Compound fault,as a primary failure leading to unexpected downtime of rotating machinery,dramatically increases the difficulty in fault diagnosis.To deal with the difficulty encountered in implementing compound fault ...Compound fault,as a primary failure leading to unexpected downtime of rotating machinery,dramatically increases the difficulty in fault diagnosis.To deal with the difficulty encountered in implementing compound fault diagnosis(CFD),researchers and engineers from industry and academia have made numerous significant breakthroughs in recent years.Admittedly,many systematic surveys focused on fault diagnosis have been conducted by reputable researchers.Nevertheless,previous review articles paid more attention to fault diagnosis with several single or independent faults,resulting in that there is still lacking a comprehensive survey on CFD.Therefore,to fulfill the above requirements,it is necessary to provide an in-depth overview of fault diagnosis methods or algorithms for compound faults of rotating machinery and uncover potential challenges or opportunities that would guide and inspire readers to devote their efforts to promoting fault diagnosis technology more effective and practical.Specifically,the backgrounds,including the related definitions and a new taxonomy of CFD methods,are detailed according to the way of implementing compound fault recognition.Then,the stateof-the-art applications of CFD are overviewed based on relevant publications in the past decades.Finally,the challenges and opportunities associated with implementing CFD are concluded and followed by a conclusion for ending this survey.We believe that this review article can provide a systematic guideline of CFD from different aspects for potential readers and seasoned researchers.展开更多
A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envel...A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to realize single channel compound fault diagnosis of bearings and improve the diagnosis accuracy, an improved CICA algorithm named constrained independent component analysis based on the energy method （E-CICA） is proposed. With the approach, the single channel vibration signal is firstly decomposed into several wavelet coefficients by discrete wavelet transform（DWT） method for the purpose of obtaining multichannel signals. Then the envelope signals of the reconstructed wavelet coefficients are selected as the input of E-CICA algorithm, which fulfills the requirements that the number of sensors is greater than or equal to that of the source signals and makes it more suitable to be processed by CICA strategy. The frequency energy ratio（ER） of each wavelet reconstructed signal to the total energy of the given synchronous signal is calculated, and then the synchronous signal with maximum ER value is set as the reference signal accordingly. By this way, the reference signal contains a priori knowledge of fault source signal and the influence on fault signal extraction accuracy which is caused by the initial phase angle and the duty ratio of the reference signal in the traditional CICA algorithm is avoided. Experimental results show that E-CICA algorithm can effectively separate out the outer-race defect and the rollers defect from the single channel compound fault and fulfill the needs of compound fault diagnosis of rolling bearings, and the running time is 0.12% of that of the traditional CICA algorithm and the extraction accuracy is 1.4 times of that of CICA as well. The proposed research provides a new method to separate single channel compound fault signals.展开更多
Recently,with the urgent demand for data-driven approaches in practical industrial scenarios,the deep learning diagnosis model in noise environments has attracted increasing attention.However,the existing research has...Recently,with the urgent demand for data-driven approaches in practical industrial scenarios,the deep learning diagnosis model in noise environments has attracted increasing attention.However,the existing research has two limitations:(1)the complex and changeable environmental noise,which cannot ensure the high-performance diagnosis of the model in different noise domains and(2)the possibility of multiple faults occurring simultaneously,which brings challenges to the model diagnosis.This paper presents a novel anti-noise