Summary
In this chapter, we saw how to implement an automatic fault diagnosis system in MATLAB. We started by introducing the essential concepts of anomaly detection and fault diagnosis. Then, we saw how to implement a system for identifying anomalous operations in MATLAB. We used vibrational data from a gearbox to train a model based on logistic regression. Subsequently, we used the same data, but this time using a model based on Random Forest to improve the performance of the predictive model.
In the next section, we implemented a model for identifying a fault in UAV propellers based on acoustic emission. We used a classification model based on an SVM.
In the final section, we introduced the most popular methods for regularizing algorithms to improve model performance.
In conclusion, this book serves as a comprehensive guide and invaluable resource for both beginners and seasoned practitioners navigating the dynamic landscape of machine learning. The book not only equips...
