https://github.com/aurelienmorgan/abnormal_vibrations_watchdog

Time Series Anomaly detection. The monitored signal is made-up of machinery vibration sensor measurements.
https://github.com/aurelienmorgan/abnormal_vibrations_watchdog

anomaly-detection autoencoder bayesian-optimization data-pipeline etl jupyter-notebook keras lstm mongo-db pyspark python rnn tensorflow time-series

Last synced: 3 months ago
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Time Series Anomaly detection. The monitored signal is made-up of machinery vibration sensor measurements.

• Host: GitHub
• URL: https://github.com/aurelienmorgan/abnormal_vibrations_watchdog
• Owner: aurelienmorgan
• Created: 2020-09-18T07:33:42.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-12-07T13:00:14.000Z (about 4 years ago)
• Last Synced: 2024-09-27T17:23:34.661Z (3 months ago)
• Topics: anomaly-detection, autoencoder, bayesian-optimization, data-pipeline, etl, jupyter-notebook, keras, lstm, mongo-db, pyspark, python, rnn, tensorflow, time-series
• Language: Python
• Homepage:
• Size: 8.71 MB
• Stars: 13
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Mechanical Failure Watchdog

##### Bearings Vibration Anomaly Detection






Welcome to this Deep Learning project page !

Here, we develop an RNN model in order to detect early the forewarning signs

of forthcoming machinery hard failure.

The model we train is ready for deployment. Taking vibration sensor signal as input, it is able

to raise an alert, would the working conditions deteriorate to an extend that

the material is very likely to fail in a foreseeable future, thus indicating that

operation shall be stopped and replacement of the faulty part operated before

larger damage could be incurred.

The below notebook contains an end-to-end ETL data pipeline plus a whole Bayesian Optimization cycle

for the LSTM Autoencoder implemented to fit the bill :






    

        


            

        

    









KEYWORDS :

	```Time Series```, ```Anomaly Detection```,

	```Tensorflow```, ```Keras```,

     ```RNN```, ```LSTM```, ```Autoencoder```,

     ```Bayesian Optimization```,

	```MongoDB```, ```PySpark```, 

    ```ETL```, ```Data Pipeline```