TECHNICAL PROGRAM

STEPHEN MCMILLAN, NAIT – ENGLISH KEYNOTE SPEAKER

Stephen McMillan is Dean of the School of Manufacturing & Automation and the School of Transportation at NAIT (Northern Alberta Institute of Technology). He previously served at BCIT in roles including Associate Dean of Mechanical Engineering, bringing over 15 years of experience in technical education and industry collaboration. He works closely with industry and academic partners to foster training in emerging manufacturing technologies and innovation, and collaborates with the Canadian Centre for Welding and Joining in advancing technical education and new technology adoption.

Abstract to come

ALEXANDRE GAUTHIER, Suncor Energy – FRENCH KEYNOTE SPEAKER

Alexandre Gauthier graduated from the Université de Sherbrooke in 2005 with a degree in mechanical engineering. He is a registered Professional Engineer with APEGA and the OIQ. He’s a member of the CMVA since 2009 and holds an Expert Vibration Analyst Category IV certification from Mobius Institute. He works in the oil and gas industry since 2005 and has developed extensive expertise in rotating equipment reliability, condition monitoring, advanced vibration analysis, troubleshooting, and asset repair. He currently serves as a Specialist Rotating Engineer at Suncor Energy within the Operational Improvement & Support Services function, supporting rotating equipment reliability across Suncor’s operating sites.

The Evolution of Machinery Diagnostics in the Age of Analytics and AI

Over the last several years, machinery diagnostics has been transformed by the growing use of data, advanced analytics, and now artificial intelligence. This keynote reflects that transformation through my own journey at Suncor, from hands-on vibration monitoring and in-depth analysis of critical equipment to an enterprise-wide role supporting rotating equipment reliability across multiple operating sites. From 2019 to 2024, I helped develop and apply a predictive and advanced analytics program for some of Suncor’s most critical assets, while remaining closely engaged in detailed monitoring, vibration analysis, and prognosis. This experience reinforced a key lesson: effective predictive work depends not only on data and models, but also on strong machinery knowledge, failure mode understanding, and sound engineering judgment. In this presentation, I will share lessons learned from that journey, including examples of early failure detection and the value of using analytics to extend specialist insight across a large fleet of equipment. I will also explore the next stage in this evolution: the use of generative AI and agentic AI to enhance learning, troubleshooting, and diagnostic support. These technologies offer real promise, but they also require clear guardrails around confidentiality, validation, and responsible use. In the end, AI can augment reliability professionals, but it does not replace their accountability.

L’évolution du diagnostic des machines à l’ère de l’analytique avancée et de l’intelligence artificielle

Au cours des dernières années, le diagnostic des machines a été profondément transformé par l’utilisation croissante des données, de l’analytique avancée et, maintenant, de l’intelligence artificielle. Cette conférence d’ouverture reflète cette transformation à travers mon propre parcours chez Suncor, depuis la surveillance vibratoire sur le terrain et l’analyse approfondie d’équipements critiques jusqu’à un rôle à l’échelle de l’entreprise en soutien à la fiabilité des équipements rotatifs sur plusieurs sites d’exploitation. De 2019 à 2024, j’ai contribué au développement et à l’application d’un programme d’analytique prédictive et avancée pour certains des actifs les plus critiques de Suncor, tout en demeurant étroitement impliqué dans la surveillance détaillée, l’analyse vibratoire et le pronostic. Cette expérience a renforcé une leçon essentielle : un travail prédictif efficace repose non seulement sur les données et les modèles, mais aussi sur une solide connaissance des machines, une bonne compréhension des modes de défaillance et un jugement d’ingénierie rigoureux. Dans cette présentation, je partagerai les leçons tirées de ce parcours, y compris des exemples de détection précoce de défaillances et la valeur de l’analytique pour étendre l’expertise des spécialistes à un vaste parc d’équipements. J’aborderai également la prochaine étape de cette évolution : l’utilisation de l’IA générative et de l’IA agentique pour améliorer l’apprentissage, le dépannage et le soutien au diagnostic. Ces technologies sont porteuses d’un réel potentiel, mais elles exigent aussi des balises claires en matière de confidentialité, de validation et d’utilisation responsable. En fin de compte, l’IA peut augmenter les professionnels de la fiabilité, sans toutefois remplacer leur responsabilité.

