Condition monitoring for industrial assets

What is the difference between condition monitoring, condition-based monitoring (CBM), and predictive maintenance?

Condition monitoring is the live (and historical) view of equipment signals — on this page, vibration, FFT, temperature, pressure, and moisture — so teams can see changing asset behaviour in one place. Condition-based monitoring (CBM) applies rules and thresholds to that stream so alarms or maintenance actions fire when limits are exceeded. Predictive maintenance (PdM) is a further step: machine-learning anomaly and failure prediction on live equipment signals, not the same job as rules-based CBM. Rubus keeps PM, CBM, and PdM on one operational foundation so the condition stream can feed the next programme as data quality grows.

Which sensors and signals does Rubus Condition Monitoring include?

The Condition Monitoring solution is built around a multi-sensor view: vibration and FFT for mechanical-fault context, plus temperature, pressure, and moisture — combined instead of reviewing isolated readings. Teams can compare live and historical trends, review degradation, and export evidence for maintenance planning.

How do condition monitoring alerts and rules work?

You apply equipment-level rules on the condition stream, then surface critical alerts first so the important changes are not buried in noise. The right team is notified through configured channels. Thresholds that trigger alarms or automatic maintenance orders live on the Preventive/Predictive (CBM) programme — this page is the live condition view and rules/alerts layer.

Can Rubus Condition Monitoring run on brownfield plants?

Yes. Condition Monitoring links to Connected Equipment and IT–OT Edge for multi-protocol acquisition without rip-and-replace. Plant sources connect through Rubus Edge. Local control loops stay with the equipment; the IIoT layer shares the condition stream for monitoring and later PdM/APM.

Does condition monitoring feed preventive (PM) and predictive (PdM) maintenance?

Yes. Condition data on the Rubus platform supports preventive programmes and predictive ML on the same stream. There is a path to Preventive Maintenance, FRACAS, and the AI/ML Workbench, and to PdM and APM, from that condition stream. PdM itself is ML anomaly and failure prediction on live equipment signals — typically after the monitoring and CBM foundation is in place, not a rebrand of threshold alerts.

What is store-and-forward in condition monitoring, and why does it matter?

Rubus Edge can store condition data locally when the plant network is unavailable and forward it when connectivity returns, so edge-to-cloud continuity does not depend on a perfect link. That keeps trends, rules, and later PdM from going blind during dropouts. Protocol-level detail lives on the IT–OT Edge page.

Who is Rubus Condition Monitoring for?

It is for industrial maintenance, reliability, and APM teams who need one operational view of asset condition — not a weekly CSV — and who want earlier, better-informed maintenance decisions. It fits plants that can connect existing equipment (brownfield) and then grow from rules and trends into PM, CBM, and PdM on the same Rubus platform.

How is condition monitoring different from calendar-based preventive maintenance?

Calendar or usage PM generates real work orders on a schedule — useful, but it does not watch how the asset is actually running between intervals. Condition monitoring puts vibration, temperature, pressure, moisture (and FFT) into live and historical views with rules and prioritized alerts. Rubus’s Preventive/Predictive page treats PM, CBM, and PdM as different jobs on one foundation: feed PM with OT evidence, use CBM thresholds when condition says so, and keep a path to predictive ML as data quality grows.

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