lucadellalib / bayesian-deep-rul

Bayesian Deep Learning for Remaining Useful Life Estimation of Machine Tool Components

Bayesian Deep Learning for Remaining Useful Life Estimation of Machine Tool Components

Bayesian and frequentist deep learning models for remaining useful life (RUL) estimation are evaluated on simulated run-to-failure data. Implemented in PyTorch, developed and tested on Ubuntu 18.04 LTS. All the experiments were run on a publicly available Google Compute Engine Deep Learning VM instance with 2 vCPUs, 13 GB RAM, 1 NVIDIA Tesla K80 GPU and PyTorch 1.2 + fast.ai 1.0 (CUDA 10.0) framework.

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Requirements

Anaconda Python >= 3.6.4 (see https://www.anaconda.com/distribution/)

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Installation

Clone or download the repository, open a terminal in the root directory and run the following commands:

conda env create -f environment.yml

conda activate bayesian-deep-rul

Now the virtual environment bayesian-deep-rul is active. To deactivate it, run:

conda deactivate

When you do not need it anymore, run the following command to remove it:

conda remove --name bayesian-deep-rul --all

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Dataset

The models were tested on the four simulated turbofan engine degradation subsets in the publicly available Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset. Check datasets/CMAPSS/README.md for instructions on how to download the dataset.

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Usage

Open a terminal in the root directory, activate the virtual environment and run one of the following commands:

• sh train.sh to train the selected model. Parameters can be modified by editing train.sh

• sh evaluate.sh to evaluate the selected model. Parameters can be modified by editing evaluate.sh

• sh run_experiments.sh to replicate the experiments on the C-MAPSS dataset

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TensorBoard

Open a terminal in the root directory, activate the virtual environment and run tensorboard --logdir . to monitor the training process with TensorBoard. If you are training on a remote server, connect through SSH and forward a port from the remote server to your local computer (gcloud compute ssh <your-vm-name> --zone=<your-vm-zone> -- -L 6006:localhost:6006 on a Google Compute Engine Deep Learning VM instance).

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Results

Training and evaluation logs of the experimental results are provided for verification. Run results/results.ipynb in Jupyter Notebook to check the results by yourself.

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Contact

[email protected]

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bayesian-deep-rul's Issues

division by zero

Dataset: CMAPSS/FD001
Traceback (most recent call last):
File "train.py", line 162, in
MODEL = getattr(module, cls)(input_size).to(DEVICE)
File "models/frequentist_dense3.py", line 23, in init
nn.Linear(input_size[0] * input_size[1] * input_size[2], 100, bias=False),
File "/home/test/anaconda2/envs/bayesian-deep-rul/lib/python3.6/site-packages/torch/nn/modules/linear.py", line 77, in init
self.reset_parameters()
File "/home/test/anaconda2/envs/bayesian-deep-rul/lib/python3.6/site-packages/torch/nn/modules/linear.py", line 80, in reset_parameters
init.kaiming_uniform_(self.weight, a=math.sqrt(5))
File "/home/test/anaconda2/envs/bayesian-deep-rul/lib/python3.6/site-packages/torch/nn/init.py", line 316, in kaiming_uniform_
std = gain / math.sqrt(fan)
ZeroDivisionError: float division by zero

some questions

Hi, my computer is on windows, can the code you provided be run in pycharm or only in ubuntu?