Predictive Condition Based Monitoring Service

DPS Predictive Condition Based Monitoring Service wird unterstützt von DPS Group

DPS Group is a Siemens Platinum Solution Partner dedicated to positively shaping the future of everyone we work with. Our purpose drives a complete approach to industrial digitalisation and engineering, delivering secure, connected, safe, and efficient operations across the full lifecycle. We specialise in end-to-end, Instrument to Cloud solutions — integrating smart assets, automation, OT/IT connectivity, and data-driven insight to give clients clear visibility and control of their operations. Alongside this, we support customers on their digital transformation journey, applying Industrial Edge, cloud technologies, and modern engineering practices to enhance performance, resilience, and sustainability.

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Warum Predict Asset failures before they occur?

Our predictive condition-based monitoring helps organisations move from reactive, time-based maintenance to a more intelligent, data-driven approach. Using Siemens Industrial Edge and Senseye, our service monitors asset condition, identifies developing faults earlier & highlights where maintenance attention is most needed. This helps customers improve reliability, reduces downtime, and make better maintenance decisions across industrial assets.

Vorteile

Reduces the risk of unplanned downtime by identifying developing asset issues earlier and enabling timely maintenance intervention.
Improves maintenance efficiency by helping teams prioritise work based on actual asset condition rather than fixed maintenance intervals alone.
Provides clearer visibility of asset health, supporting better operational decisions, improved reliability and stronger maintenance planning.

Besondere Funktionen

Predictive Asset Monitoring

Continuously monitors asset condition using AI-driven models to detect early signs of deterioration and emerging faults.

Condition-Based Maintenance Insight

Supports maintenance teams with prioritised insight so that interventions can be planned according to asset condition and operational risk.

Early Fault Identification

Highlights abnormal patterns and developing issues before they lead to significant disruption or equipment failure.

Scalable Industrial Asset Coverage

Can be applied across multiple critical industrial assets to support a more consistent and scalable monitoring strategy.

Actionable Operational Intelligence

Transforms asset data into clear, practical insight that supports reliability improvement and better maintenance decision-making.

Anwendungen in der Praxis

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Proactive detection of filling valve degradation

By monitoring the health of individual filling heads, manufacturers can shift from reactive maintenance - which often results in thousands of rejected units or hours of downtime - to intelligent, scheduled maintenance.