scholarly journals Sensitivity-Based Adaptive SRUKF for State, Parameter, and Covariance Estimation on Mechatronic Systems

2018 ◽  
Author(s):  
Mauro Hernán Riva ◽  
Mark Wielitzka ◽  
Tobias Ortmaier
Keyword(s):  
State Parameter ◽  
Covariance Estimation ◽  
Mechatronic Systems

Sensitivity-based adaptive SRUKF for online state, parameter, and process covariance estimation

2017 ◽  
Author(s):  
Mauro Hernan Riva ◽  
Mark Wielitzka ◽  
Tobias Ortmaier
Keyword(s):  
State Parameter ◽  
Covariance Estimation

Structured Robust Covariance Estimation

2015 ◽  
Author(s):  
Ami Wiesel ◽  
Teng Zhang
Keyword(s):  
Covariance Estimation

Semi-Blind Channel Covariance Estimation for MIMO Precoding

2011 ◽  
Vol E94-A (2) ◽  
pp. 833-837
Author(s):  
Mohd Hairi HALMI ◽  
Mohd Yusoff ALIAS ◽  
Teong Chee CHUAH
Keyword(s):  
Covariance Estimation ◽  
Blind Channel ◽  
Mimo Precoding

The Brownian bridge E-M algorithm for covariance estimation with missing data

1998 ◽  
Vol 2 (2) ◽  
pp. 75-100 ◽  
Author(s):  
William Morokoff
Keyword(s):  
Missing Data ◽  
Brownian Bridge ◽  
Covariance Estimation

Multi-Horizon Mean-Covariance Estimation for Serial Correlated Returns

2019 ◽  
Author(s):  
Zhuanxin Ding
Keyword(s):  
Covariance Estimation

scholarly journals Covariance estimation via fiducial inference

2021 ◽  
pp. 1-16
Author(s):  
W. Jenny Shi ◽  
Jan Hannig ◽  
Randy C. S. Lai ◽  
Thomas C. M. Lee
Keyword(s):  
Covariance Estimation ◽  
Fiducial Inference

scholarly journals Gradual Wear Diagnosis of Outer-Race Rolling Bearing Faults through Artificial Intelligence Methods and Stray Flux Signals

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1486
Author(s):  
Israel Zamudio-Ramirez ◽  
Roque A. Osornio-Rios ◽  
Jose A. Antonino-Daviu ◽  
Jonathan Cureño-Osornio ◽  
Juan-Jose Saucedo-Dorantes
Keyword(s):  
Kinematic Chain ◽  
Industrial Applications ◽  
Electric Motors ◽  
Feed Forward Neural Network ◽  
Mechatronic Systems ◽  
Linear Discriminant ◽  
Bearing Faults ◽  
Outer Race ◽  
Non Invasive ◽  
Operation Conditions

Electric motors have been widely used as fundamental elements for driving kinematic chains on mechatronic systems, which are very important components for the proper operation of several industrial applications. Although electric motors are very robust and efficient machines, they are prone to suffer from different faults. One of the most frequent causes of failure is due to a degradation on the bearings. This fault has commonly been diagnosed at advanced stages by means of vibration and current signals. Since low-amplitude fault-related signals are typically obtained, the diagnosis of faults at incipient stages turns out to be a challenging task. In this context, it is desired to develop non-invasive techniques able to diagnose bearing faults at early stages, enabling to achieve adequate maintenance actions. This paper presents a non-invasive gradual wear diagnosis method for bearing outer-race faults. The proposal relies on the application of a linear discriminant analysis (LDA) to statistical and Katz’s fractal dimension features obtained from stray flux signals, and then an automatic classification is performed by means of a feed-forward neural network (FFNN). The results obtained demonstrates the effectiveness of the proposed method, which is validated on a kinematic chain (composed by a 0.746 KW induction motor, a belt and pulleys transmission system and an alternator as a load) under several operation conditions: healthy condition, 1 mm, 2 mm, 3 mm, 4 mm, and 5 mm hole diameter on the bearing outer race, and 60 Hz, 50 Hz, 15 Hz and 5 Hz power supply frequencies

scholarly journals Basis Expansions for Functional Snippets

Biometrika ◽  
2020 ◽  
Author(s):  
Zhenhua Lin ◽  
Jane-Ling Wang ◽  
Qixian Zhong
Keyword(s):  
Data Analysis ◽  
Convergence Rate ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Covariance Function ◽  
Covariance Estimation ◽  
Simulation Studies ◽  
Finite Sample ◽  
Covariance Functions ◽  
The Mean

Summary Estimation of mean and covariance functions is fundamental for functional data analysis. While this topic has been studied extensively in the literature, a key assumption is that there are enough data in the domain of interest to estimate both the mean and covariance functions. In this paper, we investigate mean and covariance estimation for functional snippets in which observations from a subject are available only in an interval of length strictly (and often much) shorter than the length of the whole interval of interest. For such a sampling plan, no data is available for direct estimation of the off-diagonal region of the covariance function. We tackle this challenge via a basis representation of the covariance function. The proposed estimator enjoys a convergence rate that is adaptive to the smoothness of the underlying covariance function, and has superior finite-sample performance in simulation studies.

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