A Technique for Estimating Linear Parameters Using Nonlinear Restoring Force Extraction in the Absence of an Input Measurement

2005 ◽  
Vol 127 (5) ◽  
pp. 483-492 ◽  
Author(s):  
Muhammad Haroon ◽  
Douglas E. Adams ◽  
Yiu Wah Luk
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Restoring Force ◽  
Nonlinear Parameters ◽  
System Parameters ◽  
System Data ◽  
Vehicle Suspension System ◽  
Two Stages ◽  
Parameter Estimation Techniques

Conventional nonlinear system identification procedures estimate the system parameters in two stages. First, the nominally linear system parameters are estimated by exciting the system at an amplitude (usually low) where the behavior is nominally linear. Second, the nominally linear parameters are used to estimate the nonlinear parameters of the system at other arbitrary amplitudes. This approach is not suitable for many mechanical systems, which are not nominally linear over a broad frequency range for any operating amplitude. A method for nonlinear system identification, in the absence of an input measurement, is presented that uses information about the nonlinear elements of the system to estimate the underlying linear parameters. Restoring force, boundary perturbation, and direct parameter estimation techniques are combined to develop this approach. The approach is applied to experimental tire-vehicle suspension system data.

A Technique for Estimating Linear Parameters of an Automotive Suspension System Using Nonlinear Restoring Force Extraction

2004 ◽  
Author(s):  
Muhammad Haroon ◽  
Douglas E. Adams ◽  
Yiu Wah Luk
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Suspension System ◽  
Restoring Force ◽  
Nonlinear Parameters ◽  
Automotive Suspension ◽  
Vehicle Suspension System ◽  
Direction Information ◽  
Parameter Estimation Techniques

Conventional nonlinear system identification procedures assume that the system behavior is nominally linear at a specific amplitude (usually low). The nominally linear parameters are then estimated at that particular amplitude and used to estimate the nonlinear parameters of the system. Many mechanical systems are not nominally linear over a broad frequency range for any operating amplitude. A new method for nonlinear system identification, in the absence of an input measurement, is presented that works in the opposite direction. Information about the nonlinear elements of the system is used to estimate the underlying linear parameters. Restoring force, boundary perturbation and direct parameter estimation techniques are combined to develop this approach. The approach is applied to data from an experimental tire-vehicle suspension system.

A Time and Frequency Domain Approach for Identifying Non-Linear Automotive Suspension System Models in the Absence of an Input Measurement

2004 ◽  
Author(s):  
Muhammad Haroon ◽  
Douglas E. Adams ◽  
Yiu Wah Luk ◽  
Aldo A. Ferri
Keyword(s):  
System Identification ◽  
Frequency Domain ◽  
Time Domain ◽  
Nonlinear System Identification ◽  
Mechanical Systems ◽  
Suspension System ◽  
Restoring Force ◽  
Automotive Suspension ◽  
The Time Domain ◽  
Vehicle Suspension System

The inputs to many ‘real’ mechanical systems are not readily measurable. For example, the input to the tire patch of the tires of automotive road vehicles is neither measurable nor easy to estimate. As conventional system identification procedures require input measurements or at least estimates of the inputs, a new approach for nonlinear system identification of mechanical systems, in the absence of an input measurement, is presented here. This approach uses a combination of time domain (Restoring Force) and frequency domain (Nonlinear Identification through Feedback of the Outputs (NIFO)) techniques. The time domain is used to characterize the nonlinearities in the system and the observed nonlinear characteristics are used in the frequency domain to build a model of the system. The method is applied to experimental tire-vehicle suspension system data.

Nonlinear Model for Sub- and Superharmonic Motions of a MDOF Moored Structure, Part 2—Sensitivity Analysis and Comparison

2005 ◽  
Vol 127 (4) ◽  
pp. 291-299
Author(s):  
S. C. S. Yim ◽  
S. Raman ◽  
P. A. Palo
Keyword(s):  
Sensitivity Analysis ◽  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Detailed Comparison ◽  
Identification Procedure ◽  
System Parameters ◽  
Nonlinear Responses ◽  
Identification Technique ◽  
Single Set

The nonlinear R-MI/SO system identification procedure and the parameters of the MDOF system identified in Part 1 are examined in detail in this paper. A parametric study is conducted and the results are presented on the sensitivity of the system parameters for two key nonlinear responses—subharmonic and superharmonic resonances. The parameters are compared to determine the appropriateness of using a single set of system parameters for both response regions. A detailed comparison of the MDOF and the corresponding SDOF system results is performed. The knowledge gained from the SDOF and MDOF studies on the applicability of the R-MISO technique for the system identification of MDOF submerged moored structures is discussed. The results show that the MDOF extension of the R-MI/SO nonlinear system identification technique works well; the resulting system parameters are relatively constant and can be applied to the both the sub- and superharmonic regions.

