Predictive Maintenance AI for Industrial Systems
Reduce downtime and optimize performance by predicting failures before they happen.
The Cost of Reacting Too Late
Most maintenance strategies are reactive — fixing problems after they occur.
How Arcus AI Solves This
Arcus AI ingests continuous time-series data from your sensors, historians, and control systems, then applies purpose-built machine learning models to understand normal equipment behavior — and alert you the moment something deviates from it.
Unlike threshold-based alerting, our models learn the unique operating signature of each asset, catching subtle degradation patterns weeks or months before a failure manifests.
Failure Prediction & Remaining Useful Life
LSTM and transformer-based models go beyond binary alerts — delivering a concrete, confidence-scored forecast for every critical component:
- RUL estimation: models calculate the Remaining Useful Life of each component, giving teams a quantified timeline rather than a vague warning
- 30–90 day advance prediction: failures are flagged weeks or months before they occur, creating a wide window for planned intervention
- Severity ranking: predicted failures are ranked by severity, ensuring the most critical risks receive immediate attention
- Actionable recommendations: each prediction comes with specific, suggested maintenance actions — not just a flag
Maintenance Schedule Optimization
Traditional time-based maintenance wastes resources — you either over-service healthy equipment or miss components that are quietly degrading. Arcus AI replaces fixed intervals with condition-based scheduling:
- Condition-based scheduling: maintenance is triggered by actual equipment health, not fixed calendar intervals
- Failure probability scoring: the system identifies components actively degrading before they cause unplanned downtime
- Production window awareness: maintenance tasks are scheduled around your operational windows to minimize disruption
- CMMS / ERP integration: optimized schedules are pushed directly into your existing maintenance management or ERP system, with no manual re-entry
Real-Time Anomaly Detection
Arcus AI continuously monitors live sensor streams, catching complex anomalies that single-threshold rules will always miss:
- Sub-second latency monitoring: live sensor streams are analyzed in real time, ensuring no anomaly window goes undetected
- Multivariate detection: the system analyzes combinations of sensors simultaneously, catching patterns that no individual threshold could identify
- Structured alerts: every anomaly triggers a detailed alert, not just a raw alarm — including context, severity, and timing
- Root-cause hypothesis: the system isolates which sensor combination triggered the event and proposes the most likely underlying cause
Explainable AI & Operator Transparency
Arcus AI is built for engineers who need to understand and trust every recommendation — not just act on it:
- Plain-language alert explanations: every alert describes in plain English which parameters changed, by how much, and why it was flagged
- Feature importance charts: visual breakdowns show which sensor readings and variables contributed most to each prediction
- Trend visualizations: engineers can inspect the underlying data trends that drove a model recommendation before acting on it
- No black-box outputs: every prediction is traceable back to specific data inputs and model logic
Equipment Health Monitoring
Arcus AI builds and maintains a continuous, asset-specific health score for every piece of equipment in your fleet:
- Health scoring: every asset receives a live, dynamically updated health score based on its current operating condition
- Normal baseline establishment: the system learns each asset’s unique operating signature and sets baselines specific to that machine, not a generic average
- Early deviation flagging: deviations from baseline are detected and flagged as they emerge, long before they escalate into failures
- Fleet-wide unified dashboard: operators see health trends across all assets in a single view, eliminating blind spots
Seamless IoT & Industrial Integration
Arcus AI connects to your existing infrastructure from day one — no new hardware purchases, no rip-and-replace:
- OPC-UA, MQTT & Modbus support: native connectors for the three most widely deployed industrial communication protocols
- REST API & database connectivity: integrates with any platform exposing a REST API or direct database interface
- Zero new hardware required: connects to your existing sensors, PLCs, gateways, and control systems as-is
- Historical data backfilling: years of existing process data are used to pre-train models before go-live
Business Impact
Across manufacturing, energy, and transportation deployments, Arcus AI customers consistently report measurable improvements within the first 90 days of production operation.
Unplanned Failures
By catching degradation weeks before it becomes a breakdown, customers eliminate the majority of emergency work orders — the most expensive and disruptive maintenance events.
Asset Lifespan Extension
Condition-based maintenance prevents over-servicing healthy components and eliminates run-to-failure cycles — both of which accelerate wear and shorten asset life.
Maintenance Costs
Fewer emergency callouts, optimized parts procurement, and reduced labor hours combine to deliver a significant reduction in overall maintenance spend within the first year.
Equipment Efficiency
Higher uptime and more predictable production windows translate directly into increased throughput — without adding new capital equipment.
Typical ROI Payback Period
For mid-to-large industrial operations, the avoided cost of a single major unplanned outage is often sufficient to recover the full cost of the pilot deployment.
Time to First Predictions
Integration, model training, and validation are completed within a structured 90-day pilot — so your team sees real results before committing to full deployment.
How we quantify impact for your operation
During the pilot scoping phase, Arcus AI works with your reliability and finance teams to establish a baseline — documenting current failure rates, downtime costs, and maintenance spend per asset class. This baseline is used to calculate a site-specific ROI projection before the pilot begins, and is revisited at 30, 60, and 90 days to track realized savings against the forecast.
Industries