基于特征融合的空压机故障诊断算法研究  

Research on Fault Diagnosis Algorithm of Air Compressor based on Feature Fusion

作　　者：王辅民 周红娟 冯国亮[3] 邢雪[1] WANG Fumin;ZHOU Hongjuan;FENG Guoliang;XING Xue(School of Information and Control Engineering,Jilin Institute of Chemical Technology,Jilin City 132022,China;China Water Resources and Hydropower Engineering Consulting Bohai Co.Ltd.Tianjin 300222,China;School of Automation Engineering,Northeast Electric Power University,Jilin City 132022,China)

机构地区：[1]吉林化工学院信息与控制工程学院,吉林吉林132022 [2]中国水利水电建设工程咨询渤海有限公司,天津300222 [3]东北电力大学自动化工程学院,吉林吉林132012 [4]吉林化工学院

出　　处：《吉林化工学院学报》2024年第3期37-41,共5页Journal of Jilin Institute of Chemical Technology

摘　　要：空气压缩机作为工业生产的重要设备,其运行状态直接影响到生产的成败。然而,传统的故障诊断方法不易获得准确的故障特征,不同工作条件之间的特征分布差异的度量不是充分的域自适应,难以达到较好的识别精度,并且空气压缩机运行时产生一定的背景噪声,形成一定干扰,影响故障识别准确性。为了克服上述限制,提出了一种基于特征融合的空气压缩机故障诊断方法。首先,分别提取空气压缩机的梅尔倒谱系数特征和小波变换特征。然后,在决策层对置信度分数和预测边界框进行晚期融合,并根据评估指标选择最佳网络模型完成分类。对比实验结果表明,该特征融合方法显著提高了故障识别的准确性。As a critical piece of industrial production equipment,the operational status of an air compressor directly affects the success of production.However,traditional fault diagnosis methods struggle to accurately obtain fault characteristics.The feature distribution differences between different working conditions are not sufficiently measured by domain adaptation,making it difficult to achieve high recognition accuracy.Additionally,background noise generated during the operation of air compressors introduces interference that impacts fault identification accuracy.To overcome these limitations,a feature fusion-based fault diagnosis method for air compressors was proposed.Firstly,Mel-frequency cepstral coefficients(MFCC)features and wavelet transform features of the air compressor are extracted separately.Then,at the decision layer,confidence scores and predicted bounding boxes were fused late in the process,and the best network model was selected based on evaluation metrics to complete the classification.Comparative experimental results showed that this feature fusion method significantly improves fault identification accuracy.

关 键 词：特征融合 声纹识别 故障识别 特征提取 空气压缩机 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]