Leshi Shu, Ping Jiang, Li Wan, Qi Zhou, Xinyu Shao and Yahui Zhang
Metamodels are widely used to replace simulation models in engineering design optimization to reduce the computational cost. The purpose of this paper is to develop a novel…
Abstract
Purpose
Metamodels are widely used to replace simulation models in engineering design optimization to reduce the computational cost. The purpose of this paper is to develop a novel sequential sampling strategy (weighted accumulative error sampling, WAES) to obtain accurate metamodels and apply it to improve the quality of global optimization.
Design/methodology/approach
A sequential single objective formulation is constructed to adaptively select new sample points. In this formulation, the optimization objective is to select a sample point with the maximum weighted accumulative predicted error obtained by analyzing data from previous iterations, and a space-filling criterion is introduced and treated as a constraint to avoid generating clustered sample points. Based on the proposed sequential sampling strategy, a two-step global optimization approach is developed.
Findings
The proposed WAES approach and the global optimization approach are tested in several cases. A comparison has been made between the proposed approach and other existing approaches. Results illustrate that WAES approach performs the best in improving metamodel accuracy and the two-step global optimization approach has a great ability to avoid local optimum.
Originality/value
The proposed WAES approach overcomes the shortcomings of some existing approaches. Besides, the two-step global optimization approach can be used for improving the optimization results.
Details
Keywords
Xiwen Cai, Haobo Qiu, Liang Gao, Xiaoke Li and Xinyu Shao
This paper aims to propose hybrid global optimization based on multiple metamodels for improving the efficiency of global optimization.
Abstract
Purpose
This paper aims to propose hybrid global optimization based on multiple metamodels for improving the efficiency of global optimization.
Design/methodology/approach
The method has fully utilized the information provided by different metamodels in the optimization process. It not only imparts the expected improvement criterion of kriging into other metamodels but also intelligently selects appropriate metamodeling techniques to guide the search direction, thus making the search process very efficient. Besides, the corresponding local search strategies are also put forward to further improve the optimizing efficiency.
Findings
To validate the method, it is tested by several numerical benchmark problems and applied in two engineering design optimization problems. Moreover, an overall comparison between the proposed method and several other typical global optimization methods has been made. Results show that the global optimization efficiency of the proposed method is higher than that of the other methods for most situations.
Originality/value
The proposed method sufficiently utilizes multiple metamodels in the optimizing process. Thus, good optimizing results are obtained, showing great applicability in engineering design optimization problems which involve costly simulations.
Details
Keywords
Qi Zhou, Xinyu Shao, Ping Jiang, Tingli Xie, Jiexiang Hu, Leshi Shu, Longchao Cao and Zhongmei Gao
Engineering system design and optimization problems are usually multi-objective and constrained and have uncertainties in the inputs. These uncertainties might significantly…
Abstract
Purpose
Engineering system design and optimization problems are usually multi-objective and constrained and have uncertainties in the inputs. These uncertainties might significantly degrade the overall performance of engineering systems and change the feasibility of the obtained solutions. This paper aims to propose a multi-objective robust optimization approach based on Kriging metamodel (K-MORO) to obtain the robust Pareto set under the interval uncertainty.
Design/methodology/approach
In K-MORO, the nested optimization structure is reduced into a single loop optimization structure to ease the computational burden. Considering the interpolation uncertainty from the Kriging metamodel may affect the robustness of the Pareto optima, an objective switching and sequential updating strategy is introduced in K-MORO to determine (1) whether the robust analysis or the Kriging metamodel should be used to evaluate the robustness of design alternatives, and (2) which design alternatives are selected to improve the prediction accuracy of the Kriging metamodel during the robust optimization process.
Findings
Five numerical and engineering cases are used to demonstrate the applicability of the proposed approach. The results illustrate that K-MORO is able to obtain robust Pareto frontier, while significantly reducing computational cost.
Practical implications
The proposed approach exhibits great capability for practical engineering design optimization problems that are multi-objective and constrained and have uncertainties.
