时频分布是机械滚动轴承故障信号的有效分析方法,特殊情况下的机械故障信号或噪声属于非高斯 Alpha(α)稳定分布,传统的 Stockwell 变换( S 变换)时频方法性能退化甚至失效。基于 S 变换时频和分数低阶矩提出了一种分数低阶 S 变换时频...时频分布是机械滚动轴承故障信号的有效分析方法,特殊情况下的机械故障信号或噪声属于非高斯 Alpha(α)稳定分布,传统的 Stockwell 变换( S 变换)时频方法性能退化甚至失效。基于 S 变换时频和分数低阶矩提出了一种分数低阶 S 变换时频分布算法,为了减少计算量及在线及时分析信号,提出了一种快速分数低阶 S 变换时频算法。仿真结果表明,所提出的分数低阶 S 变换时频算法及其快速算法能很好地工作在高斯噪声和α稳定分布噪声环境,性能优于已有的 S 变换时频。在实际应用中,所提出的时频算法能够较好的提取机械轴承故障信号的故障特征。展开更多
Feature information extraction is one of the key steps in prognostics and health management of rotating machinery.In the present study,an investigation about the feasibility of a methodology based on generalized S tra...Feature information extraction is one of the key steps in prognostics and health management of rotating machinery.In the present study,an investigation about the feasibility of a methodology based on generalized S transform(GST)and singular value decomposition(SVD)methods for feature extraction in rolling bearing,due to local damage under variable conditions,is conducted.The technique adopts the GST method,following the time-frequency analysis,to transform a raw fault signal of the rolling bearing into a two-dimensional complex matrix.And then,the SVD method is performed to decompose the matrix to obtain the feature vectors.By this procedure it is possible to obtain the fault feature information of rolling bearing under different speeds and different loads.In order to streamline the feature parameters of the feature vectors to train more uncomplicated models,the principal component analysis(PCA)subsequently performed.The particle swarm optimization-support vector machine(PSO-SVM)model is used to identify and classify the different fault states of rolling bearing.Furthermore,in order to highlight the superiority of the proposed method some comparisons are conducted with the conventional methods.The obtained results show that the proposed method can effectively extract fault features of the rolling bearing under variable conditions.展开更多
This study is concerned with the diagnosis of discrepancies in a steel truss bridge by identifying dynamic properties from the vibration response signals of the bridges.The vibration response signals collected at brid...This study is concerned with the diagnosis of discrepancies in a steel truss bridge by identifying dynamic properties from the vibration response signals of the bridges.The vibration response signals collected at bridges under three different vehicular speeds of 10 km/hr,20 km/hr,and 30 km/hr are analyzed using statistical features such as kurtosis,magnitude of peak-to-peak,root mean square,crest factor as well as impulse factor in time domain,and Stockwell transform in the time-frequency domain.The considered statistical features except for kurtosis show uncertain behavior.The Stockwell transform showed low-resolution outcomes when the presence of noise in the recorded vibration responses.The elimination of noise and extraction of meaningful dynamic properties from the vibration responses is done by applying a new method which comes from the fusion of Hilbert transform with Spectral kurtosis and bandpass filtering.The outcomes obtained from Hilbert transform processed residual signals which are further filtered using bandpass filter show more robustness and accuracy in characterizing bridge modal frequencies from the noisy vibration responses.The proposed method produces a high-resolution frequency response which can unveil the joint discrepancy in the bridge structure.展开更多
A multiple power quality(MPQ)disturbance has two or more power quality(PQ)disturbances superimposed on a voltage signal.A compact and robust technique is required to identify and classify the MPQ disturbances.This man...A multiple power quality(MPQ)disturbance has two or more power quality(PQ)disturbances superimposed on a voltage signal.A compact and robust technique is required to identify and classify the MPQ disturbances.This manuscript investigated a hybrid algorithm which is designed using parallel processing of voltage with multiple power quality(MPQ)disturbance using stockwell transform(ST)and hilbert transform(HT).This will reduce the computational time to identify theMPQdisturbances,whichmakes the algorithm fast.A MPQ identification index(IPI)is computed using statistical features extracted from the voltage signal using the ST and HT.IPI has different patterns for various types of MPQ disturbances which effectively identify the MPQ disturbances.A MPQ time location index(IPL)is computed using the features extracted from the voltage signal using ST and HT.IPL effectively identifies the initiation and end of PQ disturbances and thereby locates the MPQ events with respect to time.Classification of MPQ disturbances is performed using decision rules in both the noise-free and noisy environments with a 20 dB noise to signal ratio(SNR).The performance of the proposed hybrid algorithm using ST and HT with rule-based decision tree(RBDT)is better compared to the ST and RBDT techniques in terms of accuracy of classification of MPQ disturbances.MATLAB software is used to perform the study.展开更多
文摘时频分布是机械滚动轴承故障信号的有效分析方法,特殊情况下的机械故障信号或噪声属于非高斯 Alpha(α)稳定分布,传统的 Stockwell 变换( S 变换)时频方法性能退化甚至失效。基于 S 变换时频和分数低阶矩提出了一种分数低阶 S 变换时频分布算法,为了减少计算量及在线及时分析信号,提出了一种快速分数低阶 S 变换时频算法。仿真结果表明,所提出的分数低阶 S 变换时频算法及其快速算法能很好地工作在高斯噪声和α稳定分布噪声环境,性能优于已有的 S 变换时频。在实际应用中,所提出的时频算法能够较好的提取机械轴承故障信号的故障特征。
基金Guangdong Provincial Natural Science Foundation of China(Grant No.2020B1515120006)Guangdong Innovation Team(Grant Nos.2020KCXTD015,2022KCXTD029)Guangdong Universities New Information Field(Grant No.2021ZDZX1057).
