A PCA-mRVM fault diagnosis strategy and its application in CHMLIS

The multi-level inverter system is becoming a very promising candidate to replace the conventional two-level inverter, but system reliability remains an open issue. The most common reliability problem is that power switch transistors have open-circuit or short-circuit faults during operation. In order to improve the accuracy of the fault diagnosis and accelerate the operation speed in a cascaded H-bridge multilevel inverter system, a novel fault diagnosis strategy based on principle component analysis and multiclass relevance vector machine (PCA-mRVM) is presented in this paper. In this strategy, the output voltage of CHMLIS, which is preprocessed through the fast Fourier transform, is used to identify the type and location of occurring fault through the mRVM model. Then PCA is utilized to reduce input sample's dimension, which decrease the training time of diagnostic model. The PCA-mRVM strategy could not only achieve higher model sparsity and shorter diagnosis time, but also provide probabilistic outputs for every class membership. Hardware experimental results of simple-fault have shown that the PCA-mRVM could achieve the best diagnosis performance than traditional fault diagnosis methods. In addition, simulation results of complicated-fault have further validated that the PCA-mRVM strategy is useful in multi-fault diagnosis, which is better than other methods.