Reliability Engineer & AI Systems Developer

YUSUF
OMOLOJA

PACIFIC ARTIS

Bridging 15+ years of industrial reliability engineering with cutting-edge AI agent development. RCM2 specialist, digital twin researcher, and builder of intelligent maintenance systems for the mining and resources sector.

15+
Years Experience
RCM2
Moubray Certified
AI
Agent Developer
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Current Focus
PhD Research – Mining Equipment Reliability
Hybrid edge-cloud digital twin framework
Latest Build
RCM2 FMEA AI Agent
Automated failure mode analysis · Outotec P0668 Thickener
Expertise
SAP PM · IBM Maximo · Digital Twins
Predictive Maintenance · Vibration · Oil Analysis
Location
Australia – Mining & Resources Sector
Available globally for consulting engagements
Yusuf Oluwasola Omoloja
About

ENGINEER.
RESEARCHER.
BUILDER.

I am Yusuf Oluwasola Omoloja, a reliability engineer with over 15 years of hands-on experience in maintenance strategy development, asset management systems, and digital transformation for industrial operations.

My career has been defined by a single question: how do we make maintained assets more intelligent? From deploying SAP PM and IBM Maximo for mining operations, to building AI agents that generate rigorous RCM2 FMEAs in minutes, I sit at the intersection of traditional engineering discipline and modern AI capability.

Currently pursuing a PhD focused on scalable hybrid reliability frameworks that combine edge computing and cloud AI for real-time equipment health monitoring in the mining sector.

RCM2 – Moubray SAP PM IBM Maximo Digital Twin Predictive Maintenance Vibration Analysis Oil Analysis Thermography AI Agents Django Claude API Python
Services

WHAT I DELIVER

A full suite of reliability and digital services for mining, resources, and industrial operations.

01 ⚙️
RCM2 & FMEA

Rigorous Reliability Centred Maintenance analysis using John Moubray's RCM2 methodology. Function-driven failure analysis, consequence categorisation, and defensible maintenance task selection — now AI-accelerated.

02 📊
Predictive Maintenance

Designing and implementing predictive maintenance programmes using vibration analysis, oil analysis, thermography, and ultrasound. From condition monitoring strategy to SAP PM integration.

03 🗄️
SAP PM & Maximo Consulting

Customising, deploying, and training maintenance teams on SAP Plant Maintenance and IBM Maximo. Functional location structures, maintenance plans, work order management, and KPI reporting.

04 🤖
AI Agent Development

Building intelligent maintenance agents that automate FMEA generation, dynamic risk scoring, and PM task management. Powered by Claude API with domain-specific reliability engineering knowledge encoded.

05 🔄
Digital Twin Deployment

Developing digital representations of physical assets to simulate performance, optimise maintenance planning, and support real-time decision-making across mining and industrial operations.

06 🎓
Training & Capability Building

RCM2 methodology training, FMEA facilitation workshops, SAP PM user training, and reliability engineering upskilling programmes for maintenance teams in the mining and resources sector.

AI Products

INTELLIGENT
MAINTENANCE AGENTS

The next generation of reliability engineering — where domain expertise meets AI capability.

Live – Available Now
RCM2 FMEA AGENT

Upload your SAP asset hierarchy and maintenance manual. The agent applies John Moubray's RCM2 decision logic — consequence-first, task-selection second — and generates a complete, formatted FMEA workbook with 64+ failure modes, risk scoring, PM task register, and hidden failure identification. What previously took 3–5 engineering days now takes minutes.

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Coming Soon
DYNAMIC FMEA ENGINE

A living FMEA that connects to your SAP PM order history and DCS historian. Risk scores update automatically as real failure data comes in. Likelihood scores reflect your actual site experience, not engineering estimates.

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In Development
MAINTENANCE WI GENERATOR

Auto-generate technician-ready Work Instructions from FMEA records. ISO 9001 format with PPE requirements, step-by-step procedures, acceptance criteria, and SAP equipment references.

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Research

PHD RESEARCH

"A scalable predictive maintenance digital twin (PdMDT) for industrial assets."

My doctoral research addresses a fundamental problem in mining maintenance: how do you build a reliability framework that works at the edge of connectivity — in remote pit environments, underground operations, and distributed plant configurations — while still leveraging the power of cloud-based AI and digital twin technologies?

The research combines RCM2 methodology as the analytical foundation with machine learning for failure prediction, edge computing for real-time condition monitoring, and cloud synchronisation for fleet-wide pattern recognition. The goal is a framework that is rigorous enough to satisfy regulatory requirements and practical enough to work in the real conditions of Australian mining operations.

Research Area
Mining Equipment Reliability
Framework
Hybrid Edge-Cloud Architecture
Methods
RCM2 · ML · Digital Twin · IoT
Sector
Mining & Resources – Australia
Blog
Follow PhD Progress →
Contact

START A CONVERSATION

Whether you need RCM2 consulting, SAP PM implementation, AI agent development, or simply want to discuss reliability engineering — get in touch.

📍
Location
Australia
🌐
🔗
✍️
Consulting Availability
Available for remote and on-site engagements across Australia. Typical response within 24 hours.