[en] In the field of structural health monitoring or machine condition monitoring, the activation of nonlinear dynamic behavior complicates the procedure of damage or fault detection. Principal Component Analysis (PCA) is known as an efficient method for damage diagnosis. However, two drawbacks of PCA are the assumption of the linearity of the system and the need of many sensors. This article presents industrial applications of two possible extensions of PCA: Null subspace analysis (NSA) and Kernel PCA (KPCA). The advantages of NSA rely on its rapidity of use and its reliability. The KPCA method, through the use of nonlinear kernel functions, allows to introduce nonlinear dependences between variables. The objective is to address the problem of fault detection in mechanical systems using subspace-based methods. The detection is achieved by comparing the subspace features between the reference and a current state through statistics. Industrial data are used as illustration of the methods.