A new sensor fault diagnosis method based on structured kernel principal component analysis （KPCA） is proposed for nonlinear processes. By performing KPCA on subsets of variables, a set of structured residuals, i.e....A new sensor fault diagnosis method based on structured kernel principal component analysis （KPCA） is proposed for nonlinear processes. By performing KPCA on subsets of variables, a set of structured residuals, i.e., scaled powers of KPCA, can be obtained in the same way as partial PCA. The structured residuals are utilized in composing an isolation scheme for sensor fault diagnosis, according to a properly designed incidence matrix. Sensor fault sensitivity and critical sensitivity are defined, based on which an incidence matrix optimization algorithm is proposed to improve the performance of the structured KPCA. The effectiveness of the proposed method is demonstrated on the simulated continuous stirred tank reactor （CSTR） process.展开更多
Shear-type structures are common structural forms in industrial and civil buildings,such as concrete and steel frame structures.Fault diagnosis of shear-type structures is an important topic to ensure the normal use o...Shear-type structures are common structural forms in industrial and civil buildings,such as concrete and steel frame structures.Fault diagnosis of shear-type structures is an important topic to ensure the normal use of structures.The main drawback of existing damage assessment methods is that they require accurate structural finite element models for damage assessment.However,for many shear-type structures,it is difficult to obtain accurate FEM.In order to avoid finite elementmodeling,amodel-freemethod for diagnosing shear structure defects is developed in this paper.This method only needs to measure a few low-order vibration modes of the structure.The proposed defect diagnosis method is divided into two stages.In the first stage,the location of defects in the structure is determined based on the difference between the virtual displacements derived from the dynamic flexibility matrices before and after damage.In the second stage,damage severity is evaluated based on an improved frequency sensitivity equation.Themain innovations of this method lie in two aspects.The first innovation is the development of a virtual displacement difference method for determining the location of damage in the shear structure.The second is to improve the existing frequency sensitivity equation to calculate the damage degree without constructing the finite elementmodel.Thismethod has been verified on a numerical example of a 22-story shear frame structure and an experimental example of a three-story steel shear structure.Based on numerical analysis and experimental data validation,it is shown that this method only needs to use the low-order modes of structural vibration to diagnose the defect location and damage degree,and does not require finite element modeling.The proposed method should be a very simple and practical defect diagnosis technique in engineering practice.展开更多
An adaptive morphological impulses extraction method （AMIE） for bearing fault diagnosis is pro- posed. This method uses the morphological closing operation with a flat structuring element （SE） to extract impulsive...An adaptive morphological impulses extraction method （AMIE） for bearing fault diagnosis is pro- posed. This method uses the morphological closing operation with a flat structuring element （SE） to extract impulsive features from vibration signals with strong background noise. To optimize the flat SE, firstly, a theoretical study is carried out to investigate the effects of the length of the flat SE. Then, based on the theoretical findings, an adaptive algorithm for the flat SE optimization is proposed. The AMIE method is tested by the simulated signal and bearing vibration signals. The test results show that this method is effective and robust in extracting impulsive features.展开更多
Working conditions of rolling bearings of wind turbine generators are complicated, and their vibration signals often show non-linear and non-stationary characteristics. In order to improve the efficiency of feature ex...Working conditions of rolling bearings of wind turbine generators are complicated, and their vibration signals often show non-linear and non-stationary characteristics. In order to improve the efficiency of feature extraction of wind turbine rolling bearings and to strengthen the feature information, a new structural element and an adaptive algorithm based on the peak energy are proposed,which are combined with spectral correlation analysis to form a fault diagnosis algorithm for wind turbine rolling bearings. The proposed method firstly addresses the problem of impulsive signal omissions that are prone to occur in the process of fault feature extraction of traditional structural elements and proposes a "W" structural element to capture more characteristic information. Then, the proposed method selects the scale of multi-scale mathematical morphology, aiming at the problem of multi-scale mathematical morphology scale selection and structural element expansion law. An adaptive algorithm based on peak energy is proposed to carry out morphological scale selection and structural element expansion by improving the computing efficiency and enhancing the feature extraction effect.Finally, the proposed method performs spectral correlation analysis in the frequency domain for an unknown signal of the extracted feature and identifies the fault based on the correlation coefficient. The method is verified by numerical examples using experimental rig bearing data and actual wind field acquisition data and compared with traditional triangular and flat structural elements. The experimental results show that the new structural elements can more effectively extract the pulses in the signal and reduce noise interference,and the fault-diagnosis algorithm can accurately identify the fault category and improve the reliability of the results.展开更多
