Maintenance Scheduling Optimization in a Multiple Production Line Considering Human Error

An analytical multiobjective maintenance planning model that maximizes reliability while minimizing cost and human error is proposed. In order to incorporate human error, the model minimizes the maximum human error over the planning horizon. Human Error Assessment and Reduction Technique (HEART) is used to quantify the human error. Maintenance activities include adjustment and replacement activities, in which each of them consumes a certain amount of human resource, spare parts, and budget and brings about a specified level of reliability and human error. Economic dependence is also considered, in which grouping maintenance activities reduces total cost. However, this may increase human error probability due to operator fatigue or time pressure. The main purpose is to investigate the relationship between human factors and maintenance activities to find the preferred maintenance plan. A multiple production line is considered as a case study. A sensitivity analysis is performed, and the effects of grouping and human factors on the preferred maintenance plan are discussed. It is shown how human proficiency may affect reliability and cost.     

Study of the LTI relations between the outputs of two coupled Wiener systems and its application to the generation of initial estimates for Wiener-Hammerstein systems

This paper consists of two parts. In the first, more theoretic part, two Wiener systems driven by the same Gaussian noise excitation are considered. For each of these systems, the best linear approximation (BLA) of the output (in mean square sense) is calculated, and the residuals, defined as the difference between the actual output and the linearly simulated output is considered for both outputs. The paper is focused on the study of the linear relations that exist between these residuals. Explicit expressions are given as a function of the dynamic blocks of both systems, generalizing earlier results obtained by Brillinger [Brillinger, D. R. (1977). The identification of a particular nonlinear time series system. Biometrika, 64(3), 509-515] and Billings and Fakhouri [Billings, S. A., & Fakhouri, S. Y. (1982). Identification of systems containing linear dynamic and static nonlinear elements. Automatica, 18(1), 15-26]. Compared to these earlier results, a much wider class of static nonlinear blocks is allowed, and the efficiency of the estimate of the linear approximation between the residuals is considerably improved. In the second, more practical, part of the paper, this new theoretical result is used to generate initial estimates for the transfer function of the dynamic blocks of a Wiener-Hammerstein system. This method is illustrated on experimental data.     

Non-parametric estimate of the system function of a time-varying system

The task of identifying an unknown dynamic system is made easier with prior knowledge on its behaviour. Using a frequency domain approach, the non-parametric maximum likelihood estimator of the system function, associated with the time-dependent impulse response of a time-varying system, is constructed. This is accomplished by the use of a simple linear least squares fitting algorithm, applied to the spectral response of the system to a multisine excitation. The noise variance on the system function is estimated simultaneously, and modelling errors can be detected, as illustrated on a simulation 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.     

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.     

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.     

Obtaining accurate confidence regions for the estimated zeros andpoles in system identification problems

In this note, a method is proposed to generate improved uncertainty bounds on the poles and zeros of an identified system. The uncertainty ellipsoids are replaced by dedicated uncertainty regions with a given probability level. The method can be applied to Markov estimators in the time or frequency domain     

Frequency-domain identification of linear systems using arbitraryexcitations and a nonparametric noise model

Presents a generalized frequency domain identification method to identify single-input/single-output (SISO) systems combining two previously published extensions in one method: arbitrary but persistent excitations are allowed and a nonparametric noise model is extracted from the same data that are used to identify the system. The method is directly applicable to identification in feedback if an external persistently exciting reference signal is available     

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.     

Identification of linear time invariant diffusion phenomena

Linear time invariant diffusion phenomena are described by linear parabolic partial differential equations with constant coefficients. The corresponding nonrational transfer functions in the Laplace variable s have an infinite number of poles. In this paper it is shown that these infinite dimensional systems can be very well approximated in a given frequency band by a rational form in √s. Potential applications are the modeling of mass or heat transfer phenomena. The theory is illustrated on the modeling of the ac impedance of two electrochemical processes: the reduction of iron and a traction battery