Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis

Gearboxes are crucial transmission components in mechanical systems. Fault diagnosis is an important tool to maintain gearboxes in healthy conditions. It is challenging to recognize fault existences and, if any, failure patterns in such transmission elements due to their complicated configurations. This paper addresses a multimodal deep support vector classification (MDSVC) approach, which employs separation–fusion based deep learning in order to perform fault diagnosis tasks for gearboxes. Considering that different modalities can be made to describe same object, multimodal homologous features of the gearbox vibration measurements are first separated in time, frequency and wavelet modalities, respectively. A Gaussian-Bernoulli deep Boltzmann machine (GDBM) without final output is subsequently suggested to learn pattern representations for features in each modality. A support vector classifier is finally applied to fuse GDBMs in different modalities towards the construction of the MDSVC model. With the present model, “deep” representations from “wide” modalities improve fault diagnosis capabilities. Fault diagnosis experiments were carried out to evaluate the proposed method on both spur and helical gearboxes. The proposed model achieves the best fault classification rate in experiments when compared to representative deep and shallow learning methods. Results indicate that the proposed separation–fusion based deep learning strategy is effective for the gearbox fault diagnosis.