Softening and melting mechanisms of polyamides interfering with sliding stability under adhesive conditions

The thermal stability of polymers is a main issue when used as friction elements under dry sliding. Cast polyamide grades processed with either natrium or magnesium catalysors are slid on a small-scale and a large-scale test configuration to reveal the effect of softening or degradation on the sliding stability and to investigate possibilities for extrapolation of friction and wear rates between both testing scales. The combination of softening and afterwards transition into the glassy state is detrimental for the sliding stability of natrium catalysed polyamides, characterised by heavy noise during sliding. A transfer film formed under continuous softening also provides high friction. Melting during initial sliding is necessary for stabilisation in both friction and wear, and eventual softening of a molten film near the end of the test then not deteriorates the sliding stability. Softening of magnesium catalysed polyamides is favourable for the formation of a coherent transfer film resulting in more stable sliding than natrium catalysed polyamides. The differences in softening mechanisms of both polyamide grades is correlated to structural changes investigated by thermal analysis and Raman spectroscopy: the γ crystalline structure prevails in magnesium catalysed samples and the α crystalline structure is predominant in natrium catalysed samples. For internal oil lubricated polyamides, a time dependent degradation of the polyamide bulk deteriorates the supply of internal oil lubricant to the sliding interface, resulting in high friction and wear under overload conditions. As the degradation mechanisms during sliding are strongly correlated to the test set-up, extrapolation is only possible for friction in a limited application range, while wear rates cannot be extrapolated.     

Model selection through a statistical analysis of the globalminimum of a weighted nonlinear least squares cost function

This paper presents a model selection algorithm for the identification of parametric models that are linear in the measurements. It is based on the mean and variance expressions of the global minimum of a weighted nonlinear least squares cost function. The method requires the knowledge of the noise covariance matrix but does not assume that the true model belongs to the model set. Unlike the traditional order estimation methods available in literature, the presented technique allows to detect undermodeling. The theory is illustrated by simulations on signal modeling and system identification problems and by one real measurement example     

Blind Maximum-Likelihood Identification of Wiener Systems

This paper is about the identification of discrete-time Wiener systems from output measurements only (blind identification). Assuming that the unobserved input is white Gaussian noise, that the static nonlinearity is invertible, and that the output is observed without errors, a Gaussian maximum-likelihood estimator is constructed. Its asymptotic properties are analyzed and the Cramer-Rao lower bound is calculated. A two-step procedure for generating high-quality initial estimates is presented as well. The paper includes the illustration of the method on a simulation example.     

Errors-in-variables identification of dynamic systems excited by arbitrary non-white input

This work deals with the identification of dynamic systems from noisy input–output observations, where the noise-free input is not parameterized. The basic assumptions made are (1) the dynamic system can be modeled by a (discrete- or continuous-time) rational transfer function model, (2) the temporal input–output disturbances are mutually independent, identically distributed noises, and (3) the input power spectrum is non-white (not necessarily rational) and is modeled nonparametrically. The system identifiability is guaranteed by exploiting the non-white spectrum property of the noise-free input. A frequency domain identification strategy is developed to estimate consistently the plant model parameters and the input–output noise variances. The uncertainty bound of the estimates is calculated and compared to the Cramér–Rao lower bound. The efficiency of the proposed algorithm is illustrated on numerical examples.     

Initial estimates for the dynamics of a Hammerstein system

This paper studies the generation of initial estimates for the dynamic part of a Hammerstein model. It will be shown that ARMAX or Box-Jenkins models result in better initial estimates than ARX or output-error (OE) models even in the absence of disturbing noise. This will be proven by noticing that a static nonlinear system can be replaced by a static gain plus a nonlinear noise source that acts in a completely similar way to disturbing noise for the study of the second-order properties of the estimators in the prediction error framework.     

Parametric and nonparametric identification of linear systems inthe presence of nonlinear distortions-a frequency domain approach

This paper studies the asymptotic behavior of nonparametric and parametric frequency domain identification methods to model linear dynamic systems in the presence of nonlinear distortions under some general conditions for random multisine excitations. In the first part, a related linear dynamic system (RLDS) approximation to the nonlinear system (NLS) is defined, and it is shown that the differences between the NLS and the RLDS can be modeled as stochastic variables with known properties. In the second part a parametric model for the RLDS is identified. Convergence in probability of this model to the RLDS is proven. A function of dependency is defined to detect and separate the presence of unmodeled dynamics and nonlinear distortions and to bound the bias error on the transfer function estimate     

Study of the bias of the MTC estimate by noise analysis due to the presence of feedback

This paper discusses the bias of the non-parametric Moderator Temperature Coefficient (MTC) estimate due to the presence of feedback. Up to now the non-parametric estimation of the Frequency Response Function (FRF) is the most commonly used method to estimate the MTC by noise analysis. This estimation method is proportional to the Cross Power Spectral Density between the total neutron flux variation and total temperature variation divided by the auto power spectral density of the total temperature variation. The estimation method is very popular since feedback is considered to be negligible in the frequency band of interest. Unfortunately this is not the case in practice. Measurements at a Nuclear Power Plant in Belgium will be used to confirm that this feedback cannot be neglected. In case of feedback the chosen estimator always results in a biased estimate when there are external neutron flux variations present. It will be seen that the ratio between the external neutron flux and external temperature variation in combination with the amplitude of the feedback determines the bias. The theoretical analysis of the bias is based on a simplified scheme of the MTC measurement setup. A simulation in MATLAB is used to confirm the theoretical results. In order to avoid a biased estimate due to the feedback we will advise to measure the external temperature variation and to use another non-parametric estimator.     

Identification of a periodic time series from an environmental proxy record

The past environment is often reconstructed by measuring a certain proxy (e.g. δ¹⁸O) in an environmental archive, i.e. a species that gradually accumulates mass and records the current environment during this mass formation (e.g. corals, shells, trees, etc.). When such an environmental proxy is measured, its values are known on a distance grid. However, to relate the data to environmental variations, the date associated with each measurement has to be known too. This transformation from distance to time is not straightforward to solve, since species usually do not grow at constant or known rates. In this paper, we investigate this problem for environmental archives exhibiting a certain periodicity. In practice, the method will be applicable to most annually resolved archives because these contain a seasonal component, e.g. clams, corals, sediment cores or trees. Due to variations in accretion rate the data along the distance axis have a disturbed periodic profile. In this paper, a method is developed to extract information about the accretion rate, such that the original (periodic, but further unknown) signal as a function of time can be recovered. The final methodology is quasi-independent of choices made by the investigator and is designed to deliver the most precise and accurate result. Every step in the procedure is described in detail, the results are tested on a Monte-Carlo simulation, and finally the method is exemplified on a real world example.     

Frequency-domain identification of linear time-invariant systemsunder nonstandard conditions

This paper presents a frequency-domain identification method for parametric transfer function models of linear time invariant systems that takes into account the nonzero initial condition and/or the transient effects in the response of the system when excited by periodic or time-limited signals. The method is useful for systems with large settling times where it takes too much time for the system to reach a steady state so that measurements can begin. In the special case of free decay experiments the presented technique is a frequency-domain version of fitting exponentially damped sinusoids embedded in noise. The theory is illustrated by simulations and a real measurement example