Intelligent Impulse Finder: A boosting multi-kernel learning network using raw data for mechanical fault identification in big data era

Abstract

Nowadays, most intelligent diagnosis methods focus on fault classification and the discriminative knowledge is unknown due to the ‘black box’ characteristic. However, impulse responses in vibration signals, which is important sign to determine whether mechanical equipment is faulty, are rarely studied under intelligent methods since their recognition is both difficult and time-consuming, especially mixed with noise. Aiming at these problems, a novel impulse recognition method was proposed to capture them from raw mechanical data. Firstly, a single-kernel convolutional neural network is proposed as weak classifier to learn discriminative information from raw data. Then, a coarse-to-fine search is proposed to locate position of impulse response. Finally, the boosting algorithm is used to ensemble several proposed weak classifiers for final output. Vibration signals of bearings with two different faults are utilized to validate the proposed model. The results prove that the proposed approach obtain higher accuracy compared with traditional Laplace wavelet method. Moreover, the extracted kernel functions reveal new knowledge about characteristics of impulse responses, which significantly differs from traditional hypothesis and sheds a light on improvement of relevant approaches.