GARY (GUANGXING) ZHANG, Baker Hugues

Gary (Guangxing) Zhang has over 30 years of engineering consulting experience in the oil and gas, power generation, and chemical industries. He is an CAT IV certified Vibration Analyst and a registered Professional Engineer (P.Eng.) with the Association of Professional Engineers and Geoscientists of Alberta (APEGA), Canada, since 2005. His expertise includes the commissioning, diagnostics, and troubleshooting of large rotating and reciprocating machinery, such as steam turbines, generators, motors, gearboxes, compressors, pumps, and fans. His work focuses on vibration analysis, machinery reliability, and root cause failure investigations.

Case study of a Steam Turbine Generator

This presentation presents a case study of a steam turbine generator during the startup phase. It provides an overview of the machine train configuration, followed by a detailed analysis of the collected operational data to identify the root causes of machine trips through a series of diagnostic data plots, supplemented by photographic evidence for validation. The case study addresses multiple machine malfunction mechanisms, including light rotor–stator rubbing, rotor imbalance, and shaft misalignment. The analytical approach and findings offer a valuable reference for diagnosing and mitigating vibration-related issues encountered during machine startups.

JANOS PATTANTYUS, Hydro-Québec

Janos Pattantyus has worked for over 30 years as a rotating equipment specialist in machinery protection, vibration analysis and predictive maintenance. A member of the CMVAsince 1996, he is Category IV certified in vibration analysis and is a licensed engineer in the Province of Quebec.

Standardising Dynamic Vibration Data transmission with IEC

A standardized dynamic data model is being proposed within the IEC (International Electrotechnical Commission). Can this lead to opportunities for interoperability oof instruments, sharing of data and feeding AI models?

WAYNE REINHART, SPM North America

Wayne Reinhart is the Technical Solutions Manager at SPM North America. A certified reliability professional with more than 25 years of experience in maintenance, asset management, and industrial engineering, Wayne brings deep technical expertise and a results-driven approach to every project. He specializes in vibration analysis, ultrasound, infrared, and alignment technologies—helping organizations enhance reliability and achieve lasting improvements in equipment performance.

From Insight to Impact: Process Optimization in Action

Condition monitoring is a foundational predictive maintenance (PdM) technology used to reduce unplanned downtime through data-driven maintenance planning and reliability-centered decision-making. The condition monitoring ecosystem includes infrared thermography, ultrasound, motor circuit analysis (MCA), electrical signature analysis (ESA), motion amplification, vibration analysis, and the Shock Pulse Method (SPM). This study focuses on the application of vibration analysis and Shock Pulse diagnostics to support process optimization and improve asset reliability.

Condition monitoring technologies generate trendable datasets including time waveforms (TWF), FFT spectra, and amplitude-based condition indicators through time-domain, frequency-domain, and amplitude analysis techniques. Frequency-domain analysis, derived through Fast Fourier Transform (FFT) processing, enables identification of characteristic fault frequencies associated with imbalance, misalignment, mechanical looseness, rolling element bearing defects, cavitation, resonance, and gear mesh anomalies. Amplitude and rate-of-change analysis provide critical severity indicators that support alarm development, failure mode identification, and Remaining Useful Life (RUL) estimation. Collectively, these methodologies provide actionable reliability intelligence that supports precision maintenance and operational decision-making.

The next evolution of condition monitoring is process optimization through integrated reliability analytics. By combining condition monitoring data with operational variables and engineered performance calculations, organizations can develop visualized impact models that quantify the relationship between asset condition, operating efficiency, and process stability. Properly implemented optimization programs can improve asset lifecycle performance, operational safety, energy efficiency, and overall equipment reliability.

In centrifugal pumping applications, the Best Efficiency Point (BEP) is not necessarily achieved at maximum operating speed, but rather through the optimized relationship between flow rate, viscosity, temperature, hydraulic loading, and system demand conditions. These integrated reliability and process optimization methodologies provide a framework for improving equipment performance, reducing mechanical stress, and enhancing operational efficiency.