文摘Feature information extraction is one of the key steps in prognostics and health management of rotating machinery.In the present study,an investigation about the feasibility of a methodology based on generalized S transform(GST)and singular value decomposition(SVD)methods for feature extraction in rolling bearing,due to local damage under variable conditions,is conducted.The technique adopts the GST method,following the time-frequency analysis,to transform a raw fault signal of the rolling bearing into a two-dimensional complex matrix.And then,the SVD method is performed to decompose the matrix to obtain the feature vectors.By this procedure it is possible to obtain the fault feature information of rolling bearing under different speeds and different loads.In order to streamline the feature parameters of the feature vectors to train more uncomplicated models,the principal component analysis(PCA)subsequently performed.The particle swarm optimization-support vector machine(PSO-SVM)model is used to identify and classify the different fault states of rolling bearing.Furthermore,in order to highlight the superiority of the proposed method some comparisons are conducted with the conventional methods.The obtained results show that the proposed method can effectively extract fault features of the rolling bearing under variable conditions.
文摘This study is concerned with the diagnosis of discrepancies in a steel truss bridge by identifying dynamic properties from the vibration response signals of the bridges.The vibration response signals collected at bridges under three different vehicular speeds of 10 km/hr,20 km/hr,and 30 km/hr are analyzed using statistical features such as kurtosis,magnitude of peak-to-peak,root mean square,crest factor as well as impulse factor in time domain,and Stockwell transform in the time-frequency domain.The considered statistical features except for kurtosis show uncertain behavior.The Stockwell transform showed low-resolution outcomes when the presence of noise in the recorded vibration responses.The elimination of noise and extraction of meaningful dynamic properties from the vibration responses is done by applying a new method which comes from the fusion of Hilbert transform with Spectral kurtosis and bandpass filtering.The outcomes obtained from Hilbert transform processed residual signals which are further filtered using bandpass filter show more robustness and accuracy in characterizing bridge modal frequencies from the noisy vibration responses.The proposed method produces a high-resolution frequency response which can unveil the joint discrepancy in the bridge structure.
文摘A multiple power quality(MPQ)disturbance has two or more power quality(PQ)disturbances superimposed on a voltage signal.A compact and robust technique is required to identify and classify the MPQ disturbances.This manuscript investigated a hybrid algorithm which is designed using parallel processing of voltage with multiple power quality(MPQ)disturbance using stockwell transform(ST)and hilbert transform(HT).This will reduce the computational time to identify theMPQdisturbances,whichmakes the algorithm fast.A MPQ identification index(IPI)is computed using statistical features extracted from the voltage signal using the ST and HT.IPI has different patterns for various types of MPQ disturbances which effectively identify the MPQ disturbances.A MPQ time location index(IPL)is computed using the features extracted from the voltage signal using ST and HT.IPL effectively identifies the initiation and end of PQ disturbances and thereby locates the MPQ events with respect to time.Classification of MPQ disturbances is performed using decision rules in both the noise-free and noisy environments with a 20 dB noise to signal ratio(SNR).The performance of the proposed hybrid algorithm using ST and HT with rule-based decision tree(RBDT)is better compared to the ST and RBDT techniques in terms of accuracy of classification of MPQ disturbances.MATLAB software is used to perform the study.
