Solutions · Predictive Maintenance
Fix it before it fails.
DigiNest watches every asset for the early signs of trouble. When equipment starts to drift, Arva catches it, finds the root cause, and tells you what to fix and when, before a small fault becomes an expensive failure.
Reactive or calendar. Both waste money.
Most building maintenance is either reactive (fix it after it breaks, with the disruption and cost that brings) or calendar-based (service everything on a fixed schedule, whether it needs it or not). Both waste money and miss the faults that matter. Equipment almost always signals trouble before it fails, the problem is that no one is listening continuously.
How DigiNest does it
Continuous health monitoring
Every asset's behavior is tracked on the live building model.
Drift and fault detection
Arva learns each asset's normal behavior and flags deviation early: the leading indicator of a developing fault.
Root-cause diagnosis
Instead of a cryptic code, Arva explains the likely cause in plain language and recommends the fix.
Prioritized action
The work order arrives pre-drafted and routed through facility operations, with timing based on real urgency.
From normal behavior to fault before the failure.
Building a baseline of normal
Predictive maintenance starts with an observation period during which DigiNest learns the operating signature of each monitored asset. An AHU's typical runtime under different load conditions, a chiller's power draw at each stage of cooling, a pump's performance curve across its speed range these become the reference against which future readings are compared. Anomaly detection runs continuously against this learned baseline, not against a fixed threshold. This means drift in a gradually degrading asset is visible weeks before it would trip any conventional alarm.
Arva's diagnostic reasoning
When Arva detects deviation from an asset's baseline, it cross-references the pattern against known fault signatures comparing runtime trends, temperature curves, and performance data together. The output is a plain-language diagnosis: "Chiller-02 compressor showing reduced efficiency at full load. Pattern consistent with refrigerant loss. Recommend inspection within 7 days." This replaces the cryptic fault code that a technician would otherwise need to decode from a panel and pairs it with a timeline so the work can be planned, not rushed.
From diagnosis to work order
Arva's predictive alerts feed directly into the facility operations workflow. A diagnosed fault generates a draft work order pre-loaded with the asset ID, location, diagnostic summary, recommended action, and an urgency rating based on the estimated time-to-failure trajectory. The work order routes to the right technician based on skill profile and availability. Planned maintenance visits replace emergency call-outs parts are ordered in advance, work is scheduled at a convenient time, and the repair happens before the failure. Arva acts within limits you set. Low-risk interventions happen automatically. High-impact changes come to you with a recommendation your approval, one tap.
Protocols and asset coverage
DigiNest connects to building assets over BACnet/IP, Modbus TCP/RTU, and LON, reading the telemetry streams already exposed by your BMS and controllers. For assets not yet instrumented, DigiNest's edge hardware can be retrofitted non-invasively to add vibration, temperature, and current sensing without interrupting operations. The specific asset types covered are confirmed during your building walkthrough coverage scales from a single critical plant room to a full multi-site estate.
Frequently asked.
What is predictive maintenance for HVAC?
HVAC predictive maintenance uses continuous sensor data and AI to detect equipment faults before they cause failure. Rather than servicing on a fixed schedule, DigiNest's Arva agent monitors each asset's operating signature (runtime, temperature curves, power draw) and flags deviation early, typically weeks before a conventional alarm would trigger.
What is predictive maintenance?
Maintenance based on the actual condition of equipment rather than on a fixed schedule or on failure. Sensors and AI detect early signs of trouble so issues are fixed before they cause downtime.
How is this different from scheduled maintenance?
Scheduled maintenance services equipment on a calendar regardless of need. Predictive maintenance acts on real evidence of developing faults, reducing both unnecessary service and surprise failures.
What equipment can DigiNest monitor?
DigiNest monitors building assets connected through your existing systems and sensors. Confirm specific equipment coverage during your walkthrough.
Ready to make your
building act?
DigiNest connects to your existing BMS, meters, and sensors in days, not months. Book a personalised walkthrough for your site.