Identification of denatured and normal biological tissues based on compressed sensing and refined composite multi-scale fuzzy entropy during high intensity focused ultrasound treatment

In high intensity focused ultrasound(HIFU)treatment,it is crucial to accurately identify denatured and normal biological tissues.In this paper,a novel method based on compressed sensing(CS)and refined composite multi-scale fuzzy entropy(RCMFE)is proposed.First,CS is used to denoise the HIFU echo signals.Then the multi-scale fuzzy entropy(MFE)and RCMFE of the denoised HIFU echo signals are calculated.This study analyzed 90 cases of HIFU echo signals,including 45 cases in normal status and 45 cases in denatured status,and the results show that although both MFE and RCMFE can be used to identify denatured tissues,the intra-class distance of RCMFE on each scale factor is smaller than MFE,and the inter-class distance is larger than MFE.Compared with MFE,RCMFE can calculate the complexity of the signal more accurately and improve the stability,compactness,and separability.When RCMFE is selected as the characteristic parameter,the RCMFE difference between denatured and normal biological tissues is more evident than that of MFE,which helps doctors evaluate the treatment effect more accurately.When the scale factor is selected as 16,the best distinguishing effect can be obtained.

Modified multi-scale symbolic dynamic entropy and fuzzy broad learning-based fast fault diagnosis of railway point machines

Railway point machine(RPM)condition monitoring has attracted engineers’attention for safe train operation and accident prevention.To realize the fast and accurate fault diagnosis of RPMs,this paper proposes a method based on entropy measurement and broad learning system(BLS).Firstly,the modified multi-scale symbolic dynamic entropy(MMSDE)module extracts dynamic characteristics from the collected acoustic signals as entropy features.Then,the fuzzy BLS takes the above entropy features as input to complete model training.Fuzzy BLS introduces the Takagi-Sug eno fuzzy system into BLS,which improves the model’s classification performance while considering computational speed.Experimental results indicate that the proposed method significantly reduces the running time while maintaining high accuracy.

基于SVMFE的往复压缩机气阀故障诊断方法

针对多尺度模糊熵(Multi-scale Fuzzy Entropy,MFE)在粗粒化计算过程中存在的问题,为准确地提取往复压缩机的故障特征,将滑动方差法引入到多尺度模糊熵中,提出基于滑动方差的多尺度模糊熵(Sliding Variance Multiscale Fuzzy Entropy,SVMFE)方法。以高斯白噪声为仿真信号,将SVMFE方法与MFE分析对比,仿真结果表明SVMFE方法在衡量序列复杂性上更准确、更稳定。基于此,提出一种基于SVMFE与极限学习机(Extreme Learning Machine,ELM)的往复压缩机故障诊断方法。最后,运用所提方法对气阀故障信号分析,与基于多尺度模糊熵的故障诊断方法进行对比,验证了所提出方法的有效性,且具有较高的故障识别率。

基于多尺度模糊熵和STOA-SVM的风机轴承故障诊断

针对风机轴承振动信号故障特征提取困难的问题,提出了一种基于多尺度模糊熵(MFE)特征提取,并结合乌燕鸥优化算法(STOA)优化支持向量机(SVM)的风机轴承故障诊断方法。首先采集原始振动信号并计算其多层次模糊熵,其次构造故障特征向量集合作为SVM的输入,最后采用STOA优化SVM对轴承故障进行分类诊断。通过凯斯西储大学轴承振动数据进行仿真,结果显示轴承故障诊断准确率达到了99.3%,证明了所提方法具有较高的准确度和有效性。

基于CEEMDAN-多尺度模糊熵和ISRNN的球磨机负荷识别

针对球磨机振动信号具有非线性、非平稳性特点导致的负荷状态难以识别问题,提出一种基于自适应噪声的完备经验模态分解(CEEMDAN),多尺度模糊熵(MFE)和改进堆叠式循环神经网络(ISRNN)的磨机负荷预测方法。首先,采用CEEMDAN算法分解球磨机振动信号以获得本征模态分量;其次,利用MFE提取负荷状态特征;最后,将特征向量作为ISRNN的输入,球磨机负荷状态作为输出,建立球磨机负荷识别模型。试验结果表明,该方法在负荷识别时有较高的精准性,整体识别率高达98.67%,证明了CEEMDAN-MFE特征提取结合ISRNN的方法可实现对球磨机负荷状态的准确识别。