China Shipbuilding Industry Corporation

About: China Shipbuilding Industry Corporation is a company organization based out in Beijing, China. It is known for research contribution in the topics: Signal & Vibration. The organization has 812 authors who have published 685 publications receiving 4011 citations. The organization is also known as: CSIC.

Design and Analysis of a Linear Permanent- Magnet Vernier Machine With Improved Force Density

Abstract: This paper proposes a novel linear permanent-magnet (PM) vernier machine, which offers high force density, high efficiency, simple structure, and low cost. The novelty of the proposed machine is substantiated by integrating appropriate magnetization directions of PMs in the armature core. First, the structure and the operation principle of the proposed machine are descried. The machine is subsequently designed for a given set of specifications and its electromagnetic performances are analyzed by time-stepped transient finite-element method. An analytical equation is derived for evaluation of the thrust force ripple. Finally, experiments on a prototype of the proposed machine are carried out for validation.

Feature extraction and recognition for rolling element bearing fault utilizing short-time Fourier transform and non-negative matrix factorization

Abstract: Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, the time-frequency analysis is often applied to describe the local information of these unstable signals smartly. However, it is difficult to classify the high dimensional feature matrix directly because of too large dimensions for many classifiers. This paper combines the concepts of time-frequency distribution(TFD) with non-negative matrix factorization(NMF), and proposes a novel TFD matrix factorization method to enhance representation and identification of bearing fault. Throughout this method, the TFD of a vibration signal is firstly accomplished to describe the localized faults with short-time Fourier transform(STFT). Then, the supervised NMF mapping is adopted to extract the fault features from TFD. Meanwhile, the fault samples can be clustered and recognized automatically by using the clustering property of NMF. The proposed method takes advantages of the NMF in the parts-based representation and the adaptive clustering. The localized fault features of interest can be extracted as well. To evaluate the performance of the proposed method, the 9 kinds of the bearing fault on a test bench is performed. The proposed method can effectively identify the fault severity and different fault types. Moreover, in comparison with the artificial neural network(ANN), NMF yields 99.3% mean accuracy which is much superior to ANN. This research presents a simple and practical resolution for the fault diagnosis problem of rolling element bearing in high dimensional feature space.