Engineers are using AI to design smarter ways to detect anomalies, identify faults, and estimate the remaining useful life of machines. In this hands-on workshop, you will write and execute code examples in MATLAB® Online™ – entirely in the browser – to learn and explore how to apply principles of AI to predictive maintenance and anomaly detection: machine learning, deep learning, feature extraction, and domain-specific data processing.
This interactive hands-on session will include the following:
- Familiarize yourself with MATLAB Online and AI tools with an introductory example that trains a machine learning model to classify faults.
- Interactively extract, analyze, and rank features from machine sensor data, then train and compare multiple AI models based on these features.
- Diving deep into an advanced, predictive maintenance workflow that covers anomaly detection and remaining useful life estimation.
MathWorks instructors and teaching assistants will be available throughout the session to guide you. If the event is being held onsite, please bring your laptop.
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Verma, Nishchal K., Rahul Kumar Sevakula, Sonal Dixit, and Al Salour. “Intelligent Condition Based Monitoring Using Acoustic Signals for Air Compressors.” IEEE Transactions on Reliability 65, no. 1 (March 2016): 291–309. https://doi.org/10.1109/TR.2015.2459684.
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Saxena, Abhinav, Kai Goebel, Don Simon, and Neil Eklund. "Damage propagation modeling for aircraft engine run-to-failure simulation." In Prognostics and Health Management, 2008. PHM 2008. International Conference on, pp. 1-9. IEEE, 2008.
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Saxena, Abhinav, Kai Goebel. "Turbofan Engine Degradation Simulation Data Set." NASA Ames Prognostics Data Repository https://phm-datasets.s3.amazonaws.com/NASA/6.+Turbofan+Engine+Degradation+Simulation+Data+Set.zip, NASA Ames Research Center, Moffett Field, CA
- MATLAB
- Predictive Maintenance Toolbox™
- Signal Processing Toolbox™
- Statistics and Machine Learning Toolbox™
- System Identification Toolbox™
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