深度学习与异常信息融合的内燃机燃油系统智能诊断方法  

A deep learning and anomaly information fusion intelligent diagnosis method for the fuel system in internal combustion engine

作　　者：张振京 曹石 窦全礼 宋业栋 褚士龙 茆志伟[4] ZHANG ZhenJing;CAO Shi;DOU QuanLi;SONG YeDong;CHU ShiLong;MAO ZhiWei(State Key Laboratory of Engine and Powertrain System,Weifang 261069;Weichai Power Co.,Ltd.,Weifang 261069;School of Civil and Hydraulic Engineering,Huazhong University of Science and Technology,Wuhan 430074;Key Laboratory of Engine Health Monitoring-Control and Networking of Ministry of Education,Beijing University of Chemical Technology,Beijing 100029,China)

机构地区：[1]内燃机与动力系统全国重点实验室,潍坊261069 [2]潍柴动力股份有限公司,潍坊261069 [3]华中科技大学土木与水利工程学院,武汉430074 [4]北京化工大学发动机健康监控及网络化教育部重点实验室,北京100029

出　　处：《北京化工大学学报(自然科学版)》2025年第2期76-87,共12页Journal of Beijing University of Chemical Technology(Natural Science Edition)

基　　金：内燃机与动力系统全国重点实验室开放基金(skler-202107)。

摘　　要：考虑到内燃机燃油系统的健康状态对整机性能具有重要影响,但目前内燃机配机监测参数尚未得到充分利用,为此提出一种基于深度学习与异常信息融合的智能诊断方法,以达到利用现有配机监测参数和能够获取的异常现象信息来提升内燃机运行可靠性的目的。首先引入互信息理论实现对配机监测参数的自动分组,并采用降噪自编码器和注意力机制结合双向门控循环单元构建一种针对内燃机配机监测参数的深度学习诊断模型,实现了燃油系统典型故障的初步智能诊断。进一步,考虑到内燃机实际运行中获取的异常现象对故障诊断的辅助价值,构建贝叶斯网络,并采用Leaky-Noisy-Or模型量化异常现象与特定故障之间的相关性,以此优化故障智能诊断结果。最后,将GT-Power仿真模拟获得的燃油系统故障样本数据集代入所提方法模型中,诊断结果验证了所提方法在提高内燃机燃油系统故障诊断准确率方面的有效性。研究成果提供了一种基于配机监测参数的深度学习智能诊断模型,同时也为燃油系统故障诊断提供了一种新的信息融合途径,对内燃机智能诊断具有重要的实际应用价值。The health state of the fuel system of an internal combustion engine has an important impact on the per-formance of the whole engine,but the current internal combustion engine dispenser monitoring parameters have not yet been fully utilized.For this reason,this paper proposes an intelligent diagnostic method based on the fusion of deep learning and anomalous information,which realizes the utilization of the existing dispenser monitoring param-eters and the anomalous phenomenon information,leading to improved reliability of the internal combustion engine operation.Mutual information theory is first introduced to realize the automatic grouping of the dispenser monitor-ing parameters,and a deep learning diagnostic model is constructed for the dispenser monitoring parameters of the internal combustion engine by using the denoising autoencoder and an attention mechanism in combination with a bidirectional gate recurrent unit.This affords a preliminary intelligent diagnosis of the typical faults of the fuel system.Subseguently,considering the auxiliary value of anomalies acquired during the actual operation of the internal combustion engine for fault diagnosis,a Bayesian network is constructed and a Leaky-Noisy-Or model is used to quantify the correlation between anomalies and specific faults,thus optimizing the results of the intelligent diagnosis of faults.Finally,the fuel system fault sample dataset obtained from GT-Power simulation is substituted into the model,and the diagnostic results verify the effectiveness of the proposed method in improving the accuracy of fuel system fault diagnosis in internal combustion engines.This provides a deep learning intelligent diagnosis model based on the monitoring parameters of the dispenser,and also provides a new information fusion pathway for fuel system fault diagnosis,and has important practical application value for the intelligent diagnosis of internal combustion engines.

关 键 词：内燃机燃油供给系统 故障诊断 配机监测参数 Leaky-Noisy-Or模型 异常现象信息融合 

分 类 号：TH17[机械工程—机械制造及自动化]