Recently, a new type of IMM （interacting multiple model） method was introduced based on the relatively new SVSF （smooth variable structure filter）, and is referred to as the IMM-SVSF. The SVSF is a type of sliding...Recently, a new type of IMM （interacting multiple model） method was introduced based on the relatively new SVSF （smooth variable structure filter）, and is referred to as the IMM-SVSF. The SVSF is a type of sliding mode estimator that is formulated in a predictor-corrector fashion. This strategy keeps the estimated state bounded within a region of the true state trajectory, thus creating a stable and robust estimation process. The IMM method may be utilized for fault detection and diagnosis, and is classified as a model-based method. In this paper, for the purposes of fault detection, the IMM-SVSF is applied through simulation on a simple battery system which is modeled from a hybrid electric vehicle.展开更多
针对于框架结构的使用环境恶劣,同时常常伴随着大量的噪声,在使用普通的一维卷积神经网络对框架结构进行故障诊断时,存在无法做出有效故障诊断的问题。本研究在一种抗噪声能力较强的卷积神经网络中加入Inception模块,提出了一种识别率...针对于框架结构的使用环境恶劣,同时常常伴随着大量的噪声,在使用普通的一维卷积神经网络对框架结构进行故障诊断时,存在无法做出有效故障诊断的问题。本研究在一种抗噪声能力较强的卷积神经网络中加入Inception模块,提出了一种识别率和抗噪声能力更高的卷积神经网络—BICNN(Convolution Neural Network based on Inception),并用BICNN卷积神经网络基于数据驱动的方式,对楼体框架模型进行了集成故障诊断研究。集成诊断结果表明BICNN具有更高的识别率和较强的抗噪声能力,而且在训练步数较少的情况下振荡次数少收敛情况良好。因此采取本研究所提出的方法,对框架结构进行故障诊断时具有高诊断率和稳定性,为维护框架结构的稳定运行具有重大安全意义。展开更多
基金supported by Scientific Reserch Fund of SiChuan Provincial Education Department (No.07ZB013)by the Scientific ResearchFoundation of CUIT (No.CSRF200704)
文摘A new sensor fault diagnosis method based on structured kernel principal component analysis （KPCA） is proposed for nonlinear processes. By performing KPCA on subsets of variables, a set of structured residuals, i.e., scaled powers of KPCA, can be obtained in the same way as partial PCA. The structured residuals are utilized in composing an isolation scheme for sensor fault diagnosis, according to a properly designed incidence matrix. Sensor fault sensitivity and critical sensitivity are defined, based on which an incidence matrix optimization algorithm is proposed to improve the performance of the structured KPCA. The effectiveness of the proposed method is demonstrated on the simulated continuous stirred tank reactor （CSTR） process.
基金the Zhejiang Public Welfare Technology Application Research Project(LGF22E080021)Ningbo Natural Science Foundation Project(202003N4169)+2 种基金Natural Science Foundation of China(11202138,52008215)the Natural Science Foundation of Zhejiang Province,China(LQ20E080013)the Major Special Science and Technology Project(2019B10076)of“Ningbo Science and Technology Innovation 2025”.
文摘Shear-type structures are common structural forms in industrial and civil buildings,such as concrete and steel frame structures.Fault diagnosis of shear-type structures is an important topic to ensure the normal use of structures.The main drawback of existing damage assessment methods is that they require accurate structural finite element models for damage assessment.However,for many shear-type structures,it is difficult to obtain accurate FEM.In order to avoid finite elementmodeling,amodel-freemethod for diagnosing shear structure defects is developed in this paper.This method only needs to measure a few low-order vibration modes of the structure.The proposed defect diagnosis method is divided into two stages.In the first stage,the location of defects in the structure is determined based on the difference between the virtual displacements derived from the dynamic flexibility matrices before and after damage.In the second stage,damage severity is evaluated based on an improved frequency sensitivity equation.Themain innovations of this method lie in two aspects.The first innovation is the development of a virtual displacement difference method for determining the location of damage in the shear structure.The second is to improve the existing frequency sensitivity equation to calculate the damage degree without constructing the finite elementmodel.Thismethod has been verified on a numerical example of a 22-story shear frame structure and an experimental example of a three-story steel shear structure.Based on numerical analysis and experimental data validation,it is shown that this method only needs to use the low-order modes of structural vibration to diagnose the defect location and damage degree,and does not require finite element modeling.The proposed method should be a very simple and practical defect diagnosis technique in engineering practice.
