Machine downtime can lead to significant production delays, disrupting workflow and potentially causing missed deadlines. Additionally, problems arising from machine maintenance can include unexpected stoppages, which disrupt production and lead to potential revenue loss.
DIGITAL TWIN SIMULATION SOLUTIONS
A Digital Twin is a virtual replica of a physical asset, enabling real-time monitoring, simulation, and optimization. GT-SUITE offers a comprehensive platform to develop digital twins, integrating robust multi-physics simulation with state-of-the-art data science. Powered by a cloud-based simulation environment that can seamlessly connect with the customer’s data collection system, GT’s solution helps enhance asset performance, reduce downtime and improve decision-making. Unlock the full potential of your systems by leveraging GT-SUITE to build your Digital Twin.
EXISTING CHALLENGES
As technology advances, conventional engineering encounters new challenges
Downtime and Maintenance
Not to mention the strain on resources and overall efficiency. This leads to increased operational costs and may also impact customer satisfaction and damage a company's reputation.
How much could you save by reducing downtime and planning for maintenance ?
Fault Detection and Prediction
Late fault detection on a malfunctioning machine can lead to extended interruptions, escalating repair costs, and potential damage to other system components. A fine balance needs to be found between detecting “true” faults or events leading to faults and avoiding “false alarms”.
False alarms can disrupt production schedules, resulting in missed deadlines, financial losses, and a decline in operational efficiency.
How much more efficient could your operations be with early fault detection and prediction?
Controls Optimization
Problems arising from controls being miscalibrated or not accounting for all possible contingencies can lead to unexpected stoppages, disrupting production and causing potential revenue loss. Control software developed in a laboratory setting may not always be robust under real-life conditions.
Therefore, the ability to correct course in real-time is crucial for enhancing safety margins and overall efficiency.If not managed properly, control software can cause well-designed hardware to experience a series of adverse conditions, negatively impacting the equipment's lifespan.
How do you optimize your control software?
What/if Scenarios
Testing all possible scenarios is expensive, often impractical, and dependent on human intervention and sensor data, which can yield inconsistent results. This inconsistency can compromise the accuracy and reliability of the tests. As a result, physical testing of all possible operating conditions struggles to keep up with the complexity and scale of modern engineering projects, making it challenging to ensure comprehensive coverage and thorough validation.
with the complexity and scale of modern engineering projects, making it challenging to ensure comprehensive coverage and thorough validation.
Are you able to cover all corner cases by means of physical testing?
DIGITAL TWIN WITH GT-SUITE
A digital twin works by creating a virtual replica of a physical asset, system, or process that is continuously updated with real-time data from the physical world. This virtual model allows for the simulation, analysis, optimization and real-time monitoring of the physical counterpart. Four major steps for creating successful digital twins are:
- Data Collection: The physical asset (e.g., a machine, engine, compressors, etc.) is equipped with sensors and IoT devices that collect various types of data, such as temperature, pressure, vibration, speed, and more
- Data Integration: The collected data is aggregated, cleaned, and processed. This step ensures that the data is accurate and can be used effectively by the virtual model.
- Virtual Model Creation: The virtual model is created using GT-SUITE’s physics-based modeling capabilities and is calibrated with data from the physical asset. Alternatively, it can be developed using machine learning techniques applied to the collected data creating fast running metamodels.
- Real-Time Interaction: The virtual model is constantly updated via data streams to reflect the current state of the physical asset. Operators can monitor the virtual model in real-time to gain insights into the current performance and condition of the physical asset.
- Physics-Based: The virtual model is built using the physics-based modeling capability of GT-SUITE and is calibrated with on-site measured data to represent the current state of the asset.
- Data-Driven Using Machine Learning: GT-SUITE’s built-in Machine Learning Assistant leverages the vast amount of data generated from testing, field operations, or design of experiments to create fast-running mathematical models (metamodels) of the physics-based models. A well-calibrated GT-SUITE model can further generate extensive data for additional simulations, significantly reducing the cost and time associated with physical testing.
- Hybrid: The most effective digital twin combines physics-based models with real-time data, enhanced by machine learning, to enable faster results and smarter decision-making.
- Diagnostics: Enhances system reliability by identifying deviations from normal operations and diagnosing root causes.
- Prognostics: Forecasts future failures, thus preventing unexpected breakdowns and reducing downtime.
- Asset Performance Management: Optimizes performance by tracking real-time data and simulating scenarios, leading to better decision-making and cost savings.
- Improve Future Designs: Refines designs by simulating performance and integrating real-time data, enhancing product quality and reducing development time.
- Manage Inventory: Forecasts component failures and manages inventory proactively, minimizing downtime by ensuring parts availability before failures occur.
- Operational Planning: Uses virtual models to simulate system performance, aiding in proactive management and improving operational efficiency.
- Virtual Verification and Validation: Simulates real-world conditions to reduce the need for physical testing, cutting costs, and accelerating development.
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