Machine learning supports predictive maintenance strategies for technical systems
Additional costs due to early replacement of possibly intact components.
Consideration of true component conditions of a technical system when planning maintenance actions.
High down-time costs due to unscheduled maintenance.
Comparison of economic efficiency with conventional maintenance strategie Identification of cost potentials of more than 50 % through AI
Smart Monitoring System:
Due to the use of the built-in current sensors, no further sensors, such as acceleration sensors, are required.
Efficient Signal Processing:
Our background as engineers enables optimal use of the measurement data by a reasonable deployment of techniques for data cleaning, integration and transformation.
Reasonable Data Reduction:
The analysis of the measurement data by our AI service allows the description of the condition via a few significant features.
Classification of the State:
Our diagnostic model evaluates new measurement data and allows conclusions about the true condition, such as normal operation and bearing, rotor or electical damage.
Remaining Useful Lifetime (RUL):
The knowledge of the RUL provides a significant competitive advantage, since field service actions are only carried out when they are actually required. The consequences are cost savings while simultaneously increasing availability and safety of an asset.
Robust Prognostic Models
Only by combining analytical and data-based models is it possible to achieve accurate, precise and robust predictions.
Natural Uncertainty of Predictions:
Our AI service provides a reasonable modeling of prediction uncertainties (e.g. caused by unknown future load conditions of a machine). For planning further maintenance actions uncertainties are essential.
The profitable use of predictive maintenance depends on many factors and is not given for every machine or component! Our tools help to analyse the profitability before major investments are made.