mean and std accuracies computing. And due to randomness, we run all of these models five times. 

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