LLE for submersible plunger pump fault diagnosis via joint wavelet and SVD approach

Locally linear embedding (LLE) is a classical non-linear dimension reduction algorithm. LLE can deal with a nonlinear dataset and extract the significant feature from the dataset. However, LLE may fail, when signal is non-stationary or contains strong noise. In this paper, wavelet transformation, singular value decomposition (SVD), and LLE are assembled to extract the feature of a dataset based on which a new fault diagnosis method is developed for submersible plunger pump fault recognition. First, each sample is preprocessed by wavelet transformation and SVD, upon which a new feature space is constructed; then, LLE is utilized to reduce the dimensions of the feature space; finally, support vector machines (SVM) is invoked to recognize the device status. The experiments performed on submersible plunger pump dataset and bearing dataset demonstrate that the proposed method is effective and can achieve a high diagnosis accuracy.