multi-scale convolutional neural network(AM-CNN)for solving the issue of compound fault diagnosis under different intensity noises.First,we propose a residual pre-processing block according to the principle of noise superposition to process the input information and present the residual loss to construct a new loss function.Additionally,considering the strong coupling of input information,we design a multi-scale convolution block to realize multi-scale feature extraction for enhancing the proposed model’s robustness and effectiveness.Finally,a multi-label classifier is utilized to simultaneously distinguish multiple bearing faults.The proposed AM-CNN is verified under our collected compound fault dataset.On average,AM-CNN improves 39.93%accuracy and 25.84%F1-macro under the no-noise working condition and 45.67%accuracy and 27.72%F1-macro under different intensity noise working conditions compared with the existing methods.Furthermore,the experimental results show that AM-CNN can achieve good cross-domain performance with 100%accuracy and 100%F1-macro.Thus,AM-CNN has the potential to be an accurate and stable fault diagnosis tool.展开更多
It is significant to detect the fault type and assess the fault level as early as possible for avoiding catastrophic accidents.Due to diversity and complexity,the compound faults detection of rotating machinery under ...It is significant to detect the fault type and assess the fault level as early as possible for avoiding catastrophic accidents.Due to diversity and complexity,the compound faults detection of rotating machinery under non-stationary operation turns to be a challenging task.Multiwavelet with two or more base functions may match two or more features of compound faults,which may supply a possible solution to compound faults detection.However,the fixed basis functions of multiwavelet transform,which are not related with the vibration signal,may reduce the accuracy of compound faults detection.Moreover,the decomposition results of multiwavelet transform not being own time-invariant is harmful to extract the features of periodical impulses.Furthermore,multiwavelet transform only focuses on the multi-resolution analysis in the low frequency band,and may leave out the useful features of compound faults.To overcome these shortcomings,a novel method called adaptive redundant multiwavelet packet(ARMP) is proposed based on the two-scale similarity transforms.Besides,the relative energy ratio at the characteristic frequency of the concerned component is computed to select the sensitive frequency bands of multiwavelet packet coefficients.The proposed method was used to analyze the compound faults of rolling element bearing.The results showed that the proposed method could enhance the ability of compound faults detection of rotating machinery.展开更多
辛周期模态分解(symplectic period mode decomposition, SPMD)方法可以准确地提取周期脉冲分量,是一种有效的滚动轴承单一故障诊断方法。但在滚动轴承出现复合故障时,尤其是强背景噪声下,周期脉冲信号往往较微弱,使得SPMD难以提取出不...辛周期模态分解(symplectic period mode decomposition, SPMD)方法可以准确地提取周期脉冲分量,是一种有效的滚动轴承单一故障诊断方法。但在滚动轴承出现复合故障时,尤其是强背景噪声下,周期脉冲信号往往较微弱,使得SPMD难以提取出不同周期的脉冲分量,进而限制了其在复合故障诊断中的应用。对此,提出了改进的辛周期模态分解(improved symplectic period mode decomposition, ISPMD)方法。该方法首先采用求差增强技术和最小噪声幅值反卷积相结合的方法对信号进行降噪,增强周期脉冲,以准确估计故障周期;然后构造对应的周期截断矩阵,并通过辛几何相似变换和周期冲击强度获得辛几何周期分量;最后对残差信号采用迭代分解,进而得到不同周期的辛几何周期分量。试验结果表明,ISPMD能准确提取出周期脉冲分量,是一种有效的滚动轴承复合故障诊断方法。展开更多
动力电池是电动化飞行得以实现的重要组成部分,其技术层次和安全水准对电动垂直起降飞行器(Electric Vertical Take off and Landing aircraft,eVTOL)的商业化推广尤为重要。本文在典型飞行任务下,研究电池性能对eVTOL飞行器的运营性能...动力电池是电动化飞行得以实现的重要组成部分,其技术层次和安全水准对电动垂直起降飞行器(Electric Vertical Take off and Landing aircraft,eVTOL)的商业化推广尤为重要。本文在典型飞行任务下,研究电池性能对eVTOL飞行器的运营性能、适航性能和安全性能的影响。利用开源软件SUAVE(Stanford University Aerospace Vehicle Environment,SUAVE)对复合翼eVTOL进行了整机与动力总成的建模,利用故障树分析(Fault Tree analysis,FTA)方法对动力总成进行了安全性分析。通过仿真,发现在现有电池技术水平下,电池的放电倍率约束是决定电池性能需求的关键限制条件,针对本文设计的eVTOL,372 Wh/kg是满足所有安全约束的最低能量密度,在使用过程中电池容量的衰退是设计者选择电池能量密度的重要参考指标。单独改善电池的可靠性对动力总成可靠性的提升是有限的,但电池性能的衰退将使电池成为动力总成失效的主要因素。通过FTA发现本文搭建的典型动力总成失效率为1.524×10^(-7),接近SC-VTOL-01中单座飞行器的基础级灾难性故障率要求。展开更多
基金This work was supported in part by the National Natural Science Foundation of China under Grants 52205100,52275111,and 52205101in part by the Natural Science Foundations of Guangdong Province-China under Grants 2023A1515012856in part by China Postdoctoral Science Foundation under Grant 2022M711197.
