“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.”
MATLAB is the easiest and most productive environment for engineers to develop predictive maintenance algorithms and deploy them in operation.
Detect anomalies, identify faults, and estimate remaining useful life with domain-specific features and low-code AI
Reuse models from design, generate synthetic sensor data, build and integrate digital twins
Integrate with IT/OT systems in the cloud, or generate C/C++ code for real-time processing
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.
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.
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.
