Services
SAHAS: Intelligent Diagnostics and Predictive Maintenance Platform
SAHAS (Smart Analytics for Health and Asset Systems) is an advanced diagnostics and predictive maintenance platform built to minimize unplanned downtime and increase the operational life of essential industrial assets. It is specifically designed for demanding sectors such as oil and gas, refining, power generation, and heavy manufacturing. SAHAS integrates smoothly into existing control environments and delivers measurable operational improvements along with a strong return on investment.
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At its core, SAHAS features a real-time condition monitoring system that captures and processes data from a wide range of sensors. These include vibration, temperature, flow, electrical, and pressure sensors. By combining data from multiple sources, SAHAS provides full visibility into the condition of equipment such as pumps, compressors, turbines, motors, and pipelines. The system uses artificial intelligence, physics-informed models, and historical trend analysis to identify early indicators of equipment wear or performance decline. This enables maintenance teams to respond before costly failures occur.
The platform includes a sophisticated diagnostics engine that applies both rule-based logic and machine learning to pinpoint specific failure modes. SAHAS projects future asset condition using predictive analytics and displays the information through user-friendly dashboards. These dashboards include forecast curves and visual confidence bands to help maintenance teams and engineers understand risk levels and remaining useful life.
Beyond diagnostics, SAHAS supports dynamic maintenance planning by recommending service schedules based on real-time asset health rather than fixed intervals. This helps reduce unnecessary maintenance activity and increases the time between required interventions. Customizable alerts and automated reporting features ensure that operational teams receive the information they need to take timely action.
SAHAS is built for fast integration and compatibility with industrial systems. It connects easily with supervisory control systems, programmable logic controllers, distributed control platforms, maintenance software, and data historian tools. Whether applied in upstream oil fields, pipeline transportation networks, or refinery facilities, SAHAS delivers scalable, actionable insights that improve reliability and reduce operational risk.
SAHAS Level 1
SAHAS Level 1 provides quantitative, physics-based early warnings of equipment degradation. Using Shannon’s Communication Theory, it treats the machine as a channel where faults generate extra “noise.” By comparing live signals to healthy baselines, SAHAS calculates the Machine Available Capacity (MAC) and issues clear health diagnostics.
How it works:
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Uses existing signals already monitored in plants
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Generates Transfer Functions between Input & Output Signals
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Compares Baseline & ongoing monitored signals
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Extracts and analyzes “noise” caused by faults
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Generates reports on machine health and areas of concern
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Why it’s different:
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Detects degradation much earlier than traditional monitoring
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No extra sensors or opaque machine learning required
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Utilizes Multiple Transfer Functions between Inputs and Outputs
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Explainable diagnostics, grounded in communication theory and physics
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Lower cost and faster deployment than conventional predictive tools
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What this enables:
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Maintenance can be scheduled proactively, not reactively
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Operators gain confidence in machine health and remaining capacity
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Companies reduce downtime and improve asset availability
Operators gain more time to plan, optimize maintenance, and prevent costly downtime — staying one step ahead of failures.
Pump with Typical Instrumentation Points
SAHAS Level 1 works with signals already measured on standard rotating equipment. The pump illustration shows typical data sources used for analysis.