文摘Compound fault,as a primary failure leading to unexpected downtime of rotating machinery,dramatically increases the difficulty in fault diagnosis.To deal with the difficulty encountered in implementing compound fault diagnosis(CFD),researchers and engineers from industry and academia have made numerous significant breakthroughs in recent years.Admittedly,many systematic surveys focused on fault diagnosis have been conducted by reputable researchers.Nevertheless,previous review articles paid more attention to fault diagnosis with several single or independent faults,resulting in that there is still lacking a comprehensive survey on CFD.Therefore,to fulfill the above requirements,it is necessary to provide an in-depth overview of fault diagnosis methods or algorithms for compound faults of rotating machinery and uncover potential challenges or opportunities that would guide and inspire readers to devote their efforts to promoting fault diagnosis technology more effective and practical.Specifically,the backgrounds,including the related definitions and a new taxonomy of CFD methods,are detailed according to the way of implementing compound fault recognition.Then,the stateof-the-art applications of CFD are overviewed based on relevant publications in the past decades.Finally,the challenges and opportunities associated with implementing CFD are concluded and followed by a conclusion for ending this survey.We believe that this review article can provide a systematic guideline of CFD from different aspects for potential readers and seasoned researchers.
基金Supported by National Natural Science Foundation of China(Grant No.51475034)
文摘A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to realize single channel compound fault diagnosis of bearings and improve the diagnosis accuracy, an improved CICA algorithm named constrained independent component analysis based on the energy method （E-CICA） is proposed. With the approach, the single channel vibration signal is firstly decomposed into several wavelet coefficients by discrete wavelet transform（DWT） method for the purpose of obtaining multichannel signals. Then the envelope signals of the reconstructed wavelet coefficients are selected as the input of E-CICA algorithm, which fulfills the requirements that the number of sensors is greater than or equal to that of the source signals and makes it more suitable to be processed by CICA strategy. The frequency energy ratio（ER） of each wavelet reconstructed signal to the total energy of the given synchronous signal is calculated, and then the synchronous signal with maximum ER value is set as the reference signal accordingly. By this way, the reference signal contains a priori knowledge of fault source signal and the influence on fault signal extraction accuracy which is caused by the initial phase angle and the duty ratio of the reference signal in the traditional CICA algorithm is avoided. Experimental results show that E-CICA algorithm can effectively separate out the outer-race defect and the rollers defect from the single channel compound fault and fulfill the needs of compound fault diagnosis of rolling bearings, and the running time is 0.12% of that of the traditional CICA algorithm and the extraction accuracy is 1.4 times of that of CICA as well. The proposed research provides a new method to separate single channel compound fault signals.
基金supported by the National Key R&D Program of China(Grant No.2020YFB1709604)the State Key Laboratory of Mechanical System and Vibration(Grant No.MSVZD202103)+1 种基金the Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0102)。
文摘Recently,with the urgent demand for data-driven approaches in practical industrial scenarios,the deep learning diagnosis model in noise environments has attracted increasing attention.However,the existing research has two limitations:(1)the complex and changeable environmental noise,which cannot ensure the high-performance diagnosis of the model in different noise domains and(2)the possibility of multiple faults occurring simultaneously,which brings challenges to the model diagnosis.This paper presents a novel anti-noise multi-scale convolutional neural network(AM-CNN)for solving the issue of compound fault diagnosis under different intensity noises.First,we propose a residual pre-processing block according to the principle of noise superposition to process the input information and present the residual loss to construct a new loss function.Additionally,considering the strong coupling of input information,we design a multi-scale convolution block to realize multi-scale feature extraction for enhancing the proposed model’s robustness and effectiveness.Finally,a multi-label classifier is utilized to simultaneously distinguish multiple bearing faults.The proposed AM-CNN is verified under our collected compound fault dataset.On average,AM-CNN improves 39.93%accuracy and 25.84%F1-macro under the no-noise working condition and 45.67%accuracy and 27.72%F1-macro under different intensity noise working conditions compared with the existing methods.Furthermore,the experimental results show that AM-CNN can achieve good cross-domain performance with 100%accuracy and 100%F1-macro.Thus,AM-CNN has the potential to be an accurate and stable fault diagnosis tool.
