E-maintenance of electro-mechanical drives: prognostics and health management solutions under non-stationary operating conditions (EMAINT)

Project duration

2016 - 2019

Project Category


Contact Information

prof. dr. Mitjan Kalin

Rotational machines and drives represent the most ubiquitous item of equipment present in almost all branches of industry, power engineering and transport. As such they contribute significantly to the maintenance costs which still represent sizeable share in the EU economy estimated to 450 billion EUR/year of which about 70 billion EUR/year are estimated to be wasted through ineffective maintenance. Downsizing the cost is becoming a must in order to keep the companies globally competitive. This requires better use of IT support and integration of "information islands" within the enterprise via concept of e-maintenance.

Prognostics and health management (PHM), with on-line condition monitoring (CM), are key decision-support technologies in e-maintenance system. In spite of significant advances in enabling technologies, no massive use of CM (and PHM in particular) in industry has been witnessed so far. According to a review done in USA, only in 1% of the machinery the condition is monitored automatically all the time! 

In spite of  obvious need for 'general purpose' PHM solutions that would be applicable to a broader range of operating machines the customers are still rather sustained to implement them, primarily due to relatively high cost of implementation and cost of ownership. The problems originate in

  1. overwhelming and hence costly design cycle,
  2. use of conventional CM methodologies incapable to handle non-stationary operational conditions in a simple and efficient way,
  3. lacking prognostic abilities, and 
  4. costly interoperability with existing enterprise resouce planning (ERP) and maintenance management systems (MMS). 

Fuelled by these challenges, the project will conduct basic and applied research leading to the following  anticipated major results:

  1. A TRL5 prototype of a versatile and low cost CM&PHM module (in the sequel referred to as CM&PHM) that will be suitable for implementation on a broad range of already operating machinery as well as an embedded module being part of newly manufactured machines. It will perform inference on asset condition and communicate the results across the e-maintenance system.
  2. New algorithms for condition assessment and prognostics of the remaining useful life robust to incomplete prior information about the operating load and speed, external disturbances and other prior data from components manufacturers. These are the heart of CM&PHM. We will stress on a class of machinery manufactured in series. The promising novel Bayesian fusion concept, will be able to merge information from on-line sensors, tribological analysis and past life cycles in the final prediction. 
  3. Novel interfaces to e-maintenance systems supporting tools for machinery life cycle management. The information about the machines' condition becomes instantaneously available through the pre-existing ERP/MMS IT infrastructure thus allowing optimal maintenance and production planning. Furthermore, timely CM information reduces the risk of unscheduled production interruptions.
  4. The prototype versions of the system will be validated on laboratory test rig and  demo industrial installation, already agreed with the company supporting the project.

Project will be realised by academic partners with top rate expertise in CM and PHM, embedded systems design and tribology and a leading manufacturers of electrical motors.

The immediate envisaged impact of the project:

  1. The oririginal equipment manufacturers in Slovenia will get the opportunity to advance their products with low cost embedded PHM  solutions, hence coming up with a new generation of self-aware machinery, highly competitive on the global market. Domel has vision of 1 mil.EC motors per year with embedded PHM by 2020.
  2. By installing the solutions on existing machines, their owners can expect near zero downtimes, minimal maintenance resources and h.