Process engineers use MATLAB® and Simulink® to analyze real-time sensor data, implement control strategies, and create predictive maintenance systems based on big data and machine learning.
MATLAB and Simulink help mining engineers:
- Develop predictive maintenance systems by applying numerical techniques on high-speed sensor data
- Use machine learning with historical data to troubleshoot process problems
- Use data modeling to improve process performance
- Adopt digitization without depending on data scientists or IT personnel
"MATLAB gave us the ability to convert previously unreadable data into a usable format; automate filtering, spectral analysis, and transform steps for multiple trucks and regions; and ultimately, apply machine learning techniques in real time to predict the ideal time to perform maintenance."Gulshan Singh, Baker Hughes
Learn More About Predictive Maintenance
Simulating Failure Data
Traditionally, engineers optimize mining plants and processes based on data collected from sensors.
However, sensor data is not always available for the multiple possible failure modes in a machine. Instead, you can use simulation data to represent failures by creating a model of your machine and simulating faulty operating conditions.
Simulink and Simscape™ enable you to build a model of your machine that can describe its behavior in terms of its physical components and dynamics. You can represent different failure modes of the machine by modifying parameter values, injecting faults, and changing model dynamics.
Optimize Assets with Predictive Maintenance and Signal Processing
MATLAB can help you develop predictive maintenance algorithms customized to the specific operational and architectural profile of your equipment. Use Predictive Maintenance Toolbox™ to design condition indicators and estimate the remaining useful life of your rotary equipment.
You can use Signal Processing Toolbox™ to automate the monitoring of performance of your control loops, remotely determine the extent of corrosion or pitting in your pipelines, and detect the location and quantity of pipeline leaks.
Read how Baker Hughes used MATLAB to implement a predictive maintenance platform for gas and oil extraction equipment and reduced overall costs by 30-40%.
Machine Learning, Deep Learning, and Big Data
Interactive apps in Statistics and Machine Learning Toolbox™ let you apply machine learning techniques without having to be an expert in data science. MATLAB also provides a single, high-performance environment for working with big data and developing deep learning models. This enables you to perform fault detection and diagnosis faster and better monitor your processes.
Read how Ruukki engineers reduced their analysis times from several days to less than a minute by integrating various databases and using machine learning for process optimization.
Process Improvement with Data Modeling
Use multivariate analysis tools in MATLAB to determine the independent driving variables affecting process performance. System Identification Toolbox™ lets you create and use models of dynamic systems that are not easily modeled from first principles or specifications. The toolbox also lets you interactively perform online parameter and state estimation.
Watch how Shell used MATLAB (3:35) to develop models and perform real-time optimization on a batch process.
Develop and Implement Process Control Strategies
You can use MATLAB controls products to design controls schemes and perform dynamic simulations for better analysis of plant behavior. Validate your design with hardware-in-the-loop testing and rapid prototyping.
Read how Tata Steel saved 40% energy on their industrial cooling towers by optimizing the control strategy via a digital twin.
MathWorks can help you adopt and implement big data strategies specific to the needs of your organization. You can use prebuilt MATLAB toolboxes and reference architectures to simplify a wide range of applications: from integrating with enterprise IT systems, the cloud, and production data infrastructure to scaling your computation to clusters or deploying your models as applications to share with non-MATLAB users. See how you can achieve this on the cloud.
Learn more about how you can also connect directly with OSIsoft PI systems.
Watch how Shell embraced digitization (29:14) using MATLAB Production Server™. Shell engineers automated their processes for integrating data from different sources, building models, and deploying their analytics onto cloud and enterprise systems.
Simplify Planning and Scheduling Activities
Improve efficiencies in production and scheduling through discrete event simulation. With SimEvents™, you can study the effects of task timing and resource usage in a batch production process. Using MATLAB and Simulink products, you can also conduct operational research for decisions related to forecasting, capacity planning, and supply-chain management.
Read how SK Innovation developed an optimal crude selection strategy at their refinery using optimizers in MATLAB.
“MATLAB enabled us, as geologists, to use our expertise in predictive frameworks, analytics, and analog matching to implement algorithms that are unique in our industry. With the help of MathWorks consultants, we then deployed those algorithms as an easy-to-use application to our colleagues worldwide."Nick Howes, Shell
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