About

MonitorX is an Innovation Project for the Industrial Sector (Innovasjonsprosjekt i næringslivet – IPN) organized by Energy Norway and supported by the Research Council of Norway.

The project period is from July 2015 to June 2019 (4 years) and the budget is 17.2 mill. NOK (incl. in-kind contribution from project partners).

MonitorX has three R&D partners and a large number of industry partners representing Norwegian and Swedish hydropower companies and hydropower equipment producers and service providers. An overview of the partners is provided at MonitorX – Partners.

Background
Hydropower has for more than a century been one of the pillars for industrialization, wealth and secure energy supply in Scandinavia. The main hydropower development and large scale construction was in the period 1945 – 1975. The energy market has been through a major change since this period, and is still changing. However, many of the continuously ageing hydropower plants are still in a shape "as built". To be able to cope with the challenges in a changing energy system and to serve new markets, hydropower must be flexible, cheap and reliable.

Hydropower technology from last century, still in use 2017.

Digital transformation
We currently experience a digital transformation in many different areas where established business models and processes are substantially changed through new concepts, methods and models such as industry 4.0, machine learning, cyber-physical systems, internet of things, data mining and internet of services . Digitalization will also effect maintenance and operation of hydropower plants, and there is a large unexploited potential for improving maintenance and reducing costs.

Digital transformation: New concepts, methods and models

MonitorX will address the new possibilities for maintenance of hydropower plants when power companies start to use the new concepts, methods and models mentioned above. Until now, condition monitoring and application of monitoring data is mainly restricted to protection systems shutting down the plants when single monitoring signals exceed pre-defined thresholds (e.g. bearings with temperature and vibration protection). However, more advanced models, in combination with past history (failures and maintenance actions) and domain knowledge (design and function of equipment), can utilize monitoring data in a new and better way than done today.

Prof. Miguel Ángel Sanz Bobi, Institute for Research in Technology (IIT), Comillas University

Project aims
The project aims can be divided into main results, benefits and knowledge gain.

Main results
Models, algorithms and corresponding software prototypes for optimal lifetime utilization of hydropower components based on monitoring of technical condition and risk. This includes:

  • To develop models for diagnosis of technical condition and fault prognosis based on advanced methods for condition monitoring.
  • To develop models for monitoring of remaining lifetime and risk.
  • To develop prototypes of models and algorithms to be able to test the prototypes in real hydropower plants and to demonstrate how the models can be used for monitoring of technical condition and risk.
  • To educate a Postdoc. Topic: Advanced condition monitoring systems for hydropower components.

MonitorX work packages

Benefits
Application of the MonitorX project results, and application of new concepts and methods in general, will result in reduced maintenance costs by:

  •  ... avoiding (catastrophic) faults ...
  • ... avoiding unnecessary component replacements ...
  • ... prioritizing the most critical components for maintenance ...
  • ... optimized maintenance ...

... through early warnings of ageing and potential faults.

Knowledge gain
After completion of the project, the project participants should be able to provide specific answers to the following questions:

  • How can hydropower plant operators utilize the mentioned concepts and methods for maintenance of their plants?
  • What are the possibilities, challenges and restrictions?
  • How can monitoring data be used to carry out maintenance more predictive?