scholarly journals Quantifying Information without Entropy: Identifying Intermittent Disturbances in Dynamical Systems

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1199
Author(s):  
Angela Montoya ◽  
Ed Habtour ◽  
Fernando Moreu
Keyword(s):  
Signal Transmission ◽  
Permutation Entropy ◽  
System Response ◽  
Response Signal ◽  
Response Data ◽  
Numerical Studies ◽  
Impulse Function ◽  
Relative Information Content ◽  
Driving Signal ◽  
Small Disturbances

A system’s response to disturbances in an internal or external driving signal can be characterized as performing an implicit computation, where the dynamics of the system are a manifestation of its new state holding some memory about those disturbances. Identifying small disturbances in the response signal requires detailed information about the dynamics of the inputs, which can be challenging. This paper presents a new method called the Information Impulse Function (IIF) for detecting and time-localizing small disturbances in system response data. The novelty of IIF is its ability to measure relative information content without using Boltzmann’s equation by modeling signal transmission as a series of dissipative steps. Since a detailed expression of the informational structure in the signal is achieved with IIF, it is ideal for detecting disturbances in the response signal, i.e., the system dynamics. Those findings are based on numerical studies of the topological structure of the dynamics of a nonlinear system due to perturbated driving signals. The IIF is compared to both the Permutation entropy and Shannon entropy to demonstrate its entropy-like relationship with system state and its degree of sensitivity to perturbations in a driving signal.

scholarly journals Compacting the description of a time-dependent multivariable system and its multivariable driver by reducing the state vectors to aggregate scalars: the Earth's solar-wind-driven magnetosphere

2019 ◽  
Vol 26 (4) ◽  
pp. 429-443 ◽  
Author(s):  
Joseph E. Borovsky ◽  
Adnane Osmane
Keyword(s):  
Solar Wind ◽  
State Vector ◽  
Complexity Analysis ◽  
Low Noise ◽  
Time Dependent ◽  
Permutation Entropy ◽  
System Response ◽  
System A ◽  
Driven System ◽  
Time Dependent System

Abstract. Using the solar-wind-driven magnetosphere–ionosphere–thermosphere system, a methodology is developed to reduce a state-vector description of a time-dependent driven system to a composite scalar picture of the activity in the system. The technique uses canonical correlation analysis to reduce the time-dependent system and driver state vectors to time-dependent system and driver scalars, with the scalars describing the response in the system that is most-closely related to the driver. This reduced description has advantages: low noise, high prediction efficiency, linearity in the described system response to the driver, and compactness. The methodology identifies independent modes of reaction of a system to its driver. The analysis of the magnetospheric system is demonstrated. Using autocorrelation analysis, Jensen–Shannon complexity analysis, and permutation-entropy analysis the properties of the derived aggregate scalars are assessed and a new mode of reaction of the magnetosphere to the solar wind is found. This state-vector-reduction technique may be useful for other multivariable systems driven by multiple inputs.

Stochastic Linearization by Data Dependent Systems

1977 ◽  
Vol 99 (4) ◽  
pp. 221-226 ◽  
Author(s):  
S. M. Pandit
Keyword(s):  
Furnace Operation ◽  
System Response ◽  
Ticket Sales ◽  
Response Data ◽  
Machine Tool Chatter ◽  
Chatter Vibrations ◽  
Stochastic Linearization ◽  
Airline Passenger ◽  
Prediction And Control ◽  
And Control

The paper presents and illustrates a method of stochastic linearization of nonlinear systems. The system response to white noise excitation is modeled by a differential equation, which provides the necessary transfer function. The linearization is optimal in the mean squared sense within the statistical limits imposed by the response. Since the linearization is accomplished purely from the response data, governing equations of the system need not be known. An application to machine tool chatter vibrations illustrates stability assessment and modal analysis. The ease with which optimal prediction and control equations can be derived and implemented is shown by an application to blast furnace operation. Detection and verification of limit cycles are illustrated by a model for airline passenger ticket sales data.

Calculating scaling function coefficients from system response data for new discrete wavelet families

2012 ◽  
Vol 27 ◽  
pp. 362-369 ◽  
Author(s):  
Andrew J. Dick ◽  
Quan M. Phan ◽  
Jason R. Foley ◽  
Pol D. Spanos
Keyword(s):  
Scaling Function ◽  
System Response ◽  
Discrete Wavelet ◽  
Response Data

scholarly journals PID-Based Design of DC Motor Speed Control

2021 ◽  
Vol 2 (1) ◽  
pp. 7
Author(s):  
Irfan Irhamni ◽  
Riries Rulaningtyas ◽  
Riky Tri Yunardi
Keyword(s):  
Transient Response ◽  
Pid Controller ◽  
Speed Control ◽  
Dc Motor ◽  
System Response ◽  
Motor Speed ◽  
Proportional Integral ◽  
Response Data ◽  
Dc Motors

DC motor is an easy-to-apply motor but has inconsistent speed due to the existing load. PID (Proportional Integral Differential) is one of the standard controllers of DC motors. This study aimed to know the PID controller's performance in controlling the speed of a DC motor. The results showed that the PID controller could improve the error and transient response of the system response generated from DC motor speed control. Based on the obtained system response data from testing and tuning the PID parameters in controlling the speed of a DC motor, the PID controller parameters can affect the rate of a DC motor on the setpoint of 500, 1000, 1500: Kp = 0.05, Ki = 0.0198, Kd = 0.05.

