PlantIQ

Reliability & Asset Intelligence

Know which assets need attention, and why, before the issue becomes a larger loss.

Combine operating data, asset history, plant context, and optional expert review to identify degradation, inefficient operating regions, and recommended checks, without collapsing everything into a generic predictive-maintenance claim.

Book a plant intelligence reviewAll solutions

Evidence-first asset intelligence with an explicit capability ladder, optional Expert-Enabled Managed Intelligence.

Asset health · Pump P-204

Illustrative example: sample data, not a customer result.

Expert-Enabled Managed Intelligence

Reliability & Asset Intelligence can be delivered as Expert-Enabled Managed Intelligence: continuous monitoring plus scheduled review by qualified specialists, with prioritized recommendations and outcome tracking.

Explore Managed Intelligence

Problem

Alarm volume is not the same as actionable reliability intelligence.

Maintenance and reliability teams often face recurring trips, drifting operating points, and incomplete history scattered across SCADA, historians, CMMS notes, and specialist knowledge.

Without evidence packaged around the asset and process context, attention goes to the loudest alarm rather than the most operationally significant risk.

Asset health · Pump P-204

Illustrative example: sample data, not a customer result.

Connect & model

What PlantIQ connects and models

Capability is presented as an intelligence ladder: visibility, rule-based findings, expert diagnosis, and advanced models only where data quality justifies them.

Never all four collapsed into an unsupported predictive-maintenance promise.

  • Pumps, motors, compressors, fans, gearboxes, boilers, chillers, and process skids
  • Run status, speed/frequency, current/power, pressure, flow, temperature, and valve state
  • Trip/alarm codes, vibration where available, and process context
  • Maintenance history or fact tables where available

Decision questions this solution answers

  1. 1

    Which equipment requires attention first?

  2. 2

    What evidence supports the recommendation?

  3. 3

    Is the issue process-related, maintenance-related, electrical, or operational?

  4. 4

    Did the check or intervention improve the operating signature?

Core experiences

  • Asset operating views

    Current state and trends for critical rotating equipment and utility assets in plant context.

  • Rule-based condition findings

    Limits, operating envelopes, repeated events, and relationships that produce reviewable evidence packets.

  • Recurring trip and restart patterns

    Chronologies that help teams see whether an issue is isolated or systemic.

  • Expert review queue

    When Managed Intelligence is enabled, specialists review findings with plant context and recommend practical checks.

  • Action and verification

    Track recommendations, owners, status, and before/after operating results.

Illustrative example: pump or compressor reliability

Example workflow

  1. 1.Detect an operating-point shift or abnormal power/pressure relationship.
  2. 2.Package evidence: trends, repeated trips, and process context.
  3. 3.Expert annotates possible causes and recommended field checks.
  4. 4.Plant team executes through existing processes; PlantIQ verifies post-action improvement.

The recommendation stays honest about uncertainty. Remote evidence may support prioritized checks without claiming a single confirmed root cause.

Data requirements and realistic limits

Required

  • Asset identity and process association in the plant model
  • Core operating signals relevant to the asset class
  • Agreed limits, envelopes, or comparison baselines

Helpful

  • Vibration and additional condition indicators
  • Maintenance history and intervention annotations
  • Trip/alarm detail and restart context

Limits

  • Advanced models require sufficient data volume, quality, labels, and validation.
  • Field inspection may still be required before diagnosis or action.
  • PlantIQ does not remotely control equipment or replace customer maintenance authority.

Guardrails

  • Not autonomous predictive maintenance.
  • Not a CMMS replacement.
  • Not a guarantee of failure prevention.

Business value and measurable KPIs

  • Time to prioritize and review significant asset findings
  • Recurring trip or abnormal-event reduction after verified actions
  • Operating-envelope adherence and efficiency drift indicators
  • Action closure and before/after verification rates

Personas

Outcomes by role

The same plant model, composed for the decisions each stakeholder owns.

  • Maintenance / reliability

    • Prioritize work with evidence packets
    • Separate process, electrical, and mechanical hypotheses more clearly

  • Plant engineering

    • See asset behavior in process context
    • Improve reusable checks and playbooks over time

  • Plant manager

    • Focus attention on assets linked to operational loss or risk
    • Track whether interventions changed outcomes

Deployment path

  1. 1

    Select critical assets

    Start with a focused set of pumps, compressors, or utility assets.

  2. 2

    Connect signals and context

    Integrate operating data and build asset/process relationships.

  3. 3

    Enable findings

    Configure visibility and rule-based findings before adding expert cadence.

  4. 4

    Add Managed Intelligence

    Layer scheduled specialist review, recommendation cards, and verification.

Related solutions

Expand on the same operational model

Next step

Start with Reliability & Asset Intelligence

Bring us the process, asset, utility system, or production loss that matters. We will assess available data and define a focused first deployment.

Book a plant intelligence reviewExplore the platform