Joint sparse representation based classification of rub-impact in rotating machinery with multiple acoustic emission sensors

Abstract

Acoustic emission (AE) technique has been widely used for the classification of rub-impact in rotating machinery due to its high sensitivity, wide frequency response range and dynamic detection property. However, it is still unsatisfied to effectively classify the rub-impact in rotating machinery under complicated environment using traditional classification method tailored to a single AE sensor. Recently, motivated by the theory of compressed sensing, a sparse representation based classification (SRC) method has been successfully used in many classification applications. Moreover, when dealing with multiple measurements the joint sparse representation based classification (JSRC) method could improve the classification accuracy with the aid of employing structural complementary information from each measurement. This paper investigates the use of multiple AE sensors for the classification of rub-impact in rotating machinery based on the JSRC method. First, the cepstral coefficients of each AE sensor are extracted as the features for the rub-impact classification. Then, the extracted cepstral features of all AE sensors are concatenated as the input matrix for the JSRC based classifier. Last, the backtracking simultaneous orthogonal matching pursuit (BSOMP) algorithm is proposed to solve the JSRC problem aiming to get the rub-impact classification results. The BSOMP has the advantages of not requiring the sparsity to be known as well as deleting unreliable atoms. Experiments are carried out on real-world data sets collected from in our laboratory. The results indicate that the JSRC method with multiple AE sensors has higher rub-impact classification accuracies when compared to the SRC method with a single AE sensor and the proposed BSOMP algorithm is more flexible and it performs better than the traditional SOMP algorithm for solving the JSRC method.