Multiple channels (multivariate) signals

2011 ◽  
pp. 95-120
Keyword(s):  
Multiple Channels ◽  
Multivariate Signals

Multiple Channels (Multivariate) Signals

2021 ◽  
pp. 103-136
Author(s):  
Katarzyna J. Blinowska ◽  
Jarosław Żygierewicz
Keyword(s):  
Multiple Channels ◽  
Multivariate Signals

An MEWMA-based segmental multivariate hidden Markov model for degradation assessment and prediction

2021 ◽  
pp. 1748006X2199052
Author(s):  
Yaping Li ◽  
Enrico Zio ◽  
Ershun Pan
Keyword(s):  
Markov Models ◽  
Hidden Markov ◽  
Average Run Length ◽  
Moving Average ◽  
Model Parameters ◽  
Current Time ◽  
Multivariate Processes ◽  
Industrial Systems ◽  
Hidden States ◽  
Multivariate Signals

Degradation is an unavoidable phenomenon in industrial systems. Hidden Markov models (HMMs) have been used for degradation modeling. In particular, segmental HMMs have been developed to model the explicit relationship between degradation signals and hidden states. However, existing segmental HMMs deal only with univariate cases, whereas in real systems, signals from various sensors are collected simultaneously, which makes it necessary to adapt the segmental HMMs to deal with multivariate processes. Also, to make full use of the information from the sensors, it is important to differentiate stable signals from deteriorating ones, but there is no good way for this, especially in multivariate processes. In this paper, the multivariate exponentially weighted moving average (MEWMA) control chart is employed to identify deteriorating multivariate signals. Specifically, the MEWMA statistic is used as a comprehensive indicator for differentiating multivariate observations. Likelihood Maximization is used to estimate the model parameters. To avoid underflow, the forward and backward probabilities are normalized. In order to assess degradation, joint probabilities are defined and derived. Further, the occurrence probability of each degradation state at the current time, as well as in the future, is derived. The Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset of NASA is employed for comparative analysis. In terms of degradation assessment and prediction, the proposed model performs very well in general. By sensitivity analysis, we show that in order to improve further the performance of the method, the weight of the chart should be set relatively small, whereas the method is not sensitive to the change of the in-control average run length (ARL).

Hydrodynamics and Mixer-Induced Bubble Formation in Micro Bubble Columns with Single and Multiple-Channels

2006 ◽  
Vol 29 (9) ◽  
pp. 1015-1026 ◽  
Author(s):  
V. Haverkamp ◽  
V. Hessel ◽  
H. Löwe ◽  
G. Menges ◽  
M. J. F. Warnier ◽  
...  
Keyword(s):  
Bubble Formation ◽  
Bubble Columns ◽  
Multiple Channels ◽  
Micro Bubble
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