A complementary approach for fault diagnosis of rolling bearing using canonical variate analysis based short-time energy feature

Signal decomposition is a meaningful and effective methodology which is widely used for fault diagnosis. Mode/feature selection is an inevitable topic for fault diagnosis of rolling bearing due to over-decomposition. In practical application, the selection of sensitive modes is a challenging task, so many valuable works have been performed to cope with it. However, the published works lack an effective approach to acquire few meaningful modes by avoiding the complicated mode selection procedures, prior to feature extraction. Moreover, selection of the modes of interest fails to take the residual part into account, which makes the diagnosis result sensitive to the number of modes/features retained. This paper proposes a complementary approach to extract fault features and avoid the selection of single mode of interest, which employs canonical variate analysis to convert the original variable into two complementary spaces; canonical variate space; and residual space. Then the complementary statistical indicators Hotelling T² statistic and Q statistic are used to provide important information about the conditions of the rolling bearing. Subsequently, a new feature index, complementary short-time energy extracted from the two statistics are used as fault features which are given as an input to a classifier such as a support vector machine. Two data sets collected from different test rigs are used for demonstration of the proposed work. The experimental result shows that the troublesome feature/mode selection issue is avoided, and the diagnosis result is not sensitive to the number of canonical variate retained. Besides, the proposed approach can identify various working conditions of rolling bearing accurately, which is simple and effective for fault diagnosis of rolling bearing, compared with the existing methods.