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MS2017-04: Anomaly detection for industrial sensor data

posted Nov 27, 2017, 3:36 AM by Marco Spruit
With the advance of Internet of Things (IoTs), nowadays mechanical equipment, ranging from elevators, vehicles, to aircrafts and wind turbines, are typically instrumented with numerous sensors to constantly capture the behaviors and health of the machine. Those sensors have been used to create systems that monitor devices in real-time. Besides real-time monitoring systems, both researchers and practitioners are working on utilizing data collected by these sensors to profile the failures of devices. In some cases, even to build models to predict device failures. Due to the lack of labelled datasets, building predictive models with supervised learning is very difficult and time-consuming. Unsupervised anomaly detection becomes a better option in handling such data (Malhotra et al., 2015, Malhotra et al., 2016, Park et al., 2017).

The aim of this project is to explore the use of various anomaly detection techniques, including one-class SVM, PCA, LSTMs and etc., in industrial sensor data collected by Shell. The results of your anomaly detection will provide useful insights for maintenance and help create more efficient maintenance plans.

You will be working with a dedicated data science team in Shell (globally), and have the opportunities to solve real business problem with your advanced data techniques through an internship. To get an internship there, you need to pass their online recruitment test and a short phone interview. 

Contact Ian for more details.

References

  • Malhotra, P., Vig, L., Shroff, G., & Agarwal, P. (2015). Long short term memory networks for anomaly detection in time series. In Proceedings (p. 89). Presses universitaires de Louvain. Chicago 
  • Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., & Shroff, G. (2016). Lstm-based encoder-decoder for multi-sensor anomaly detection. arXiv preprint arXiv:1607.00148. 
  • Park, D., Hoshi, Y., & Kemp, C. C. (2017). A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder. arXiv preprint arXiv:1711.00614. 
  • Luo, C., Yang, D., Huang, J., & Deng, Y. D. (2017). LSTM-Based Temperature Prediction for Hot-Axles of Locomotives. In ITM Web of Conferences (Vol. 12, p. 01013). EDP Sciences.
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