Optimizing Equipment Effectiveness with Machine Learning

Condition Monitoring and Predictive Maintenance

The optimization of equipment effectiveness mainly is based on two measures:

  • Minimizing downtime
  • Maximizing availability

Reactive maintenance is difficult to plan due to spontaneous errors. Longer maintenance times are the result. Risks of failure are reduced by regular maintenance intervals. However, this is at the expense of the equipment's productive operating time.

Condition Monitoring and Predictive Maintenance

»Condition Monitoring« of equipment detects critical events and conditions with highwear potential. Events and faults are classified and evaluated.  Critical events or adverse operating states can be eliminated immediately by rapid reactions in order to avert cost-intensive consequential damage.Downtimes are reduced because service technicians, spare parts and logistics can be made available in a targeted manner through appropriate diagnostics.

»Predictive Maintenance« estimates risks of unwanted operating conditions and events. These predictions enable demand-oriented planning of service and maintenance activities. They are created for both, individual equipments as well as equipment parks. Ideally, predictive maintenance maximizes equipment availability and provides early information for targeted maintenance actions.

Step-by-step optimization of the effectiveness of the systems.
© Fraunhofer ITWM

Step-by-step optimization of the effectiveness of the systems.

Five Advantages

With the help of our experience, you can upgrade your equipment with condition monitoring. You combine the collected telemetry, service and maintenance information to estimate appropriate models and extend your service with »Predictive Maintenance«:

  • Detect events, anomalies or failures
  • Identify causes of unplanned failures or errors
  • Plan with reliable prognosis of the remaining useful life of equipment
  • Maximize usage time
  • Minimize maintenance time through early planning of upcoming maintenance actions

Our Services

Our department System Analysis, Prognosis and Control supports you in optimizing the effectiveness of your equipment step by step.

  1. We support you, designing a solution-oriented condition monitoring and predictive maintenance systems.
  2. We analyse your existing knowledge and determine the information required by your application.
  3. Furthermore, we identify, develop and integrate machine-learning and deep-learning algorithms tailored for your data and information system.

Needless to say, implemented solutions can be integrated into common IOT platforms.

Condition Monitoring

  • System modeling and simulation with digital twins
  • Selection and placement of sensors
  • Construction of virtual sensors
  • Identification and rating of operating states
  • Classification of failures

Predictive Maintenance

  • Trend analysis, model-based prognosis of failures and critical events
  • Computation of remaining useful life time
  • Predictive control for efficient equipment utilisation
  • Generation of automatic reports and dashboards

Meet us

Workshop / Munich / 05/15/2019 & 09/18/2019

Predictive Maintenance

In May and September 2019 we offer the seminar »Digital Business Models and Services via Predictive Maintenance« at Haus der Technik.

Fair / Essen / 02/05/2019 - 02/07/2019

E-world energy & water

At E-world we will show current projects and technologies for energy industry in hall 4, booth 4-619.

Fair / Nuremberg / 02/26/2019 - 02/28/2019

embeddedWorld

At embeddedWorld we will present our methods for controller design and validation in machine and plant engineering in hall 4, booth 4-470.