Copyright (c) 2018, California Institute of Technology ("Caltech"). U.S. Government sponsorship acknowledged.
All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
• Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
• Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
• Neither the name of Caltech nor its operating division, the Jet Propulsion Laboratory, nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Telemanom employs vanilla LSTMs using [Keras](https://github.com/keras-team/keras)/[Tensorflow](https://github.com/tensorflow/tensorflow) to identify anomalies in multivariate sensor data. LSTMs are trained to learn normal system behaviors using encoded command information and prior telemetry values. Predictions are generated at each time step and the errors in predictions represent deviations from expected behavior. Telemanom then uses a novel nonparametric, unsupervised approach for thresholding these errors and identifying anomalous sequences of errors.
This repo along with the linked data can be used to re-create the experiments in our 2018 KDD paper, "[Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding](https://arxiv.org/abs/1802.04431)", which describes the background, methodologies, and experiments in more detail. While the system was originally deployed to monitor spacecraft telemetry, it can be easily adapted to similar problems.
# Getting Started
Clone the repo (only available from source currently):
Configure system/modeling parameters in `config.yaml` file (to recreate experiment from paper, leave as is). For example:
-`train: True` if `True`, a new model will be trained for each input stream. If `False` (default) existing trained model will be loaded and used to generate predictions
-`predict: True` Generate new predictions using models. If `False` (default), use existing saved predictions in evaluation (useful for tuning error thresholding and skipping prior processing steps)
-`l_s: 250` Determines the number of previous timesteps input to the model at each timestep `t` (used to generate predictions)
#### To run via **Docker**:
```shell
docker build -t telemanom .
# rerun experiment detailed in paper or run with your own set of labeled anomlies in 'labeled_anomalies.csv'
Pre-split training and test sets must be placed in directories named `data/train/` and `data/test`. One `.npy` file should be generated for each channel or stream (for both train and test) with shape (`n_timesteps`, `n_inputs`). The filename should be a unique channel name or ID. The telemetry values being predicted in the test data *must* be the first feature in the input.
For example, a channel `T-1` should have train/test sets named `T-1.npy` with shapes akin to `(4900,61)` and `(3925, 61)`, where the number of input dimensions are matching (`61`). The actual telemetry values should be along the first dimension `(4900,1)` and `(3925,1)`.
## Raw experiment data
The raw data available for download represents real spacecraft telemetry data and anomalies from the Soil Moisture Active Passive satellite (SMAP) and the Curiosity Rover on Mars (MSL). All data has been anonymized with regard to time and all telemetry values are pre-scaled between `(-1,1)` according to the min/max in the test set. Channel IDs are also anonymized, but the first letter gives indicates the type of channel (`P` = power, `R` = radiation, etc.). Model input data also includes one-hot encoded information about commands that were sent or received by specific spacecraft modules in a given time window. No identifying information related to the timing or nature of commands is included in the data. For example:
This data also includes pre-split test and training data, pre-trained models, predictions, and smoothed errors generated using the default settings in `config.yaml`. When getting familiar with the repo, running the `result-viewer.ipynb` notebook to visualize results is useful for developing intuition. The included data also is useful for isolating portions of the system. For example, if you wish to see the effects of changes to the thresholding parameters without having to train new models, you can set `Train` and `Predict` to `False` in `config.yaml` to use previously generated predictions from prior models.
## Anomaly labels and metadata
The anomaly labels and metadata are available in `labeled_anomalies.csv`, which includes:
-`channel id`: anonymized channel id - first letter represents nature of channel (P = power, R = radiation, etc.)
-`spacecraft`: spacecraft that generated telemetry stream
-`anomaly_sequences`: start and end indices of true anomalies in stream
-`class`: the class of anomaly (see paper for discussion)
-`num values`: number of telemetry values in each stream
To provide your own labels, use the `labeled_anomalies.csv` file as a template. The only required fields/columns are `channel_id` and `anomaly_sequences`. `anomaly_sequences` is a list of lists that contain start and end indices of anomalous regions in the test dataset for a channel.
Each time the system is started a unique datetime ID (ex. `2018-05-17_16.28.00`) will be used to create the following
- a **results** file (in `results/`) that extends `labeled_anomalies.csv` to include identified anomalous sequences and related info
- a **data subdirectory** containing data files for created models, predictions, and smoothed errors for each channel. A file called `params.log` is also created that contains parameter settings and logging output during processing.
As mentioned, the jupyter notebook `telemanom/result-viewer.ipynb` can be used to visualize results for each stream.
# Citation
If you use this work, please cite:
```@article{hundman2018detecting,
title={Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding},
author={Hundman, Kyle and Constantinou, Valentino and Laporte, Christopher and Colwell, Ian and Soderstrom, Tom},
journal={arXiv preprint arXiv:1802.04431},
year={2018}
}
```
# License
Telemanom is distributed under [Apache 2.0 license](http://www.apache.org/licenses/LICENSE-2.0).
