摘要
为提高发动机故障诊断准确率,提出了基于同步压缩广义S变换(synchrosqueezing generalized S-transform,简称SSGST)与中心对称局部二值模式(center-symmetric local binary patterm,简称CSLBP)的故障诊断方法。首先,针对信号时频分析中的能量泄露、频谱涂抹、频带混叠和时频分辨率较低的问题,基于同步压缩算法与广义S变换提出了SSGST,对缸盖振动信号进行时频分析得到时频聚集性较高的二维时频图;然后,利用CSLBP提取缸盖振动信号时频图的纹理谱特征,并将其输入交叉验证寻优的核极限学习机对发动机进行故障诊断。实验结果表明,SSGST的能量聚集效果好,时频分辨率高,各频带分布较窄且不存在混叠,能够有效分离出非线性混合信号中的各频带分量;时频图的CSLBP纹理谱特征维数较低,且具有良好的类内聚集性和类间离散性;利用交叉验证寻优的KELM对故障特征进行分类,实现发动机故障诊断,获得了较高的诊断速度和精度。
To improve the accuracy of engine fault diagnosis,a fault diagnosis method based on synchrosqueezing generalized S-transform(SSGST)and center-symmetric local binary pattern(CSLBP)is proposed. Firstly,aiming at the problems of energy leakage,spectrum smearing,frequency band aliasing and low time-frequency resolution in time-frequency analysis of signals,SSGST is proposed based on the synchrosqueezing algorithm and generalized S-transform. Two-dimensional time-frequency images with high time-frequency aggregation are obtained by the analysis of cylinder head vibration signals based on SSGST. Then,texture spectrum features of time-frequency images are extracted by CSLBP and input into the kernal extream learning machine(KELM)with cross validation optimization to diagnose engine faults. The results of the simulated signal analysis and engine fault diagnosis experiments show that SSGST has good energy aggregation performance,high time-frequency resolution,narrow frequency band distribution and no aliasing,and can effectively separate each component from the non-linear mixed signal. CSLBP-based texture spectrum features has low dimension,good intra-class aggregation and inter-class discreteness. The KELM with cross validation optimization is used to classify the fault features,and diagnose the engine faults,which obtains high diagnosis speed and accuracy.
作者
刘敏
陈健
张岩
陈玉昆
范红波
张英堂
LIU Min;CHEN Jian;ZHANG Yan;CHEN Yukun;FAN Hongbo;ZHANG Yingtang(People's Liberation Army of China No.96901 Beijing,100085,China;Department No.7,Army Engineering University Shijiazhuang,050003,China)
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2021年第5期984-990,1038,共8页
Journal of Vibration,Measurement & Diagnosis
基金
国家自然科学基金资助项目(51305454)。
关键词
发动机
时频分析
故障诊断
同步压缩广义S变换
中心对称局部二值模式
engine
time-frequency analysis
fault diagnosis
synchrosqueezing generalized S-transform
center-symmetric local binary pattern
作者简介
第一作者:刘敏,男,1990年8月生,助理研究员。主要研究方向为数字信号处理与模式识别。曾发表《基于多尺度核独立成分分析的柴油机故障诊断》(《振动、测试与诊断》2017年第37卷第5期)等论文。E‑mail:hunter1848@163.com。