基金Supported by the High Technology Research and Development Programme of China （No. 2007AA04Z433） and the National Natural Science Foundation of China （No. 50635010）.
文摘An adaptive morphological impulses extraction method （AMIE） for bearing fault diagnosis is pro- posed. This method uses the morphological closing operation with a flat structuring element （SE） to extract impulsive features from vibration signals with strong background noise. To optimize the flat SE, firstly, a theoretical study is carried out to investigate the effects of the length of the flat SE. Then, based on the theoretical findings, an adaptive algorithm for the flat SE optimization is proposed. The AMIE method is tested by the simulated signal and bearing vibration signals. The test results show that this method is effective and robust in extracting impulsive features.
基金Supported by National Basic Research Program of China （973 Program） （2009CB320602）, National Natural Science Foundation of China （60721003, 60736026）, and Changjiang Professorship by Ministry of Education of P. R. China
基金supported by National Natural Science Foundation of China (No. 61763037)Inner Mongolia Autonomous Region Natural Science Foundation of China(No. 2019LH06007)Science and Technology Plan Project of Inner Mongolia (No. 2019,2020GG028)。
文摘Working conditions of rolling bearings of wind turbine generators are complicated, and their vibration signals often show non-linear and non-stationary characteristics. In order to improve the efficiency of feature extraction of wind turbine rolling bearings and to strengthen the feature information, a new structural element and an adaptive algorithm based on the peak energy are proposed,which are combined with spectral correlation analysis to form a fault diagnosis algorithm for wind turbine rolling bearings. The proposed method firstly addresses the problem of impulsive signal omissions that are prone to occur in the process of fault feature extraction of traditional structural elements and proposes a "W" structural element to capture more characteristic information. Then, the proposed method selects the scale of multi-scale mathematical morphology, aiming at the problem of multi-scale mathematical morphology scale selection and structural element expansion law. An adaptive algorithm based on peak energy is proposed to carry out morphological scale selection and structural element expansion by improving the computing efficiency and enhancing the feature extraction effect.Finally, the proposed method performs spectral correlation analysis in the frequency domain for an unknown signal of the extracted feature and identifies the fault based on the correlation coefficient. The method is verified by numerical examples using experimental rig bearing data and actual wind field acquisition data and compared with traditional triangular and flat structural elements. The experimental results show that the new structural elements can more effectively extract the pulses in the signal and reduce noise interference,and the fault-diagnosis algorithm can accurately identify the fault category and improve the reliability of the results.
文摘Recently, a new type of IMM （interacting multiple model） method was introduced based on the relatively new SVSF （smooth variable structure filter）, and is referred to as the IMM-SVSF. The SVSF is a type of sliding mode estimator that is formulated in a predictor-corrector fashion. This strategy keeps the estimated state bounded within a region of the true state trajectory, thus creating a stable and robust estimation process. The IMM method may be utilized for fault detection and diagnosis, and is classified as a model-based method. In this paper, for the purposes of fault detection, the IMM-SVSF is applied through simulation on a simple battery system which is modeled from a hybrid electric vehicle.
文摘针对于框架结构的使用环境恶劣,同时常常伴随着大量的噪声,在使用普通的一维卷积神经网络对框架结构进行故障诊断时,存在无法做出有效故障诊断的问题。本研究在一种抗噪声能力较强的卷积神经网络中加入Inception模块,提出了一种识别率和抗噪声能力更高的卷积神经网络—BICNN(Convolution Neural Network based on Inception),并用BICNN卷积神经网络基于数据驱动的方式,对楼体框架模型进行了集成故障诊断研究。集成诊断结果表明BICNN具有更高的识别率和较强的抗噪声能力,而且在训练步数较少的情况下振荡次数少收敛情况良好。因此采取本研究所提出的方法,对框架结构进行故障诊断时具有高诊断率和稳定性,为维护框架结构的稳定运行具有重大安全意义。
