Since joining Pimsoft in 2014, Bernard Morneau has been involved in expanding Pimsoft’s Sigmafine business globally. Prior to joining Pimsoft, Bernard served in an executive capacity at OSIsoft, a Strategic Alliance of Pimsoft, where he held the positions of General Manager of OSIsoft Canada, Vice-President Worldwide Sales, and Executive Vice President and President of OSIsoft LLC from 2001 to 2012.
Bernard has engineering and information technology experience and a proven track record of successful business development, sales, and operations management. In 1991, he co-founded Cogexel, a software development and integration company which later became the OSIsoft Distributorship in Canada and was acquired by OSIsoft LLC in 2001. Before this entrepreneurial period of his career, Bernard occupied diverse technical, management, and executive positions in the metallurgical, chemical, and oil & gas industries in eastern Canada. Bernard earned his bachelor’s degree in Chemical Engineering from the University of Ottawa in 1978.
Pimsoft is owner & maker of Sigmafine, enterprise-wide solution for improving the reliability of business processes through better data fit for use at all times.
The mission of the Sigmafine team is to generate value for our customers by ensuring the continual accuracy and usability of their process and manufacturing data so that their data can be fully trusted for decision and action. Pimsoft brings innovative software solutions and engineering skills to industries where data quality and usability is mission critical in shaping information for action and decision. With its offices in Turin, Milan and Rome, Italy and Houston, Texas, Pimsoft maintains the largest contingent of skilled and experienced IT and Engineering professionals in this application domain.
At the core of Pimsoft is an expertise in process and manufacturing know-how applied to data curation and governance. We ensure the successful implementation and application of Sigmafine to support operational, business and compliance mandates of the enterprise.
Industrial IoT time-series data engineering - a layered approach to data quality
Data quality is a foundational prerequisite for data engineering in your digital transformation and data science/machine learning initiatives. Join us in this session for a “how-to” on getting started and implementing a layered approach to data quality with the PI System, such as:
- PI System Monitoring
- Simple analytics to validate (individual) sensor data streams
- Simple analytics to validate at an equipment level (several related sensors)
- Advanced (multivariate/machine learning) analytics to validate at a functional equipment group (FEG) level - tens or hundreds of sensors
- Advanced (connectivity model based) analytics - data reconciliation with mass/energy balance (one or more process units)
Whether it is an individual sensor (pressure, temperature, flow, vibration etc.) an individual piece of equipment such as a pump (with a few sensors), a functional equipment group (FEG) i.e. a feed water system (made up of a group of equipment like motor/pump/valve/filter/pipe assembly), or an entire process unit or a plant – the scope of data quality requirement has to be understood in its usage context. And, with streaming industrial sensor/IoT time-series data, a layered approach to data quality gives you the flexibility to apply fit-for-purpose techniques mapped to use case requirements.
We will review the basic validation checks for missing data (I/O timeout), flat-line data (sensor malfunction), out-of-range data, etc., and issues in source instrumentation/control system, network connectivity, faulty data collection configuration (scan rate, unit-of-measure, exception/compression specs), etc. We will also cover advanced analytics, including machine learning and data reconciliation with illustrative use cases.
Thursday, October 22nd, 12:45 - 13:15 (UTC -5)