Fault diagnosis for industrial robots based on a combined approach of manifold learning, treelet transform and Naive Bayes.

This research introduces a novel fault diagnosis method for an industrial robot based on manifold learning algorithms, Treelet Transform (TT) and Naive Bayes. The vibration signals of an industrial robot working under three working conditions are acquired as the raw data. Three typical manifold learning algorithms, Principal Component Analysis (PCA), Locality Preserving Projections (LPPs), and Isometric Feature Mapping (ISOMAP), are utilized to extract three-dimensional features from the vibration signals. Then, these features were combined into nine-dimensional features and, these nine-dimensional features were reduced to three-dimensional feature vectors by TT. Finally, a Naive Bayes model is trained with these three-dimensional feature vectors. Experimental results show that compared with the three methods, PCA, LPP, and ISOMAP, the accuracy of the proposed combined method is higher than the single method. The fault diagnosis method presented in this paper is easy to implement and can effectively identify the fault types.