Technology
The science behind SAHAS
SAHAS (System Asset Health Assurance Service) uses physics and information theory to assess a machine’s ability to perform its required function.
Prepared operating data is compared with reference behavior to interpret changes in available capacity. MEC engineers review the findings alongside operating conditions to support reliability and maintenance decisions.
Each assessment starts with the asset, the required function, and the quality and coverage of the available data.
Reading functional health
SAHAS S1 assesses available functional capacity from prepared operating signals. SMAC — SAHAS Machine Available Capacity — is interpreted against reference behavior and the asset’s defined functional requirements.
Available capacity
SMAC expresses the machine’s remaining ability to perform its required task relative to healthy reference behavior. It connects measured input-output relationships to functional capability, rather than treating each sensor reading in isolation.
Functional failure
A machine can still run after it loses the ability to meet its required duty. A pump, for example, may rotate while no longer delivering the specified flow and pressure. The assessment therefore starts with an explicit functional boundary.
Health Margin and RUL
Health Margin is the excess functional capability remaining before that boundary. Current RUL estimation relates the remaining margin to its estimated rate of change under the operating assumptions. Changes in duty or degradation rate change the estimate.
Physics and information
Degradation changes how a machine converts inputs into useful output. The Degradation–Entropy Generation (DEG) framework connects irreversible physical processes—such as friction and wear—to degradation. Information theory provides a complementary way to assess changes in machine capacity.
Existing measurements are the starting point. Depending on the asset, these may include voltage, current, speed, pressure, temperature and flow. Sampling, synchronization and operating coverage determine which input-output relationships can be assessed.
A departure from the healthy reference is a finding to investigate. Observability asks whether the available measurements contain enough information to reveal the relevant degradation. Operating records help distinguish equipment changes from a new duty, control action or sensor issue.
What it supports
Capacity trends and RUL inform inspection priorities, maintenance timing and preparation of people and parts. MEC reviews the findings with the equipment team and relates them to the asset’s duty, criticality and existing reliability practices.
SAHAS development roadmap
S1 — Functional Health Assessment
Current capability: S0 prepares the data; S1 assesses functional capacity, observable changes and current RUL against reference behavior and required duty, with MEC engineering review.
S3 — Fault Compensation
S2 — Component Diagnostics
Roadmap: use physics-based digital-twin tuning to investigate degraded components. Future scope; availability and timing are not established.
Roadmap: explore operating strategies through what-if analysis with an S2-tuned digital twin. Future scope; availability and timing are not established.
S4 — Condition and Fault Forecasting
Roadmap: investigate DEG-based methods for degradation forecasting and maintenance planning. Future scope; availability, timing and predictive performance are not established.