Turbomechanica®
Physics-based, AI-powered prescriptive diagnostics for industrial machines
Turbomechanica® is Mechademy’s Diagnostics OS for critical plant assets and processes. It unifies physics-based models, machine learning, operational data, and AI-powered Turbo Agents to continuously monitor equipment, detect developing failures, diagnose root causes, and recommend the actions needed to prevent costly unplanned downtime.
Behind the platform is Mechademy’s Remote Diagnostics Lab, where Turbo Agents and expert engineers work together to evaluate signals, investigate multivariate anomalies, validate diagnoses, and coordinate insights with plant teams. This combination of intelligent software and human expertise provides operators with a continuous layer of diagnostic support—helping them understand what is happening, why it is happening, and what actions to take next.
Turbomechanica is designed to scale across critical plant assets and systems—from turbines, centrifugal pumps, and heat exchangers to balance-of-plant equipment such as valves, instrumentation, and sensors. The result is a unified approach to plant health, reliability, and maintenance that gives teams greater visibility across operations and enables faster, more informed decisions.
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Proprietary physics-based performance models
These models compare the performance of equipment to the baseline performance established during testing or field operation. By continuously monitoring and comparing the actual performance to the expected performance, the platform provides early detection of impending faults with a high degree of accuracy.
Extensive asset library
OEM agnostic performance models
Drag and drop functionality allows fast, customizable equipment setup
Real gas equations of state for performance accuracy
State-of-the-art diagnostics engine provides prescriptive insights
Domain informed machine learning & deep learning
The platform utilizes deep learning and machine learning models that are trained on historical plant data or simulated data. These models analyze vast amounts of data to identify patterns, correlations, and potential failure modes.
Domain-informed use cases and feature engineering
Model building at scale using AutoEDA and AutoML
Model drift and data drift detection
Hybrid models using simulation-based transfer learning
Integrations with popular DL (PyTorch, TensorFlow, and Keras) and ML (scikit-learn, XGBoost, PySpark and more) frameworks
End to end lifecycle management of models (MLOps)
Sophisticated orchestration to enable state-of-the-art diagnostics
The Turbomechanica platform uses a unique orchestration strategy that allows the seamless flow of data between physics and machine learning models. This allows the use of physics/ML generated synthetic sensors within models.
Significantly expanded scope of early fault detection
Richer insights into fault causality
Hybrid physics + ML digital twins
Use of physics generated dimensionless parameters allows better transferability of ML models
Predict machine failure, receive actionable insights and prescriptive alerts to minimize downtime
Info
Alert description and prescriptive insights
Plots
Automatically generated using diagnostic sensor groups
Timeline
Timeline of similar historical events
Service Requests
CMMS integration with service requests and work orders
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Discover the Power of Turbomechanica®
Our platform utilizes data from multiple (tens or even hundreds of) sources to identify anomalies and flag sub-optimal operations based on the underlying relationships among variables.
15X ROI
Identify potential issues months in advance, reducing downtime, maintenance, and operational costs
10-20% Higher Efficiency
Increase equipment uptime, energy efficiency, and asset lifetime
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Book a Demo
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