Cross-working-condition fault diagnosis method based on open set joint transfer learning

• Main Authors: DING YU, XU SHU, SHANG PENGCHAO, MA JIAN, WANG CHAO, LYU CHEN
• Format: Patent
• Language: Chinese; English
• Published: 11.05.2021
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; HANDLING RECORD CARRIERS; PHYSICS; PRESENTATION OF DATA; RECOGNITION OF DATA; RECORD CARRIERS

The invention discloses a cross-working-condition fault diagnosis method based on open set joint transfer learning. The method comprises the steps: training a feature extraction model and a feature classification model by extracting and recognizing the fault types of cross-working-condition source domain sample data and target domain sample data; building a fault diagnosis model by using the trained feature extraction model and feature classification model; and inputting target domain data needing fault diagnosis into the fault diagnosis model, and diagnosing a fault type corresponding to the target domain data. 本发明公开了一种基于开放集联合迁移学习的跨工况故障诊断方法，包括：通过提取并识别跨工况的源域样本数据和目标域样本数据的故障类型，训练特征提取模型和特征分类模型；用训练好的特征提取模型和特征分类模型搭建故障诊断模型；将需要进行故障诊断的目标域数据输入到所述故障诊断模型，对目标域数据对应的故障类型进行诊断。