Services
SAHAS: Engineer-Reviewed Functional-Health Assessment
SAHAS (System Asset Health Assurance Service) is a physics- and information-theory-based engineering approach to machine functional health. It assesses available functional capacity from prepared operating data relative to reference behavior and defined functional requirements.
Current delivery retains MEC engineering judgment and operator context. Applicability depends on the approved asset, dataset, relevant variables, operating coverage, sensor and data quality, reference behavior, observability, and the defined task.
SAHAS is not presented here as live or continuous monitoring, automated ingestion or analysis, automatic alerts or reporting, automated recommendations, autonomous control, a dashboard, a native integration, or a guarantee.
SAHAS S1 — Functional Health Assessment
SAHAS S1 provides quantitative, signal-based early assessment of machine functional health. Using Shannon's Communication Theory, it treats the machine as a communication channel in which faults generate extra "noise." By building healthy reference baselines and comparing them with operating-period signals, S1 interprets SAHAS Machine Available Capacity (SMAC) against reference behavior and defined functional requirements. Technical maturity: Deployed.
How it works:
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Uses prepared operating data and a defensible healthy or reference description.
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Evaluates available functional capacity against defined functional requirements.
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Uses SMAC — SAHAS Machine Available Capacity — for S1-centered interpretation.
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Produces MEC engineer-reviewed findings for the approved scope.
Why it’s different:
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No extra sensors or opaque machine learning required
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Uses the signals you already have – motor current, vibration, temperature, flow, and pressure from existing instrumentation.
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Combines signal-based diagnostics with data-calibrated, physics-based models – not a black box, but grounded in how your machines actually work.
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Focuses on degradation and remaining capacity – detects early changes, quantifies SAHAS Machine Available Capacity (SMAC), and supports better decisions on when and how to intervene.
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What it supports
SAHAS supports engineering discussion of machine functional health and may inform maintenance or operating decisions within an approved scope. It does not guarantee RUL, fault identification, accuracy, availability, downtime reduction, cost savings, or any other operating result.
SAHAS connects to the existing current measurement of the motor and uses that signal while the equipment operates in its normal modes. No additional sensors or special test runs are required, making deployment fast and non-intrusive.
