Projects/Mining & Industry/Predictive Maintenance for Petrochemical Assets

Case Study

Predictive Maintenance for Petrochemical Assets

Catch the fault before the trip.

AI-driven monitoring and diagnostics for critical rotating equipment, built on multi-sensor data from compressors and centrifuges.

Industry

Petrochemical

Status

Completed

Timeline

1404 (SH)

Partners

Bandar Imam Petrochemical, Baspar Unit

Predictive Maintenance for Petrochemical Assets

FIX IT
BEFORE IT FAILS

01 / The Problem

Unplanned trips are expensive.

A single compressor trip can halt a production line. Traditional threshold alarms fire late and say little about the underlying fault.

Costly unplanned shutdowns

Alarms that come too late

Hard-to-diagnose root causes

Maintenance on fixed schedules, not condition

Early warning

fewer trips

02 / Our Solution

From sensor data to early warnings.

Each asset is equipped with multi-sensor systems. Our models learn normal behaviour, predict warnings before they occur, diagnose faults and detect anomalies to prevent trips.

Technical details

Warning Prediction

Forecast alarms before they trigger

Fault Diagnostics

Identify the likely root cause

Anomaly Detection & Trip Prevention

Spot abnormal behaviour early

03 / System Architecture

From the plant floor to insight.

Designed to integrate with existing plant instrumentation.

Sensors

(Multi-sensor per asset)

Data Pipeline

(Historian integration)

AI Models

(Prediction & diagnosis)

Dashboard

(Alerts for engineers)

04 / Technical Details

Assets and features.

Predictive maintenance across Bandar Imam Petrochemicals. Each asset is equipped with multi-sensor systems enabling advanced AI-driven monitoring and diagnostics.

Monitored assets