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
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.