MATLAB code for dimensionality reduction, feature extraction, fault detection, and fault diagnosis using Kernel Principal Component Analysis (KPCA).

This project is used to estimate, isolate and diagnose faults for a quadcopter and a PVTOL and also use a methods to control the system by tolerating the fault. Both quadcopter and PVTOL systems have nonlinear dynamics. The ways for fault estimation in this project consist of nonlinear AO and linear PIO for the PVTOL and qLPV PIO for the quadcop…

To find out when was the time that the fault occurs and make predictions to find out early faults,you can use a LSTM network to classify each time step of sequence data

Using Bayesian optimization to optimaze the network of CNN,which is used in fault diagnosis

Fault diagnosis using CNN

An ensemble bagged trees classification approach for monitoring of the engine conditions and fault diagnosis using Visual Dot Patterns of acoustic and vibration Signals

Implementation in MATLAB of the PLS algorithm

Fault prognosis using LSTM and CNN

SA im SoSe 2022

This project introduces a fault detection framework using neural networks for the RCAM. The study begins by remodeling the RCAM’s nonlinear dynamics to simulate various fault conditions. Subsequently, data generated from these faulty models underpin the training and testing of neural networks.