Data Analytics & Machine Learning Implementation for Gas Turbine Compressor Reliability Improvement

Overview

A leading oil & gas company in Indonesia initiated a Data Analytics and Machine Learning (ML) implementation program to improve Gas Turbine Compressor reliability, optimize production performance, and reduce the risk of unexpected shutdown events.

The project focused on leveraging existing AVEVA PI System infrastructure by integrating advanced analytics capabilities and custom Machine Learning models to provide early anomaly detection and predictive insights for critical rotating equipment.

1. Business Challenge & Problem Definition

The customer operates multiple Gas Turbine Compressor units across remote production sites with large volumes of operational data continuously collected through the existing AVEVA PI Historian System.

However, the increasing complexity and volume of operational data created challenges in extracting meaningful insights and predicting potential equipment failures before they impacted production.

Key Challenges:

• Massive volumes of operational data required advanced processing and analytics capabilities.
• Production sites were located in remote areas, while the centralized PI Server infrastructure was located at the Jakarta headquarters.
• Existing AVEVA PI capabilities required enhancement through customized Machine Learning solutions.
• Development of a custom ML model was required as an add-on analytics layer integrated with the existing PI ecosystem.
• The customer required centralized KPI dashboards and monitoring capabilities for all Gas Turbine Compressor units.

2. Technology Solution

A comprehensive Industrial Data Analytics and Machine Learning solution was implemented by integrating existing operational data infrastructure with advanced analytics capabilities.

Solution Components:

1. Industrial Data Connectivity

• Implementation of OPC Kepware as an industrial communication layer to ensure reliable data acquisition from field equipment.
• Standardization of equipment data collection from multiple operational sources.

2. AVEVA PI Data Infrastructure Enhancement

• Utilization of the existing AVEVA PI System as the centralized operational data historian.
• Optimization of data storage, accessibility, and historical analysis capability.
• Implementation of data backup and recovery mechanisms to ensure data reliability and availability.

3. Custom Machine Learning Analytics

• Development of customized Python-based Machine Learning models integrated with the AVEVA PI environment.
• Development and testing of anomaly detection algorithms using historical operating data.
• Model optimization and validation against existing equipment performance data.
• Generation of predictive insights to identify abnormal operating conditions before equipment failure occurs.

3. Solution Architecture

Gas Turbine Compressor & Field Instrumentation
↓
OPC Kepware Connectivity Layer
↓
AVEVA PI Historian System
↓
Python-Based Machine Learning Model
↓
Anomaly Detection & Predictive Analytics
↓
KPI Dashboard & Reliability Monitoring

4. Result & Business Impact

The implementation successfully transformed traditional equipment monitoring into a proactive, data-driven reliability management approach.

Key Benefits:

✅ Predictive Anomaly Detection
Machine Learning models provide early warning indicators of abnormal Gas Turbine Compressor behavior, enabling proactive maintenance actions before unexpected shutdown occurs.

✅ Reduced Unplanned Downtime
Early identification of equipment degradation helps minimize production losses and improve equipment availability.

✅ Optimized Production Performance
Operational insights from historical and real-time data support better production optimization and faster decision-making.

✅ Easy Operation & Centralized Monitoring
Engineering and management teams gain improved visibility of compressor performance through integrated monitoring and analytics dashboards.

✅ Improved Data Utilization
Existing AVEVA PI historical data is transformed into actionable intelligence through advanced analytics and Machine Learning.

✅ Scalable Analytics Platform
The solution provides a foundation for future implementation of Predictive Maintenance, Asset Performance Management, and AI-driven optimization initiatives.

Key Achievement Summary

Digital Reliability Transformation Outcome

From reactive equipment monitoring → Predictive, intelligent, and data-driven asset reliability management.

This implementation established a foundation for advanced AI-based Predictive Maintenance, enabling the oil & gas operation team to improve reliability, optimize production, and reduce the risk of unplanned Gas Turbine Compressor shutdowns.