The goal of Predictive Maintenance (PdM) is to identify approaching equipment failures and conduct Preventive Maintenance (PM) before the failures occur. Scheduled preventive maintenance can be expensive and time consuming, and may even harm equipment if conducted excessively.
Predictive maintenance is advantageous over scheduled preventive maintenance, conducting preventive maintenance only when necessary, thereby reducing costs and asset downtime.
While predictive maintenance offers many advantages to asset Operation & Maintenance (O&M), PdM systems are not cheap. The cost of PdM includes: installation of sensors and infrastructure, managing data storage and servers, deploying software updates and usually hiring a data scientist.
Many predictive maintenance companies emerged in recent years due to reduced cost of sensors and infrastructure, as well as improvements in Machine Learning capabilities.
Asset-Eigentümer stehen vor vielen Optionen für PdM-Systeme, und die Auswahl der richtigen ist eine Herausforderung.
In many cases assets already have a lot of collected data (from SCADA and EAM) that is not utilized, and new, expensive, PdM systems are not always necessary.
Predictive maintenance is closely related to Prognostics and Health Management (PHM). The PHM system monitors the asset, and the PdM system recommends maintenance actions based on the PHM results.
BQR offers several software and consulting services for Predictive Maintenance
- apmOptimizer can be used for “what if analysis” by calculating the expected ROI of PdM systems, allowing asset owners to select the best predictive maintenance for their needs
- BQR-Digital collects and analyzes field logs (imports data from EAM, CMMS and SCADA) in order to predict Remaining Useful Life
- BQR-Tools und Beratung Bereitstellung von Anlagenwartung und Logistikoptimierung, einschließlich: Inspektionen, PM und Ersatzteile
