A maximum correntropy criterion based recursive method for output-only modal identification of time-varying structures under non-Gaussian impulsive noise

2019 ◽  
Vol 448 ◽  
pp. 178-194
Author(s):  
Lei Yu ◽  
Li Liu ◽  
Zhenjiang Yue ◽  
Jie Kang
Keyword(s):  
Impulsive Noise ◽  
Modal Identification ◽  
Recursive Method ◽  
Time Varying ◽  
Maximum Correntropy Criterion ◽  
Non Gaussian ◽  
Output Only

scholarly journals A Simple Condition Monitoring Method for Gearboxes Operating in Impulsive Environments

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2115 ◽  
Author(s):  
Stephan Schmidt ◽  
Radoslaw Zimroz ◽  
Fakher Chaari ◽  
P. Stephan Heyns ◽  
Mohamed Haddar
Keyword(s):  
Impulsive Noise ◽  
Operating Conditions ◽  
Fault Identification ◽  
Time Varying ◽  
Monitoring Method ◽  
Condition Indicators ◽  
Diagnosis And Prognosis ◽  
Conventional Condition ◽  
Non Gaussian ◽  
Effective Diagnosis

Reliable condition indicators are necessary to perform effective diagnosis and prognosis. However, the vibration signals are often corrupted with non-Gaussian noise and rotating machines may operate under time-varying operating conditions. This impedes the application of conventional condition indicators. The synchronous average of the squared envelope is a relatively simple yet effective method to perform fault detection, fault identification and fault trending under constant and time-varying operating conditions. However, its performance is impeded by the presence of impulsive signal components attributed to impulsive noise or the presence of other damage modes in the machine. In this work, it is proposed that the synchronous median of the squared envelope should be used instead of the synchronous average of the squared envelope for gearbox fault diagnosis. It is shown on numerical and experimental datasets that the synchronous median is more robust to the presence of impulsive signal components and is therefore more reliable for estimating the condition of specific machine components.

scholarly journals Diffusion Correntropy Subband Adaptive Filtering (SAF) Algorithm over Distributed Smart Dust Networks

Symmetry ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1335 ◽  
Author(s):  
Ying Guo ◽  
Jingjing Li ◽  
Yingsong Li
Keyword(s):  
Adaptive Filtering ◽  
Impulsive Noise ◽  
Gaussian Kernel ◽  
Sparse System ◽  
Smart Dust ◽  
Input Signals ◽  
Maximum Correntropy Criterion ◽  
Non Gaussian ◽  
Weighted Values ◽  
Dynamic Gain

The diffusion subband adaptive filtering (DSAF) algorithm has attracted much attention in recent years due to its decorrelation ability for colored input signals. In this paper, a modified DSAF algorithm using the symmetry maximum correntropy criterion (MCC) with individual weighting factors is proposed and discussed to combat impulsive noise, which is denoted as the MCC-DSAF algorithm. During the iterations, the negative exponent in the Gaussian kernel of the MCC-DSAF eliminates the interference of outliers to provide a robust performance in non-Gaussian noise environments. Moreover, in order to enhance the convergence for sparse system identifications, a variant of MCC-DSAF named as improved proportionate MCC-DSAF (MCC-IPDSAF) is presented and investigated, which provides a dynamic gain assignment matrix in the MCC-DSAF to adjust the weighted values of each coefficient. Simulation results verify that the newly presented MCC-DSAF and MCC-IPDSAF algorithms are superior to the popular DSAF algorithms.

A Hierarchical Safety Control Strategy for Exoskeleton Robot Based on Maximum Correntropy Kalman Filter and Bounding Box

Robotica ◽  
2019 ◽  
Vol 37 (12) ◽  
pp. 2165-2175 ◽  
Author(s):  
Yang Mo ◽  
Zhenzi Song ◽  
Hui Li ◽  
Zhihong Jiang
Keyword(s):  
Kalman Filter ◽  
Control Strategy ◽  
Impulsive Noise ◽  
Safety Control ◽  
Exoskeleton Robot ◽  
Bounding Box ◽  
Safe Control ◽  
Maximum Correntropy Criterion ◽  
Non Gaussian ◽  
Relationship Of

SummaryExoskeleton robots have been widely used in many fields at present. When wearing the exoskeleton to operate, the wearer may be unconscious of the position of exoskeleton or affected by the surrounding environment, causing collision between two arms of exoskeleton or between arms and environment. The collision may result in the exoskeleton destroyed or even the wearer injured. This paper proposes a hierarchical safety control strategy for exoskeleton robots based on maximum correntropy Kalman filter and bounding box to ensure safe operation. Accurate joint angle prediction can be obtained by filtering out non-Gaussian impulsive noise using maximum correntropy criterion as evaluation criterion. Relative position relationship of the arms can be derived based on bounding box to realize hierarchical safe control. Enough experiments have been carried out, and the results validated the feasibility of the proposed method.

