MATLAB and Simulink for Predictive Maintenance
Develop and Deploy custom predictive maintenance algorithms
“Despite having little previous experience with AI, within a limited budget and a tight deadline, we completed a diagnostics model in MATLAB capable of detecting wind turbine component failure with over 90% accuracy.”
– Jung Chul Choi, Korea Institute of Energy Research
Empower Your Engineers
MATLAB® is the easiest most productive environment for engineers to develop predictive maintenance algorithms and deploy them in operation
Design Predictive AlgorithmsDetect anomalies, identify faults, and estimate remaining useful life with domain-specific features and low-code AI |
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 Model Components and SystemsReuse models from design, generate synthetic sensor data, build and integrate digital twins |
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 Deploy AnywhereIntegrate with IT/OT systems in the cloud, or generate C/C++ code for real-time processing |
Using MATLAB and Simulink for Predictive Maintenance
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Predictive Maintenance with MATLAB
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Design Predictive Algorithms
Creating a reliable predictive algorithm is more than just AI: access, clean, and explore your data, then use your engineering expertise to extract the best features for training predictive algorithms. Get started quickly with application-specific functions and reference examples.
- Access streaming and archived data using built-in interfaces to cloud storage, databases, data historians, and industrial protocols
- Clean and explore data using interactive statistical and signal processing techniques
- Extract and rank time-domain, frequency-domain, and application-specific features with the Diagnostic Feature Designer
- Identify faults and predict time-to-failure using low-code AI, statistical, and model-based methods
Model Components and Systems
With physics-based models built in Simulink and Simscape, you can generate synthetic fault and degradation data, identify the best sensors, and simulate future performance.
- Create or repurpose Simulink and Simscape models of components and systems
- Tune model parameters to match real equipment performance
- Generate synthetic fault and degradation data for training predictive algorithms
- Deploy models as digital twins
Deploy Anywhere
Shorten response times, transmit less data, and make results immediately available to operators by implementing your MATLAB algorithms on embedded devices and in enterprise IT/OT systems.
- Eliminate hand-coding by generating C/C++ code from MATLAB for real-time processing directly on assets and edge devices
- Scale your MATLAB algorithms by integrating with a variety of cloud platforms—without recoding or creating custom infrastructure
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