scholarly journals Forensic Engineering Analysis Of Trailer Sway Accidents

2008 ◽  
Vol 25 (1) ◽  
Author(s):  
Mark A.M. Ezra ◽  
Landiss Danel J.
Keyword(s):  
Data Reduction ◽  
Damping Ratio ◽  
Experimental System ◽  
Second Order ◽  
Coupled Systems ◽  
Order System ◽  
System Response ◽  
Statistical Validation ◽  
Response Data ◽  
Data Reduction Method

This Paper Describes A Method For The Reduction Of System Response Data For Second Order Electrical Or Mechanical Systems When That Data Is Available Only In Graphical Format. The Method Of Data Reduction Described Allows Quantitative Evaluation Of Generally Accepted Second Order System Parameters Such As: System Time Constant, Damped Natural Frequency, Damping Ratio, And Exponential Decay Time. The Discussion Includes The Application Of The Described Graphical Technique To Experimental System Response Data Of Coupled Systems, But Whose Experimental Response Approximates That Of An Isolated Second Order System. A Practical Application Of The Described Data Reduction Method Is Covered In Detail. The Described Technique Is Applied To The Analysis Of Data Obtained Experimentally For The Response Of A Tow Vehicle And Trailer System To A Standardized Steering Disturbance. Finally, The Statistical Validation For Experimental System Response Data And The Results Obtained From The Analysis Of Such Data, Using The Described Graphical Method, Is Discussed.

Identification of Multiple Damages in a Structure Using Embedded Sensitivity Functions and Optimization Techniques

2011 ◽  
Author(s):  
Chulho Yang ◽  
Douglas E. Adams
Keyword(s):  
Optimization Techniques ◽  
System Response ◽  
Story Structure ◽  
Response Data ◽  
Sensitivity Functions ◽  
Suggested Technique ◽  
Taylor Series Expansions ◽  
Measured Frequency Response ◽  
The Difference ◽  
Two Degree Of Freedom

A new method for identifying multiple damages in a structure using embedded sensitivity functions and optimization algorithms is presented in this work. Optimization techniques are used to minimize the difference between the measured frequency response functions from a damaged structure and the predicted FRFs from the baseline structure. The predicted FRF functions are calculated directly from the undamaged system response data using the embedded sensitivity functions and their Taylor series expansions. The optimal damage parameters are identified in engineering units as changes in stiffness, damping, or mass through the optimization process for minimizing the difference between those two FRFs. The method is applied to a two degree of freedom analytical model to determine the accuracy of the diagnostic results. Finite element analyses are then conducted on a three-story structure with damages in the form of stiffness and mass perturbations to demonstrate the applicability of this method to more complicated structural systems. It is shown that the suggested technique can detect and quantify multiple damages in a structure with high numerical accuracy in the level of the estimated damages.

On the Impact Intensity of Vibrating Axially Moving Roller Chains

1992 ◽  
Vol 114 (3) ◽  
pp. 397-403 ◽  
Author(s):  
K. W. Wang ◽  
S. P. Liu ◽  
S. I. Hayek ◽  
F. H. K. Chen
Keyword(s):  
Chain Structure ◽  
Momentum Balance ◽  
System Response ◽  
Roller Chain ◽  
Impulse Function ◽  
Static Approach ◽  
Axially Moving ◽  
Local Impacts ◽  
The Impact ◽  
Chain Drives

Experimental observation has shown that the most significant noise source in roller chain drives is from the impacts between the chain and the sprocket during their meshing process. Despite its importance, studies have not been made to thoroughly analyze the chain/sprocket impact dynamics and their interaction with the vibrating, axially moving chain structure. This paper presents a novel analysis which integrates the local meshing phenomena with the global system. An axially moving chain interacting with local impacts has been modelled and the momentum balance method is employed to derive the impulse function. A study is carried out to quantify the intensity of subsequent impacts. It is found that the impact intensity is significantly affected by the vibration characteristics and response of the moving chain, and vice versa. The classical quasi-static approach will create errors in predicting the impulse magnitude and system response. Meshing frequencies that will cause maximum and minimum impulses are analytically predicted. This fundamental investigation provides new insight into roller chain dynamics, which is an essential step toward the design of quiet chain drives.

scholarly journals Pressure drop test as a hydroinformatic tool for preliminary network topology validation

2018 ◽  
Vol 19 (2) ◽  
pp. 502-510
Author(s):  
M. Milašinović ◽  
D. Prodanović ◽  
M. Stanić
Keyword(s):  
Pressure Drop ◽  
Network Topology ◽  
Distribution Systems ◽  
Water Distribution ◽  
Pressure Change ◽  
Drop Test ◽  
System Response ◽  
Response Data ◽  
Reliable Model ◽  
Validation Tests

Abstract Usage of the appropriate model of water distribution systems (WDS) enables easier everyday operations and management decisions. Creating a reliable model of WDS requires a large amount of system response data for different case scenarios. Commonly used software for creating models of WDS is EpaNet. Ongoing processes in WDS, such as pipe bursts, permanently closed valves which are not registered in the data base and other inconsistencies will change WDS network topology, so WDS validation tests are to be applied from time to time. This paper presents the WDS network topology validation test conducted on one district metered area of Belgrade with two inflows. The pressure drop test combined with genetic algorithm and ant colony optimization are simple hydroinformatic tools available for network topology validation. The system's reaction under a pressure change during the isolation test was measured at two observation points. Obtained results are then compared with assumed WDS topology using 55 potential locations of inconsistencies in the EpaNet model. This step is repeated until a good enough match between results from the real system and the created model's version is obtained. Heuristic optimization algorithms are used for speeding up the process of finding a satisfactory match (unknown locations of inconsistencies) by minimizing or maximizing the defined criteria function.

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