Our Offerings
MEC provides rotating-equipment reliability consulting and engineer-reviewed SAHAS functional-health assessments.
SAHAS evaluates available functional capacity relative to reference behavior and defined functional requirements. Applicability depends on the defined task, relevant variables, operating coverage, sensor and data quality, and observability.
SAHAS is not presented as live monitoring, automated ingestion or analysis, automatic reporting or alerts, automated recommendations, control, a dashboard, a native integration, or a guarantee.
Consulting
Rotating equipment & reliability consulting powered by data-calibrated, physics-based models
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Dry gas seal and critical failure investigations
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Motor-pump and rotating equipment troubleshooting
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RCM studies and maintenance strategy optimization
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System modeling, digital twins, and reliability assessments
SAHAS
System Asset Health Assurance Service
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Functional-health assessment from prepared operating data
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SMAC: SAHAS Machine Available Capacity
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Comparison with reference behavior and defined functional requirements
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MEC engineer-reviewed findings for the approved scope
Why are we here
Need from the Industry
SAHAS addresses a defined engineering-assessment task using physics, information theory, prepared operating data, reference behavior, functional requirements, and observability.
90+ years Experience in Diagnostics of Industrial Equipment
Our team has 90 years of accumulated experience in the Energy Industry. Machine Essence’s experience is a concentration of subject matter experts with real-life experience both on and out of the field with the tools to resolve some of the most challenging aspects in diagnostics.
Business Opportunity
Applicability depends on the approved asset, dataset, relevant variables, operating coverage, sensor and data quality, reference behavior, observability, and the defined task. SAHAS findings retain MEC engineering judgment and operator context.
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System Asset Health Assurance Service
S1 — Functional Health Assessment
Assesses available functional capacity from prepared operating data against reference behavior and defined functional requirements, with SMAC-centered interpretation and MEC engineering review. Technical maturity: Deployed.
S2 — Component Diagnostics
A roadmap stage using physics-based digital-twin parameter tuning to identify degraded parts. No current implementation, configuration, availability, or timing is claimed here.
S3 — Fault Compensation
A roadmap stage using an S2-tuned digital twin in what-if analysis to assess fault-compensating operating strategies. No current implementation, configuration, availability, or timing is claimed here.
S4 — Condition and Fault Forecasting
A roadmap stage using DEG-based methods for prognosis, fault evolution, and maintenance-planning support. No current implementation, confidence behavior, availability, timing, or performance is claimed here.
The Industries
Oil & Gas
Chemical and Petrochemical
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Power Generation
These industries are characterized by a large population of critical assets, and managing those assets is a challenge in itself. Many are safety- and production-critical, where maintenance and reliability are of utmost importance.
Applicability depends on the asset, defined functional requirements, available data, reference behavior, operating coverage, and observability. SAHAS findings are engineering estimates for an approved scope, not guarantees of fault identification, timing, safety, availability, cost, or profit.
Value Proposition
SAHAS assesses a machine's available capacity to perform its required function, using physicsand information-theory based methods on operating data for earlier, explainable insight into how and why an asset is moving toward functional failure. A degradation explanation, not another alarm stream.
Unplanned downtime costs the world's 500 largest companies an estimated $1.4 trillion a year — about 11% of revenue (Siemens, True Cost of Downtime 2024). SAHAS gives reliability teams earlier, explainable insight into functional decline, so more of it can be anticipated and planned rather than be caught by surprise.
Explainable
Physics- and information-theory-based methods with explicit reference behavior and functional requirements.
Bounded
Applicability depends on the approved asset, dataset, operating coverage, sensor and data quality, and observability.
Current functional capacity
How much of its required function the asset can still deliver, measured against reference behavior
Evidence-based confidence
Conclusions are stated with the confidence the physics and data support — rigorous, defensible, and never inflated.
Engineer-reviewed
Findings retain MEC engineering judgment and operator context.
Health margin
How much room remains before the functional-failure boundary under current conditions — a capacity margin, not a countdown clock.
Early degradation signals
Surfaces early signs of degradation from the operating data, within the limits of observability and data quality.
Founders
Mike Bryant, Phd, PE
CEO
After obtaining a BS in Bioengineering from the University of Illinois at Chicago in 1972, Mike achieved a Masters in Mechanical Engineering at Northwestern University in 1980 and a PhD in Engineering Science and Applied Mathematics from the same university in 1981.
Benito Fernández, Phd
CTO
Benito began his career in Venezuela, where he studied Chemical Engineering in 1979 and Materials Engineering in 1981. In 1981, he moved to the US to carry out graduate research at MIT, where he received his MS in 1985 and PhD in 1988, both in Mechanical Engineering.
Antonio Machado is a seasoned mechanical engineer with over 40 years of industrial experience specializing in Rotating Equipment and Reliability. He holds a Dipl.-Ing./M.S. (1990) in Mechanical Engineering with a focus on Turbomachinery and Internal Combustion Engines from the University of Karlsruhe, Germany, and a B.S. (1983) in Mechanical Engineering from the University of Zulia, Venezuela.
Our Publication
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