Industrial AI & Reliability Expert
I help industrial organizations harness vibration data, IoT sensor networks, and AI-driven analytics to eliminate unplanned downtime, reduce maintenance costs, and build lasting reliability programmes.
Dr. [Your Name]
Ph.D. Mechanical Engineering · CRE · CMRP
// live vibration signal — 1X running speed
Background
With a Ph.D. in Mechanical Engineering and over two decades of hands-on experience, I have built my career at the convergence of classical reliability theory and modern data science. My work spans rotating machinery diagnostics, structural health monitoring, and the practical deployment of machine learning models in industrial environments.
I began my career on the factory floor — deploying accelerometers on gearboxes and turbines, building signal processing pipelines, and translating waveform anomalies into maintenance decisions. That grounding shapes everything I do: every algorithm I develop is tested against real physics, not just benchmark datasets.
Today I advise manufacturers, utilities, and asset-intensive industries on transitioning from reactive maintenance to predictive programmes underpinned by IoT sensor infrastructure, cloud data platforms, and AI models that provide actionable prognostics — all integrated with existing CMMS and ERP systems.
My research has been published in leading journals including Mechanical Systems and Signal Processing, Reliability Engineering & System Safety, and IEEE Transactions on Industrial Electronics, and recognised with patents covering novel fault-detection algorithms and sensor fusion methodologies.
Ph.D., Mechanical Engineering
[Your University] · [Year] · Dissertation: Vibration-Based Fault Diagnosis in Rotating Machinery
M.Sc., Mechanical Engineering
[Your University] · [Year] · Structural Dynamics & Signal Processing
Certified Reliability Engineer (CRE)
American Society for Quality (ASQ)
Certified Maintenance & Reliability Professional (CMRP)
Society for Maintenance & Reliability Professionals (SMRP)
Previous: Senior Reliability Engineer, [Company]
Led IIoT rollout across 12 manufacturing sites; reduced unplanned downtime 38%
Consulting Services
From sensor selection to board-level reliability strategy — end-to-end expertise without the consultancy overhead.
Vibration Data Collection & Monitoring
Design and deploy robust vibration measurement programmes for rotating and reciprocating machinery.
Reliability Analysis
Systematic approaches to understanding, quantifying, and eliminating failure modes before they cause downtime.
Industrial AI & Predictive Analytics
Physics-informed machine learning models that deliver real prognostic value — not lab-only demos.
CMMS Integration
Connect your condition monitoring data to your maintenance management systems for closed-loop reliability.
IoT & IIoT Architecture
End-to-end industrial IoT strategy — from edge hardware to cloud analytics — that scales with your operation.
Data Management & Engineering
Build the data infrastructure that makes your AI and analytics programmes reliable, repeatable, and scalable.
Research & Intellectual Property
Peer-reviewed research and patented innovations spanning vibration diagnostics, prognostics, and industrial AI.
Physics-Informed Neural Networks for Bearing Fault Prognosis Under Variable Operating Conditions Journal
Mechanical Systems and Signal Processing, Vol. 210 · DOI: 10.1016/j.ymssp.2024.XXXXX
Remaining Useful Life Estimation for Industrial Gearboxes Using Transformer-Based Temporal Attention Conference
IEEE International Conference on Prognostics and Health Management (ICPHM 2024)
System and Method for Adaptive Vibration-Based Anomaly Detection in Rotating Machinery Patent
US Patent No. US11,XXX,XXX · Granted March 2023
A Federated Learning Framework for Privacy-Preserving Predictive Maintenance Across Multiple Plants Journal
Reliability Engineering & System Safety, Vol. 235
Multi-Sensor Fusion Method for Early-Stage Compound Fault Detection in Wind Turbine Drivetrains Journal
IEEE Transactions on Industrial Electronics, Vol. 69, No. 11
IoT-Enabled Condition Monitoring Architecture for Distributed Asset Fleets: A Case Study in Petrochemical Processing Conference
ASME Turbo Expo 2022 · Rotterdam, Netherlands
Apparatus and Method for Real-Time Edge-Based Vibration Feature Extraction for Embedded Predictive Maintenance Patent
US Patent No. US10,XXX,XXX · Granted July 2021
Explainable Artificial Intelligence for Fault Classification in Industrial Rotating Machinery: Methods and Benchmarks Journal
Expert Systems with Applications, Vol. 162
Selected Projects
A selection of consulting and research engagements across energy, manufacturing, and process industries.
Smart Grid Pump Fleet Monitoring — Water Utility
Deployed a wireless IIoT vibration monitoring network across 240 pumping stations. Developed edge AI models for cavitation and imbalance detection, integrated alerts with IBM Maximo for automated work-order generation.
▲ 42% reduction in unplanned pump failures · ROI achieved in 14 months
Predictive Maintenance Programme — Automotive Tier-1 Supplier
Built a physics-informed ML model for spindle bearing degradation prognosis on CNC machining centres. Integrated RUL predictions with SAP PM scheduling module. Reduced scrap from tool/bearing failures by 31%.
▲ $2.1M annual maintenance cost savings · 31% scrap reduction
Wind Turbine Gearbox Health Management — Renewable Energy OEM
Developed a multi-sensor fusion algorithm combining vibration, oil particle, and thermal data for compound fault detection in wind turbine gearboxes. Resulted in two filed patents and a published journal article.
▲ Detected 94% of incipient faults ≥6 weeks before failure
Historian Modernisation & Analytics Platform — Midstream Operator
Led migration from legacy Wonderware Historian to a cloud time-series lakehouse. Designed feature engineering pipelines for compression train monitoring. Trained operations staff on new dashboarding and alert workflows.
▲ 5× faster anomaly detection · 60% lower data infrastructure cost
CMMS Implementation & Data Governance — Mining Company
Led greenfield eMaint CMMS deployment across three mine sites. Established asset hierarchy, PM templates, and KPI reporting. Developed integration layer connecting vibration monitoring alerts to work-order workflow.
▲ 28% improvement in PM compliance · Full data governance framework established
Federated Predictive Maintenance — Multi-Site Industrial Research
Led a research collaboration between three universities and two industry partners to develop a federated learning framework allowing plants to collaboratively train prognostic models without sharing raw sensor data.
▲ Published in Reliability Engineering & System Safety (2023) · 2 industry deployments
Get In Touch
Whether you're scoping a new predictive maintenance programme, need a technical review of your existing IIoT architecture, or require expert witness support — I'd be glad to discuss how I can help.
Typical engagements range from a focused 2-day diagnostic assessment to multi-year strategic partnerships. All enquiries are responded to within 48 hours.
Your information is kept confidential and never shared with third parties.