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.
At its core, SAHAS features a condition-monitoring system that can capture and process data from a wide range of sensors, including vibration, temperature, flow, electrical, and pressure measurements. By combining data from multiple sources, SAHAS provides clear visibility into the condition of equipment such as pumps, compressors, turbines, motors, and other critical rotating machinery. The system uses artificial intelligence and physics-based models calibrated with real operating data, together with historical trend analysis, to identify very early indicators of equipment wear, component degradation, or performance decline—often long before they appear in traditional vibration or temperature trends. This enables maintenance teams to plan and respond before costly failures occur.
The platform includes a sophisticated diagnostics engine that combines rule-based logic with machine-learning models to pinpoint specific incipient failure modes. SAHAS projects future asset condition using predictive analytics and presents results through user-friendly dashboards. These dashboards include forecast curves and visual confidence bands that help maintenance teams and engineers understand risk levels and remaining useful life at a glance.
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, signal-based early warnings of equipment degradation. Using Shannon’s Communication Theory, this level treats the machine as a communication channel in which faults generate extra “noise.” By building healthy Transfer Function baselines and comparing them with operating-period signals, SAHAS calculates Machine Available Capacity (MAC) and issues clear health diagnostics and short-term prognosis.
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
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 Machine Available Capacity (MAC), and supports better decisions on when and how to intervene.
What this enables:
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Data-driven, proactive maintenance scheduling instead of calendar- or run-to-failure approaches.
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Greater confidence in machine health and remaining capacity, giving operators the certainty they need to push assets safely.
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Reduced unplanned downtime and higher asset availability, improving plant performance and bottom-line results
Operators gain more time to plan, optimize maintenance, and prevent costly downtime — staying one step ahead of failures.
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.
