Abstract: In general, issues related to design and maintenance are considered in an independent manner. However, the decisions made in these two sets influence each other. The design for maintenance is considered an opportunity to optimize the life cycle cost of a product, particularly in the nuclear or aeronautical field, where maintenance expenses represent more than 60% of life cycle costs. The design of large-scale systems starts with product architecture, a choice of components in terms of cost, reliability, weight and other attributes, corresponding to the specifications. On the other hand, the design must take into account maintenance by improving, in particular, real-time monitoring of equipment through the integration of new technologies such as connected sensors and intelligent actuators. We noticed that different approaches used in the Design For Maintenance (DFM) methods are limited to the simultaneous characterization of the reliability and maintainability of a multi-component system. This article proposes a method of DFM that assists designers to propose dynamic maintenance for multi-component industrial systems. The term "dynamic" refers to the ability to integrate available monitoring data to adapt the maintenance decision in real time. The goal is to maximize the availability of the system at a given life cycle cost. This paper presents an approach for simultaneous optimization of the design and maintenance of multi-component systems. Here the design is characterized by four decision variables for each component (reliability level, maintainability level, redundancy level, and level of monitoring data). The maintenance is characterized by two decision variables (the dates of the maintenance stops and the maintenance operations to be performed on the system during these stops). The DFM model helps the designers choose technical solutions for the large-scale industrial products. Large-scale refers to the complex multi-component industrial systems and long life-cycle, such as trains, aircraft, etc. The method is based on a two-level hybrid algorithm for simultaneous optimization of design and maintenance, using genetic algorithms. The first level is to select a design solution for a given system that considers the life cycle cost and the reliability. The second level consists of determining a dynamic and optimal maintenance plan to be deployed for a design solution. This level is based on the Maintenance Free Operating Period (MFOP) concept, which takes into account the decision criteria such as, total reliability, maintenance cost and maintenance time. Depending on the life cycle duration, the desired availability, and the desired business model (sales or rental), this tool provides visibility of overall costs and optimal product architecture.
Abstract: A direction for ACO is to optimize continuous and mixed (discrete and continuous) variables in solving problems with various types of data. Support Vector Machine (SVM), which originates from the statistical approach, is a present day classification technique. The main problems of SVM are selecting feature subset and tuning the parameters. Discretizing the continuous value of the parameters is the most common approach in tuning SVM parameters. This process will result in loss of information which affects the classification accuracy. This paper presents two algorithms that can simultaneously tune SVM parameters and select the feature subset. The first algorithm, ACOR-SVM, will tune SVM parameters, while the second IACOMV-R-SVM algorithm will simultaneously tune SVM parameters and select the feature subset. Three benchmark UCI datasets were used in the experiments to validate the performance of the proposed algorithms. The results show that the proposed algorithms have good performances as compared to other approaches.
Abstract: In this paper we are interested in classification problems
with a performance constraint on error probability. In such
problems if the constraint cannot be satisfied, then a rejection option
is introduced. For binary labelled classification, a number of SVM
based methods with rejection option have been proposed over the
past few years. All of these methods use two thresholds on the SVM
output. However, in previous works, we have shown on synthetic data
that using thresholds on the output of the optimal SVM may lead to
poor results for classification tasks with performance constraint. In
this paper a new method for supervised classification with rejection
option is proposed. It consists in two different classifiers jointly
optimized to minimize the rejection probability subject to a given
constraint on error rate. This method uses a new kernel based linear
learning machine that we have recently presented. This learning
machine is characterized by its simplicity and high training speed
which makes the simultaneous optimization of the two classifiers
computationally reasonable. The proposed classification method with
rejection option is compared to a SVM based rejection method
proposed in recent literature. Experiments show the superiority of
the proposed method.
Abstract: Machining is an important manufacturing process used to produce a wide variety of metallic parts. Among various machining processes, turning is one of the most important one which is employed to shape cylindrical parts. In turning, the quality of finished product is measured in terms of surface roughness. In turn, surface quality is determined by machining parameters and tool geometry specifications. The main objective of this study is to simultaneously model and optimize machining parameters and tool geometry in order to improve the surface roughness for AISI1045 steel. Several levels of machining parameters and tool geometry specifications are considered as input parameters. The surface roughness is selected as process output measure of performance. A Taguchi approach is employed to gather experimental data. Then, based on signal-to-noise (S/N) ratio, the best sets of cutting parameters and tool geometry specifications have been determined. Using these parameters values, the surface roughness of AISI1045 steel parts may be minimized. Experimental results are provided to illustrate the effectiveness of the proposed approach.
Abstract: Cooktop burners are widely used nowadays. In
cooktop burner design, nozzle efficiency and greenhouse
gas(GHG) emissions mainly depend on heat transfer from the
premixed flame to the impinging surface. This is a complicated
issue depending on the individual and combined effects of various
input combustion variables. Optimal operating conditions for
sustainable burner design were rarely addressed, especially in the
case of multiple slot-jet burners. Through evaluating the optimal
combination of combustion conditions for a premixed slot-jet
array, this paper develops a practical approach for the sustainable
design of gas cooktop burners. Efficiency, CO and NOx emissions
in respect of an array of slot jets using premixed flames were
analysed. Response surface experimental design were applied to
three controllable factors of the combustion process, viz.
Reynolds number, equivalence ratio and jet-to-vessel distance.
Desirability Function Approach(DFA) is the analytic technique
used for the simultaneous optimization of the efficiency and
emission responses.