Nonlinear System Identification Technique for a Base-Excited Structure Based on Modal Space Formulation

2016 ◽  
Vol 11 (6) ◽  
Author(s):  
Sushil Doranga ◽  
Christine Q. Wu
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Degrees Of Freedom ◽  
Nonlinear System Identification ◽  
Model Space ◽  
Least Square ◽  
Closed Form Expression ◽  
Restoring Force ◽  
Nonlinear Restoring Force ◽  
Modal Space

Most of the nonlinear system identification techniques described in the existing literature required force and response information at all excitation degrees-of-freedom (DOFs). For cases, where the excitation comes from base motion, those methods cannot be applied as it is not feasible to obtain the measurements of motion at all DOFs from an experiment. The objective of this research is to develop the methodology for the nonlinear system identification of continuous, multimode, and lightly damped systems, where the excitation comes from the moving base. For this purpose, the closed-form expression for the equivalent force also known as the pseudo force from the measured data for the base-excited structure is developed. A hybrid model space is developed to find out the nonlinear restoring force at the nonlinear DOFs. Once the nonlinear restoring force is obtained, the nonlinear parameters are extracted using “multilinear least square regression” in a modal space. A modal space is chosen to express the direct and cross-coupling nonlinearities. Using a cantilever beam as an example, the proposed methodology is demonstrated, where the experimental setup, testing procedure, data acquisition, and data processing are presented. The example shows that the method proposed here is systematic and constructive for nonlinear parameter identification for base-excited structure.

Modeling and Identification of a Nonlinear SDOF Moored Structure, Part 1—Hydrodynamic Models and Algorithms

2004 ◽  
Vol 126 (2) ◽  
pp. 175-182 ◽  
Author(s):  
S. Narayanan ◽  
S. C. S. Yim
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Small Body ◽  
Nonlinear System Identification ◽  
Ocean Waves ◽  
System Response ◽  
Restoring Force ◽  
Nonlinear Structure ◽  
Submerged Sphere ◽  
Highly Nonlinear

The highly nonlinear responses of compliant ocean structures characterized by a large-geometry restoring force and coupled fluid-structure interaction excitation are of great interest to ocean and coastal engineers. Practical modeling, parameter identification, and incorporation of the inherent nonlinear dynamics in the design of these systems are essential and challenging. The general approach of a nonlinear system technique using very simple models has been presented in the literature by Bendat. In Part 1 of this two-part study, two specific nonlinear small-body hydrodynamic Morison type formulations: (A) with a relative-velocity (RV) model, and (B) with an independent flow-field (IFF) model, are formulated. Their associated nonlinear system-identification algorithms based on the reverse multiple-input/single-output (R-MI/SO) system-identification technique: (A.1) nonlinear-structure linearly damped, and (A.2) nonlinear-structure coupled hydrodynamically damped for the RV model, and (B.1) nonlinear-structure nonlinearly damped for the IFF model, are developed for a specific experimental submerged-sphere mooring system under ocean waves exhibiting such highly nonlinear response behaviors. In Part 2, using the measured input wave and output system response data, the algorithms derived based on the MI/SO linear analysis of the reverse dynamic systems are applied to identify the properties of the highly nonlinear system. Practical issues on the application of the R-MI/SO technique based on limited available experimental data are addressed.

Highly Sensitive Nonlinear Identification To Track Early Fatigue Signs in Flexible Structures

2021 ◽  
pp. 1-33
Author(s):  
Ed Habtour ◽  
Dario Di Maio ◽  
Thijs Masmeijer ◽  
Laura Cordova Gonzalez ◽  
Tiedo Tinga
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Fatigue Damage ◽  
Nonlinear System Identification ◽  
Flexible Structures ◽  
Nonlinear Frequency ◽  
Nonlinear Parameters ◽  
Geometric Stiffness ◽  
Nonlinear Frequency Response ◽  
Aluminum 7075

Abstract This study describes a physics-based and data-driven nonlinear system identification approach for detecting early fatigue damage due to vibratory loads. The approach also allows for tracking the evolution of damage in real-time. Nonlinear parameters such as geometric stiffness, cubic damping and phase angle shift can be estimated as a function of fatigue cycles, which are demonstrated experimentally using flexible aluminum 7075-T6 structures exposed to vibration. Nonlinear system identification is utilized to create and update nonlinear frequency response functions, backbone curves and phase traces to visualize and estimate the structural health. Findings show that the dynamic phase is more sensitive to the evolution of early fatigue damage than nonlinear parameters such as the geometric stiffness and cubic damping parameters. A modifed Carrella-Ewins method is introduced to calculate the backbone from the nonlinear signal response, which is in good agreement with the numerical and harmonic balance results. The phase tracing method is presented, which appears to detect damage after approximately 40% of fatigue life, while the geometric stiffness and cubic damping parameters are capable of detecting fatigue damage after approximately 50% of the life-cycle.

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