Exploring how machine learning enhances engineering workflows, providing tools to maximise industrial asset performance.
The Challenge
Engineering teams are often under pressure to analyse vast amounts of telemetry data, optimise complex systems, and make time-sensitive decisions that deliver on performance, cost, and sustainability objectives. Many tools fail to meet modern demands, forcing engineers to manually gather and format data, and tune numerous assumptions and rules. This can lead to inefficiencies, increased costs, and missed opportunities to prevent downtime and failures.
The Solution
Industrial AI combines physics, machine learning, and optimisation modelling to create advanced tools for detection, prediction, and optimisation. These machine learning powered systems provide recommended actions for specific engineering tasks, such as preventing equipment downtime or enhancing asset performance.
Engineering, Done Differently
Industrial AI systems that can process and analyse millions of telemetry data points in seconds, transforming them into actionable insights for engineers.
By learning the behaviour of specific assets from real-world data, these systems provide precise, context-aware recommendations. This enables engineers to provide strategic oversight and optimise intelligent, adaptive systems that are purpose-built to maximise performance.
Let’s highlight 7 scenarios of where this capability just makes sense.
1 Comprehensive Oversight of Equipment Health
Enable continuous and automated monitoring to preemptively identify and address potential issues.
How It Works: Machine learning systems can be configured to monitor specific operational parameters and recommend actions when specific events are predicted to impact reliability, performance, cost or emissions.
Benefits: Increased asset uptime, reduced unexpected failures, and quicker response to issues.
2 Optimise Simulation Efforts
Enhance simulation accuracy and efficiency in various engineering fields.
How It Works: Machine learning models collect outputs from simulation runs, to identify where there are opportunities to optimise simulation performance, providing insights into optimal configurations and predicting outcomes under various scenarios.
Benefits: More accurate simulations, faster iteration cycles, and improved strategic decision-making.
3 Proactive Maintenance and Failure Prevention
Predict maintenance needs and potential equipment failures, allowing for proactive maintenance or intervention scheduling.
How It Works: These systems analyse historical and real-time data to forecast when maintenance should be performed.
Benefits: Reduced downtime, lower maintenance costs, and extended equipment lifespan.
4 Dynamic Process Optimisation
Dynamically optimise operational parameters in real-time to maximise efficiency and output.
How It Works: The system continuously adjusts parameters based on real-time data to optimise performance.
Benefits: Improvements in operational efficiency, reduced operational costs, and increased production throughput.
5 Advanced, Smart Control Systems
Deliver advanced process control solutions that enhance adaptability and response in multivariable, dynamic environments.
How It Works: These systems learn from historical data and perform dynamic optimisation within predictive and intelligent control frameworks.
Benefits: Improved responsiveness, consistency and performance from control systems.
6 Auto-Adjusted Forecasting
Incrementally adjust forecasted performance based on real-world outcomes, delivering accurate and automatically updated forecasts.
How It Works: Machine learning is used to compare expected performance with actual outcomes, learning and adjusting based on specific knowledge-driven parameters to refine future forecasts.
Benefits: Improved preparedness for operational challenges and enhanced alignment with market dynamics.
7. Enhanced Strategic Oversight
Oversee the performance of multiple models and systems, guiding optimised use and configuration.
How It Works: Industrial AI analyses recommendations and outcomes from all deployed systems. It then provides refined configurations and strategic decision-making guidance.
Benefits: More informed decisions, better resource allocation, and enhanced long-term performance.
These are just 7 ways in which organisations are utilising machine learning to improve industrial performance - by bringing more intelligent technology into workflows, engineers continue to discover new ways for work to get done - as more data is collected, analysed and modelled than ever before.
If one or more of the above resonate with you and you’d like to have an honest conversation about whether Industrial AI could help improve efficiency and performance in your team, contact us today, and we’ll happily discuss if Industrial AI is right fit, and offer a demonstration of NEXGINEER.
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