From Sensor Readings to Predictions: On the Process of Developing Practical Soft Sensors

This source preferred by Marcin Budka

Authors: Budka, M., Eastwood, M., Gabrys, B., Kadlec, P., Schwan, S., Tsakonas, A. and Žliobaitė, I.

This data was imported from Scopus:

Authors: Budka, M., Eastwood, M., Gabrys, B., Kadlec, P., Salvador, M.M., Schwan, S., Tsakonas, A. and Žliobaitė, I.

Pages: 49-60

eISSN: 1611-3349

ISBN: 9783319125701

ISSN: 0302-9743

DOI: 10.1007/978-3-319-12571-8

© 2014 Springer International Publishing Switzerland. Automatic data acquisition systems provide large amounts of streaming data generated by physical sensors. This data forms an input to computational models (soft sensors) routinely used for monitoring and control of industrial processes, traffic patterns, environment and natural hazards, and many more. The majority of these models assume that the data comes in a cleaned and pre-processed form, ready to be fed directly into a predictive model. In practice, to ensure appropriate data quality, most of the modelling efforts concentrate on preparing data from raw sensor readings to be used as model inputs. This study analyzes the process of data preparation for predictive models with streaming sensor data. We present the challenges of data preparation as a four-step process, identify the key challenges in each step, and provide recommendations for handling these issues. The discussion is focused on the approaches that are less commonly used, while, based on our experience, may contribute particularly well to solving practical soft sensor tasks. Our arguments are illustrated with a case study in the chemical production industry.

This data was imported from Web of Science (Lite):

Authors: Budka, M., Eastwood, M., Gabrys, B., Kadlec, P., Salvador, M.M., Schwan, S., Tsakonas, A. and Zliobaite, I.

Pages: 49-60

ISBN: 978-3-319-12570-1

ISSN: 0302-9743

The data on this page was last updated at 17:31 on November 21, 2017.