Random Matrix-Based Approach for Data Reduction and Sensor Selection with Application to Degradation Index Construction

This paper relies on the random matrix theory to reduce data dimension and to identify useful data sources in the unsupervised context. A so-called random matrix based principal component analysis algorithm is thus developed and then applied to the well-known 2008 PHM dataset to build efficient but less costly degradation indices. A comparison of the degradation indices obtained with and without sensors selection confirms the performances of our proposed approach.