MOHAMMADALI SADAGHIAN, University of Manitoba *Student Presentation*

Mohammadali Sadaghian is a Ph.D. Candidate in Mechanical Engineering at the University of Manitoba, specializing in AI-driven condition monitoring, fault diagnosis, and prognostics of rotating machinery. He has over 10 years of experience in maintenance engineering within the railway industry, more than five years of expertise in condition monitoring, and approximately three years of experience applying artificial intelligence and machine learning to predictive maintenance. He is a CAT II Certified Vibration Analyst, a recipient of the Research Manitoba PhD Research Studentship Award, and the author of two peer-reviewed journal publications. He has also collaborated with a Winnipeg-based aerospace company to develop bearing condition monitoring software for industrial applications. His current research focuses on developing an AI agent for bearing condition monitoring, integrating advanced signal processing and machine learning to enhance predictive maintenance and machinery health assessment.

An AI Agent for Autonomous Bearing Condition Monitoring and Predictive Maintenance

Rolling element bearings are among the most critical components in rotating machinery, and their unexpected failure can result in costly downtime, reduced productivity, and safety risks. Although recent advances in artificial intelligence have significantly improved bearing fault detection and diagnosis, most existing approaches remain model-centric, requiring considerable human expertise for signal interpretation and maintenance decision-making. Furthermore, general-purpose large language models (LLMs) provide powerful reasoning capabilities but are not designed to execute reliable condition monitoring workflows, directly analyze vibration data, or consistently integrate domain-specific diagnostic knowledge. These limitations motivate the development of specialized AI agents tailored to industrial maintenance applications.

This work presents the development of an intelligent AI agent for autonomous bearing condition monitoring by integrating advanced signal processing, machine learning, engineering knowledge, and large language model reasoning within a unified decision-support framework. The proposed agent performs end-to-end analysis, including vibration signal preprocessing, feature extraction, health indicator generation, fault diagnosis, severity assessment, and interpretation of results. Unlike conventional diagnostic models that primarily provide fault classifications, the agent explains its conclusions, recommends appropriate analyses, and supports maintenance engineers through interactive, context-aware reasoning.
The proposed architecture is modular and designed to accommodate data collected under constant operating conditions while integrating both data-driven models and physics-informed knowledge. By orchestrating specialized analytical tools rather than relying solely on a general-purpose LLM, the AI agent delivers consistent, explainable, and application-oriented diagnostics suitable for real-world industrial environments. Preliminary development demonstrates the feasibility of the proposed framework for reducing diagnostic complexity, improving maintenance decision-making, and enabling continuous learning as new machine data become available.

The proposed AI agent represents a step toward next-generation intelligent maintenance systems that combine autonomous analysis, explainable diagnostics, and human–AI collaboration to enhance predictive maintenance and machinery reliability.

HEBERT LIBREROS, PdMA

Hebert Libreros is an Industrial Engineer with an MBA from the University of California, Los Angeles (UCLA) and more than 35 years of experience in manufacturing and industrial reliability. He has served as Regional Manager at PdMA Corporation for the past 22 years, leading the company’s international technical and marketing initiatives. Before joining PdMA in 2004, Hebert held engineering and plant management roles across manufacturing facilities in Latin and North America. During this time, he successfully led the implementation of Lean Manufacturing and Total Productive Maintenance (TPM), significantly improving overall equipment effectiveness and operational performance. Throughout his career at PdMA, Hebert has worked with more than 500 industrial facilities worldwide, helping organizations improve motor reliability, increase equipment uptime, and optimize maintenance strategies. His practical experience and results-driven approach make him a sought-after speaker on predictive maintenance, asset reliability, and operational excellence.

From Portable Tester and Permanently Installed Ecosystem for Electric Motor Testing

Global competition and economic uncertainty have made equipment reliability a top priority. Unplanned downtime costs industrial manufacturers $50 billion a year; the average manufacturer loses roughly 800 hours annually. Combined with an aging workforce and the loss of long-term motor expertise, these pressures make an effective electric motor test program essential. Motor reliability testing can take different forms, the purpose of this paper is to present electrical testing as an element of world-class reliability programs.

