Integrated reconfiguration and age-based preventive maintenance decision making

The use of manufacturing system reconfiguration in conjunction with maintenance operations has not been previously reported in the literature. This research attempts to incorporate reconfiguration into Preventive Maintenance (PM) actions for improved system performance in terms of reduced total cost. This paper presents an Integrated Reconfiguration and Age-Based Maintenance (IRABM) policy and applies it to a parallel-serial manufacturing system. The expected total cost of implementing the IRABM policy is estimated and minimized through a simulation-based heuristic optimization procedure. Using this method, it is possible to systematically identify the conditions under which the integration of reconfiguration into maintenance is cost effective. In addition, numerical examples demonstrate that the manufacturing system could have a higher probability of fulfilling production requirements at a lower cost under the IRABM policy compared to the conventional age-based PM policy. The influences of the input parameters associated with reconfiguration, production, and reliability on the performance of IRABM policy also are studied.     

DATA CLUSTERING USING A REORGANIZING NEURAL NETWORK

A new approach, designed for clustering of arbitrary distributed patterns, is presented. This study is concerned with the use of a self-organizing neural network as a frame for data clustering. The nearest network nodes in feature space are treated as prototypes, assigned to the corresponding cluster. The rules for dead-node shifting and simple adjustment of coordinates of the active nodes are introduced. The performance of the proposed self-organizing neural network is examined on the benchmark synthetic and the real-world problem.     

Maximum likelihood estimation from interval censored recurrent event data

In this paper, we consider the recurrent failures of several repairable units, which can only be observed at periodic inspection times. A unit is not aging over the period between a failure and its detection. The failure times are interval censored by the periodic assessment times. The observed data consists of censoring intervals of failure times and the unobserved data are the actual ages of the units at the failure times. We formulate the likelihood function and use several iterative algorithms to find the maximum likelihood estimate (MLE) of the parameters. The complete Expectation–Maximization (EM) algorithm, the EM gradient, full Newton–Raphson (NR), and the Simplex method are used. We derive recursive equations to calculate the expected values required in the algorithms. We estimate the parameters for four failure datasets, assuming that the failures follow a non-homogeneous Poisson process (NHPP). Three datasets are obtained from a hospital for the components of general infusion pump, and the fourth dataset is simulated. Since the estimation could take a long time, we compare the performance of the algorithms in terms of the required number of iterations to converge, the total execution time, and the precision of the estimated parameters. We also use Monte Carlo and Quasi-Monte Carlo simulation as the substitutes for the recursive procedures in the Expectation step of the EM gradient and compare the results.     

Adaptive neuro-fuzzy wheel slip control

Due to complex and nonlinear dynamics of a braking process and complexity in the tire–road interaction, the control of automotive braking systems performance simultaneously with the wheel slip represents a challenging problem. The non-optimal wheel slip level during braking, causing inability to achieve the desired tire–road friction force strongly influences the braking distance. In addition, steerability and maneuverability of the vehicle could be disturbed. In this paper, an active neuro-fuzzy approach has been developed for improving the wheel slip control in the longitudinal direction of the commercial vehicle. The dynamic neural network has been used for prediction and an adaptive control of the brake actuation pressure, during each braking cycle, according to the identified maximum adhesion coefficient between the wheel and road surface. The brake actuation pressure was dynamically adjusted on the level that provides the optimal level of the longitudinal wheel slip vs. the brake pressure selected by driver, the current vehicle speed, the brake interface temperature, vehicle load conditions, and the current value of longitudinal wheel slip. Thus the dynamic neural network model operates (learn, generalize and predict) on-line during each braking cycle, fuzzy logic has been integrated with the neural model as a support to the neural controller control actions in the case when prediction error of the dynamic neural model reached the predefined value. The hybrid control approach presented here provided intelligent dynamic model – based control of the brake actuation pressure in order to keep the longitudinal wheel slip on the optimum level during a braking cycle.     

Experimental validation of the discrete-time MCS adaptive strategy

This paper is concerned with the implementation and experimental validation of a discrete-time model reference adaptive control strategy, known as Minimal Control Synthesis (MCS) algorithm. After discussing the proof of stability of the algorithm when applied to discretized models of continuous-time plants, the problem of controlling a highly nonlinear electro-mechanical device is taken as a representative case of study. It is shown that the discrete-time MCS is an effective strategy to solve the problem while guaranteeing robustness to unmodeled nonlinear dynamics over a wide range of test manoeuvres.     

Analytical and graphical design of lead–lag compensators

In this article an approach based on inversion formulae is used for the design of lead–lag compensators which satisfy frequency domain specifications on phase margin, gain margin and phase (or gain) crossover frequency. An analytical and graphical procedure for the compensator design on the Nyquist and Nichols planes is presented with some numerical examples.     

Iterative algorithm to compute the maximal and stabilising solutions of a general class of discrete-time Riccati-type equations

In this article an iterative method to compute the maximal solution and the stabilising solution, respectively, of a wide class of discrete-time nonlinear equations on the linear space of symmetric matrices is proposed. The class of discrete-time nonlinear equations under consideration contains, as special cases, different types of discrete-time Riccati equations involved in various control problems for discrete-time stochastic systems. This article may be viewed as an addendum of the work of Dragan and Morozan (Dragan, V. and Morozan, T. (2009), ‘A Class of Discrete Time Generalized Riccati Equations’, Journal of Difference Equations and Applications, first published on 11 December 2009 (iFirst), doi: 10.1080/10236190802389381) where necessary and sufficient conditions for the existence of the maximal solution and stabilising solution of this kind of discrete-time nonlinear equations are given. The aim of this article is to provide a procedure for numerical computation of the maximal solution and the stabilising solution, respectively, simpler than the method based on the Newton–Kantorovich algorithm.     

Influence of mediator immobilization on the electrochemically assisted microbial dechlorination of trichloroethene (TCE) and cis‐dichloroethene (cis‐DCE)

BACKGROUND: A bioelectrochemical process for trichloroethene (TCE) dechlorination was developed. In this new process, a solid‐state electrode polarized to ?450 mV versus the standard hydrogen electrode (SHE), in combination with a redox mediator (i.e., methyl viologen, MV) is employed as an electron donor for the microbial reductive dechlorination of TCE. In this study we compared the performance of the process with the redox mediator immobilized at the surface of electrodes or dissolved in the bulk liquid, using both a culture highly enriched in Desulfitobacterium spp., capable of dechlorinating TCE to cis‐dichloroethene (cis‐DCE), and a culture highly enriched in Dehalococcoides spp. capable of dechlorinating cis‐DCE to ethene. RESULTS: Short‐term potentiostatic (?450 mV versus SHE) experiments showed that TCE or cis‐DCE was dechlorinated both in the presence of soluble (500 µmol L^?1) and immobilized MV. However, TCE or cis‐DCE dechlorination rates with MV‐modified electrodes were remarkably lower than with soluble MV. Both cultures produced significant amounts of H₂ in the presence of electrically reduced, soluble MV, whereas no H₂ was produced when the mediator was immobilized at the electrode surface, regardless of the potential applied to the electrode, in the range ?425 to ?500 mV versus SHE. CONCLUSIONS: The process, operated with modified electrodes, supports the microbial dechlorination of TCE to ethene. Immobilization not only allows retention of the mediator within the system, but also increases process efficiency by preventing bioelectrochemical H₂ formation. On the other hand, strategies to increase dechlorination rates with modified electrodes need to be developed. Copyright © 2009 Society of Chemical Industry