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基于Transformer的分段供电故障诊断方法

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分段供电系统是长初级直线电机的重要组成部分,对其应用有效的故障诊断方法有助于电机的正常工作和故障检修。根据实测三相电流波形特点,提出一种结合幅值特征序列提取算法和Transformer深度学习模型的分段供电故障诊断方法。除此之外,引入深度学习模型的解释性方法,实现模型对异常进行自监督辅助定位。最后,将以上方法在某型分段供电直线电机的试验数据上进行应用,并验证这些方法的有效性和可靠性。
FAULT DIAGNOSIS METHOD FOR SEGMENT-POWERED SUPPLY SYSTEM BASED ON TRANSFORMER
The segment-powered supply system is an important part of linear induction motor.The effective fault diagnosis method is helpful to the normal operation and troubleshooting of the motor.According to the characteristics of the measured three-phase current waveform,a fault diagnosis method of segment-powered supply system combining an algorithm for extracting the amplitude feature sequence and Transformer model is proposed.In addition,the interpretative method of deep learning model was introduced to assist the model to locate anomalies roughly by it-self.The validity and reliability of these methods were verified by the test data of a segment-powered linear induction motor.

Deep learningTransformer modelTime seriesFault diagnosisSegment-powered supply

姜锋、徐兴华、梁英杰、崔小鹏、廖涛

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海军工程大学舰船综合电力技术国防科技重点实验室 湖北武汉 430033

深度学习 Transformer模型 时间序列 故障诊断 分段供电

国家自然科学基金项目

61701517

2024

计算机应用与软件
上海市计算技术研究所 上海计算机软件技术开发中心

计算机应用与软件

CSTPCD北大核心
影响因子:0.615
ISSN:1000-386X
年,卷(期):2024.41(9)