Fault Diagnosis of Rotating Machinery: A Highly Efficient and Lightweight Framework Based on a Temporal Convolutional Network and Broad Learning System
Hao Wei,
Qinghua Zhang,
Yu Gu
Abstract:Efficient fault diagnosis of rotating machinery is essential for the safe operation of equipment in the manufacturing industry. In this study, a robust and lightweight framework consisting of two lightweight temporal convolutional network (LTCN) backbones and a broad learning system with incremental learning (IBLS) classifier called LTCN-IBLS is proposed for the fault diagnosis of rotating machinery. The two LTCN backbones extract the fault’s time–frequency and temporal features with strict time constraints. T… Show more
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