Condition-Based Maintenance

CBM uses sensors to continuously monitor key variables like temperature, pressure, fluid levels, flow rates, vibration and more, providing real-time insight into equipment health. By storing and structuring this data, trends and early deviations from normal conditions can be detected.

Comparing trends against critical thresholds allows early identification of wear or potential failure, enabling timely, targeted maintenance.

This reduces downtime, optimizes resource use, and extends equipment lifespan.

During operation, a substantial volume of data is generated, placing considerable demands on both storage and transmission capacity. Moreover, using raw data over a given period to identify trends can be challenging. To address these issues, we implement local preprocessing.

Each monitored parameter is summarized using a calculated average, which significantly reduces the need for storage and bandwidth.

This approach also simplifies the visualization and analysis of trends over time.

Advanced algorithms are used to generate actionable insights across all plants. This includes the use of machine learning techniques that automatically filter and group similar sessions.

Customers access self-service data via a web portal, where plant and machinery data is continuously updated and intelligently organized.

Dashboards are created on demand, showing only the data users request.

A cost model calculates the best time for maintenance, aligning it with planned operations for minimal disruption.