基金supported by the National Natural Science Foundation of China (Grant Nos. 50975220 and 51035007)the National Basic Research Program of China ("973" Program) (Grant No. 2009CB724405)the Important National Science and Technology Specific Projects (Grant No.2010ZX04014-016)
文摘It is significant to detect the fault type and assess the fault level as early as possible for avoiding catastrophic accidents.Due to diversity and complexity,the compound faults detection of rotating machinery under non-stationary operation turns to be a challenging task.Multiwavelet with two or more base functions may match two or more features of compound faults,which may supply a possible solution to compound faults detection.However,the fixed basis functions of multiwavelet transform,which are not related with the vibration signal,may reduce the accuracy of compound faults detection.Moreover,the decomposition results of multiwavelet transform not being own time-invariant is harmful to extract the features of periodical impulses.Furthermore,multiwavelet transform only focuses on the multi-resolution analysis in the low frequency band,and may leave out the useful features of compound faults.To overcome these shortcomings,a novel method called adaptive redundant multiwavelet packet(ARMP) is proposed based on the two-scale similarity transforms.Besides,the relative energy ratio at the characteristic frequency of the concerned component is computed to select the sensitive frequency bands of multiwavelet packet coefficients.The proposed method was used to analyze the compound faults of rolling element bearing.The results showed that the proposed method could enhance the ability of compound faults detection of rotating machinery.
文摘辛周期模态分解(symplectic period mode decomposition, SPMD)方法可以准确地提取周期脉冲分量,是一种有效的滚动轴承单一故障诊断方法。但在滚动轴承出现复合故障时,尤其是强背景噪声下,周期脉冲信号往往较微弱,使得SPMD难以提取出不同周期的脉冲分量,进而限制了其在复合故障诊断中的应用。对此,提出了改进的辛周期模态分解(improved symplectic period mode decomposition, ISPMD)方法。该方法首先采用求差增强技术和最小噪声幅值反卷积相结合的方法对信号进行降噪,增强周期脉冲,以准确估计故障周期;然后构造对应的周期截断矩阵,并通过辛几何相似变换和周期冲击强度获得辛几何周期分量;最后对残差信号采用迭代分解,进而得到不同周期的辛几何周期分量。试验结果表明,ISPMD能准确提取出周期脉冲分量,是一种有效的滚动轴承复合故障诊断方法。
文摘动力电池是电动化飞行得以实现的重要组成部分,其技术层次和安全水准对电动垂直起降飞行器(Electric Vertical Take off and Landing aircraft,eVTOL)的商业化推广尤为重要。本文在典型飞行任务下,研究电池性能对eVTOL飞行器的运营性能、适航性能和安全性能的影响。利用开源软件SUAVE(Stanford University Aerospace Vehicle Environment,SUAVE)对复合翼eVTOL进行了整机与动力总成的建模,利用故障树分析(Fault Tree analysis,FTA)方法对动力总成进行了安全性分析。通过仿真,发现在现有电池技术水平下,电池的放电倍率约束是决定电池性能需求的关键限制条件,针对本文设计的eVTOL,372 Wh/kg是满足所有安全约束的最低能量密度,在使用过程中电池容量的衰退是设计者选择电池能量密度的重要参考指标。单独改善电池的可靠性对动力总成可靠性的提升是有限的,但电池性能的衰退将使电池成为动力总成失效的主要因素。通过FTA发现本文搭建的典型动力总成失效率为1.524×10^(-7),接近SC-VTOL-01中单座飞行器的基础级灾难性故障率要求。