scholarly journals Robust Weighted l1,2 Norm Filtering in Passive Radar Systems

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3270 ◽  
Author(s):  
Baris Satar ◽  
Gokhan Soysal ◽  
Xue Jiang ◽  
Murat Efe ◽  
Thiagalingam Kirubarajan
Keyword(s):  
Target Detection ◽  
Impulsive Noise ◽  
Simulated Data ◽  
Real Data ◽  
Detection Performance ◽  
Passive Radar ◽  
Mobile Telecommunication ◽  
Telecommunication System ◽  
Conventional Methods ◽  
Non Gaussian

Conventional methods such as matched filtering, fractional lower order statistics cross ambiguity function, and recent methods such as compressed sensing and track-before-detect are used for target detection by passive radars. Target detection using these algorithms usually assumes that the background noise is Gaussian. However, non-Gaussian impulsive noise is inherent in real world radar problems. In this paper, a new optimization based algorithm that uses weighted l 1 and l 2 norms is proposed as an alternative to the existing algorithms whose performance degrades in the presence of impulsive noise. To determine the weights of these norms, the parameter that quantifies the impulsiveness level of the noise is estimated. In the proposed algorithm, the aim is to increase the target detection performance of a universal mobile telecommunication system (UMTS) based passive radars by facilitating higher resolution with better suppression of the sidelobes in both range and Doppler. The results obtained from both simulated data with α stable distribution, and real data recorded by a UMTS based passive radar platform are presented to demonstrate the superiority of the proposed algorithm. The results show that the proposed algorithm provides more robust and accurate detection performance for noise models with different impulsiveness levels compared to the conventional methods.

Maximum Correntropy Criterion Constrained Kalman Filter

2017 ◽  
Author(s):  
Seyed Fakoorian ◽  
Mahmoud Moosavi ◽  
Reza Izanloo ◽  
Vahid Azimi ◽  
Dan Simon
Keyword(s):  
Kalman Filter ◽  
Gaussian Noise ◽  
State Constraints ◽  
Statistical Information ◽  
Second Order ◽  
System Model ◽  
Error Covariance ◽  
Inequality State ◽  
Maximum Correntropy Criterion ◽  
Non Gaussian

Non-Gaussian noise may degrade the performance of the Kalman filter because the Kalman filter uses only second-order statistical information, so it is not optimal in non-Gaussian noise environments. Also, many systems include equality or inequality state constraints that are not directly included in the system model, and thus are not incorporated in the Kalman filter. To address these combined issues, we propose a robust Kalman-type filter in the presence of non-Gaussian noise that uses information from state constraints. The proposed filter, called the maximum correntropy criterion constrained Kalman filter (MCC-CKF), uses a correntropy metric to quantify not only second-order information but also higher-order moments of the non-Gaussian process and measurement noise, and also enforces constraints on the state estimates. We analytically prove that our newly derived MCC-CKF is an unbiased estimator and has a smaller error covariance than the standard Kalman filter under certain conditions. Simulation results show the superiority of the MCC-CKF compared with other estimators when the system measurement is disturbed by non-Gaussian noise and when the states are constrained.

scholarly journals An Extended Time Series Algorithm for Modal Identification from Nonstationary Ambient Response Data Only

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Chang-Sheng Lin ◽  
Dar-Yun Chiang ◽  
Tse-Chuan Tseng
Keyword(s):  
Time Series ◽  
Arma Model ◽  
Identification Problem ◽  
Nonstationary Time Series ◽  
Modal Identification ◽  
Ambient Vibration ◽  
Time Varying ◽  
Varying Parameters ◽  
Response Data ◽  
Time Varying Parameters

Modal Identification is considered from response data of structural systems under nonstationary ambient vibration. The conventional autoregressive moving average (ARMA) algorithm is applicable to perform modal identification, however, only for stationary-process vibration. The ergodicity postulate which has been conventionally employed for stationary processes is no longer valid in the case of nonstationary analysis. The objective of this paper is therefore to develop modal-identification techniques based on the nonstationary time series for linear systems subjected to nonstationary ambient excitation. Nonstationary ARMA model with time-varying parameters is considered because of its capability of resolving general nonstationary problems. The parameters of moving averaging (MA) model in the nonstationary time-series algorithm are treated as functions of time and may be represented by a linear combination of base functions and therefore can be used to solve the identification problem of time-varying parameters. Numerical simulations confirm the validity of the proposed modal-identification method from nonstationary ambient response data.

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