Domain Knowledge-Based Deep-Broad Learning Framework for Fault Diagnosis

Abstract

Intelligent fault diagnosis is a vital role in smart manufacturing. And deep-learning-based fault diagnosis has become a hot topic due to its strong feature extraction ability. However, traditional deep-learning-based methods show two limitations. One is that a large number of labeled samples are required to construct effective diagnosis models. Another is that these methods lack flexibility, especially for homologous multitasking problems. In this article, a novel domain-knowledge-based deep-broad learning framework (DK-DBLF) is proposed to overcome abovementioned limitations. A DK-DBLF consists of two parts: a task-specific feature extractor and a flexible fault recognizer. The first part is constructed by several convolutional neural networks to obtain abstract features automatically, and the second part employs a broad learning system to improve the flexibility of the proposed framework. To combine these two parts more effectively, bridge label-based strategy is designed, which is a key connection that can integrate domain knowledge into the learning process. The performance of a DK-DBLF is tested on motor-bearing and pipeline defect datasets, which are health condition classification and homologous multitask estimation problems, respectively. The results have demonstrated that our framework can significantly reduce the usage of labeled samples in the learning process, and architecture adjustment can be easily performed when compared with traditional deep methods.