Advances in Intelligent Data Analysis XIII

Volume 8819 of the series Lecture Notes in Computer Science pp 49-60

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

  • Marcin BudkaAffiliated withBournemouth University
  • , Mark EastwoodAffiliated withCoventry University
  • , Bogdan GabrysAffiliated withBournemouth University
  • , Petr KadlecAffiliated withEvonik Industries
  • , Manuel Martin SalvadorAffiliated withBournemouth University
  • , Stephanie SchwanAffiliated withEvonik Industries
  • , Athanasios TsakonasAffiliated withBournemouth University
  • , Indrė ŽliobaitėAffiliated withAalto University and HIIT

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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.