Recursive principal component analysis with forgetting factor for operational modal analysis of linear time-varying system

2016 ◽  
Vol 52 (3-4) ◽  
pp. 999-1006 ◽  
Author(s):  
Wei Guan ◽  
Cheng Wang ◽  
D.S. Chen ◽  
Xiangyu Luo ◽  
F.F. Su
Keyword(s):  
Principal Component Analysis ◽  
Modal Analysis ◽  
Linear Time ◽  
Principal Component ◽  
Component Analysis ◽  
Operational Modal Analysis ◽  
Time Varying ◽  
Forgetting Factor

scholarly journals An online and real-time adaptive operational modal parameter identification method based on fog computing in Internet of Things

2020 ◽  
Vol 16 (2) ◽  
pp. 155014772090361 ◽  
Author(s):  
Cheng Wang ◽  
Haiyang Huang ◽  
Jianwei Chen ◽  
Wei Wei ◽  
Tian Wang
Keyword(s):  
Principal Component Analysis ◽  
Internet Of Things ◽  
Modal Analysis ◽  
Real Time ◽  
Fog Computing ◽  
Principal Component ◽  
Component Analysis ◽  
Operational Modal Analysis ◽  
Smart Devices ◽  
Analysis Method

A large number of smart devices make the Internet of Things world smarter. However, currently cloud computing cannot satisfy real-time requirements and fog computing is a promising technique for real-time processing. Operational modal analysis obtains modal parameters that reflect the dynamic properties of the structure from the vibration response signals. In Internet of Things, the operational modal analysis method can be embedded in the smart devices to achieve structural health monitoring and fault detection. In this article, a four-layer framework for combining fog computing and operational modal analysis in Internet of Things is designed. This four-layer framework introduces fog computing to solve tasks that cloud computing cannot handle in real time. Moreover, to reduce the time and space complexity of the operational modal analysis algorithm and support the real-time performance of fog computing, a limited memory eigenvector recursive principal component analysis–based operational modal analysis approach is proposed. In addition, by examining the cumulative percent variance of principal component analysis, this article explains the reasons behind the identified modal order exchange. Finally, the time-varying operational modal identification results from non-stationary random response signals of a cantilever beam whose density changes slowly indicate that the limited memory eigenvector recursive principal component analysis–based operational modal analysis method requires less memory and runtime and has higher stability and identification effect.

scholarly journals A New Online Operational Modal Analysis Method for Vibration Control for Linear Time-Varying Structure

2019 ◽  
Vol 10 (1) ◽  
pp. 48 ◽  
Author(s):  
Cheng Wang ◽  
Haiyang Huang ◽  
Xiongming Lai ◽  
Jianwei Chen
Keyword(s):  
Vibration Control ◽  
Modal Analysis ◽  
Natural Frequencies ◽  
Linear Time ◽  
Vibration Response ◽  
Operational Modal Analysis ◽  
Modal Identification ◽  
Modal Parameters ◽  
Time Varying ◽  
Forgetting Factor

From the viewpoint of vibration control, if the amplitude of the main frequencies of the vibration response can be reduced, the vibration energy of the structure is greatly reduced. Modal parameters, including modal shapes, natural frequencies, and damping ratios, can reflect the dynamics of the structure and can be used to control the vibration. This paper integrates the idea of “forgetting factor weighting” into eigenvector recursive principal component analysis, and then proposes an operational modal analysis (OMA) method that uses eigenvector recursive PCA with a forgetting factor (ERPCAWF). The proposed method can identify the transient natural frequencies and transient modal shapes online and realtime using only nonstationary vibration response signals. The identified modal parameters are also suitable for online, real-time health monitoring and fault diagnosis. Finally, the modal identification results from a three-degree-of-freedom weakly damped linear time-varying structure shows that the ERPCAWF-based OMA method can effectively identify transient modal parameters online using only nonstationary response signals. The results also show that the ERPCAWF-based approach is faster, requires less memory space, and achieves higher identification accuracy and greater stability than autocorrelation matrix recursive PCA with a forgetting factor-based OMA.

scholarly journals Fault detection and diagnosis in water resource recovery facilities using incremental PCA

2020 ◽  
Vol 82 (12) ◽  
pp. 2711-2724 ◽  
Author(s):  
Pezhman Kazemi ◽  
Jaume Giralt ◽  
Christophe Bengoa ◽  
Armin Masoumian ◽  
Jean-Philippe Steyer
Keyword(s):  
Principal Component Analysis ◽  
Missing Values ◽  
Computational Cost ◽  
Principal Component ◽  
Process Variations ◽  
Component Analysis ◽  
Fault Detection And Diagnosis ◽  
Time Varying ◽  
Best Linear Unbiased ◽  
Detection And Diagnosis

Abstract Because of the static nature of conventional principal component analysis (PCA), natural process variations may be interpreted as faults when it is applied to processes with time-varying behavior. In this paper, therefore, we propose a complete adaptive process monitoring framework based on incremental principal component analysis (IPCA). This framework updates the eigenspace by incrementing new data to the PCA at a low computational cost. Moreover, the contribution of variables is recursively provided using complete decomposition contribution (CDC). To impute missing values, the empirical best linear unbiased prediction (EBLUP) method is incorporated into this framework. The effectiveness of this framework is evaluated using benchmark simulation model No. 2 (BSM2). Our simulation results show the ability of the proposed approach to distinguish between time-varying behavior and faulty events while correctly isolating the sensor faults even when these faults are relatively small.

The Fault Diagnosis Model of Beer Fermentation Process Based on Kernel Principal Component Analysis for Constant Value Detection

2014 ◽  
Vol 1030-1032 ◽  
pp. 1822-1827
Author(s):  
Ning Lv ◽  
Guang Yuan Bai ◽  
Lu Qi Yan ◽  
Yuan Jian Fu
Keyword(s):  
Principal Component Analysis ◽  
Computational Complexity ◽  
Fault Detection ◽  
Fermentation Process ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Time Varying ◽  
Beer Fermentation ◽  
Non Linear

In order to overcome the application limitations of principal component analysis fault diagnose model in non-linear time-varying and reduce computational complexity for process monitoring based on non-linear principal component, we introduced kernel transformation theory of nonlinear space to extract data feature extraction and a fault monitoring model based on kernel principal component analysis (KPCA) for constant value detection was proposed. Through the proper selection of kernel function parameter values, the KPCA model can achieve constant value of process fault detection and has lower computational complexity than other non-linear algorithms. The fault detection experiment for beer fermentation process shows that this method is able to detect process faults in a timely manner and has good real-time performance and accuracy in the batch process of slowly time-varying.

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