The current practices – Reliability of critical electrical system can be evaluated through Six Fault Zones: Power Quality, Power Circuit, Insulation, Stator, Rotor, and Air Gap. With variable test voltage from 250 to 5,000 V, a portable system can perform off-line and on-line, torque and efficiency analysis on motors of any size, type, or condition. For safe operation and testing, the system can be paired with permanently installed test points measuring three voltages and three currents on running three-phase motors, reducing voltage and current to safe levels per OSHA and NFPA 70E. Technicians collect data in seconds from a single connection point, without full PPE or lock-out/tag-out.

The new Ecosystem with 24/7 monitoring – Adding PdMAeye®, permanently installed technology that monitors critical motors 24/7 and sends immediate cell phone notification when a fault is detected. It issues color-coded alarms and stores data in the cloud for access anywhere. This enables data-driven and earlier failure detections. At a remote pumping station, PdMAeye caught rotor degradation early, letting the company schedule repairs on its own terms while raising test frequency from quarterly to continuous with no added manpower. At a cement plant, data revealed elevated machine train frequencies traced to debris on the grinding rollers.

We will present how to maximize uptime, safety, and return on investment with applying closer monitoring to motor criticality within manpower and budget constraints.

JOSE RABELL, SDT Ultrasound Solutions

Jose Rabell has over 20 years of condition monitoring experience. He is a CAT III vibration analyst certified by Technical Associates of Charlotte, and MLT1 by ICML. He has worked at several pulp & paper mills across USA & Canada; from asset management consultant to reliability manager of a paper mill.

PdM 4.0 wireless vibration sensors

More and more we see wireless vibration sensors coming up in the market. How do they work? How is the data collected and transmitted? How is data analyzed and transformed into information? What assets should be covered? Are these sensors the right tool for me? Let’s explore the technology behind them and how they can make PdM a success.

GILLES LANTHIER, SDT Ultrasound Solutions

Gilles Lanthier is the SDT Ultrasound Solutions District Manager for Quebec and Maritimes. He is a CMVA CAT III Vibration Analyst, a Level 2 Ultrasound Specialist, and a Certified Reliability Leader with more than 25 years experience in the field.

Ultrasound & Electrical Systems

Ultrasound has traditionally been used in electrical condition monitoring as a screening technology to identify abnormal acoustic emissions associated with electrical discharge. However, the potential of the technology extends considerably beyond simply determining whether an abnormal condition exists. This development project focuses on advancing ultrasound from an inspection tool toward a more comprehensive electrical diagnostic methodology capable of supporting fault identification, localization, confirmation, and maintenance decision-making.

Electrical conditions such as corona, tracking, arcing, partial discharge, loose connections, and deteriorated contacts can generate ultrasonic energy before obvious physical deterioration or significant temperature increases are detected. By combining airborne and structure-borne ultrasound with technologies such as the SDT 340, acoustic imaging systems such as SonaVu, thermography, time waveform analysis, and heterodyned/demodulated spectral analysis, considerably more information can be obtained about the nature and location of abnormal electrical activity.

A fundamental objective of this development work is to establish diagnostic signatures based on confirmed field conditions. Rather than relying solely on ultrasonic amplitude, the methodology evaluates signal characteristics including impulsiveness, repetition, broadband energy, modulation, stability, and relationships with the 60 Hz electrical power cycle. These characteristics are then correlated with equipment operating conditions, thermographic findings, acoustic localization, and ultimately physical inspection of the affected component.

This approach is particularly important because no single condition-monitoring technology provides complete diagnostic certainty. Ultrasound may detect electrical activity without a significant thermal anomaly, while thermography may identify resistance-related heating without significant ultrasonic emissions. Combining technologies therefore provides a stronger diagnostic foundation.

The ultimate goal of this project is to develop a repeatable, evidence-based electrical ultrasound diagnostic process. By building a library of confirmed defects, associated waveforms, spectra, acoustic images, thermograms, and physical findings, field observations can progressively be transformed into practical diagnostic knowledge—improving electrical reliability while reducing unnecessary exposure to energized equipment.

MATTHEW FIRTH, NB Power Point Lepreau