Originality/value
A K-MORO approach is proposed, which can obtain the robust Pareto set under the interval uncertainty and ease the computational burden of the robust optimization process.
Details
Keywords
Jun Wu, Chaoyong Wu, Yaqiong Lv, Chao Deng and Xinyu Shao
Rolling bearings based on rotating machinery are one of the most widely used in industrial applications because of their low cost, high performance and robustness. The purpose of…
Abstract
Purpose
Rolling bearings based on rotating machinery are one of the most widely used in industrial applications because of their low cost, high performance and robustness. The purpose of this paper is to describe how to identify degradation condition of rolling bearing and predict its fault time in big data environment in order to achieve zero downtime performance and preventive maintenance for the rolling bearing.
Design/methodology/approach
The degradation characteristic parameters of rolling bearings including intrinsic mode energy and failure frequency were, respectively, extracted from the pre-processed original vibration signals using EMD and Hilbert transform. Then, Spearman’s rank correlation coefficient and PCA were used to obtain the health index of the rolling bearing so as to detect the appearance of degradations. Furthermore, the degradation condition of the rolling bearings might be identified through implementing the monotonicity analysis, robustness analysis and degradation analysis of the health index.
Findings
The effectiveness of the proposed method is verified by a case study. The result shows that the proposed method can be applied to monitor the degradation condition of the rolling bearings in industrial application.
Research limitations/implications
Further experiment remains to be done so as to validate the effectiveness of the proposed method using Apache Hadoop when massive sensor data are available.
Practical implications
The paper proposes a methodology for rolling bearing condition monitoring representing the steps that need to be followed. Real-time sensor data are utilized to find the degradation characteristics.
Originality/value
The result of the work presented in this paper form the basis for the software development and implementation of condition monitoring system for rolling bearings based on Hadoop.
Details
Keywords
Ji Cheng, Ping Jiang, Qi Zhou, Jiexiang Hu, Tao Yu, Leshi Shu and Xinyu Shao
Engineering design optimization involving computational simulations is usually a time-consuming, even computationally prohibitive process. To relieve the computational burden, the…
Abstract
Purpose
Engineering design optimization involving computational simulations is usually a time-consuming, even computationally prohibitive process. To relieve the computational burden, the adaptive metamodel-based design optimization (AMBDO) approaches have been widely used. This paper aims to develop an AMBDO approach, a lower confidence bounding approach based on the coefficient of variation (CV-LCB) approach, to balance the exploration and exploitation objectively for obtaining a global optimum under limited computational budget.
Design/methodology/approach
In the proposed CV-LCB approach, the coefficient of variation (CV) of predicted values is introduced to indicate the degree of dispersion of objective function values, while the CV of predicting errors is introduced to represent the accuracy of the established metamodel. Then, a weighted formula, which takes the degree of dispersion and the prediction accuracy into consideration, is defined based on the already-acquired CV information to adaptively update the metamodel during the optimization process.
Findings
Ten numerical examples with different degrees of complexity and an AIAA aerodynamic design optimization problem are used to demonstrate the effectiveness of the proposed CV-LCB approach. The comparisons between the proposed approach and four existing approaches regarding the computational efficiency and robustness are made. Results illustrate the merits of the proposed CV-LCB approach in computational efficiency and robustness.
Practical implications
The proposed approach exhibits high efficiency and robustness in engineering design optimization involving computational simulations.
Originality/value
CV-LCB approach can balance the exploration and exploitation objectively.
Details
Keywords
Qi Zhou, Ping Jiang, Xinyu Shao, Hui Zhou and Jiexiang Hu
Uncertainty is inevitable in real-world engineering optimization. With an outer-inner optimization structure, most previous robust optimization (RO) approaches under interval…
Abstract
Purpose
Uncertainty is inevitable in real-world engineering optimization. With an outer-inner optimization structure, most previous robust optimization (RO) approaches under interval uncertainty can become computationally intractable because the inner level must perform robust evaluation for each design alternative delivered from the outer level. This paper aims to propose an on-line Kriging metamodel-assisted variable adjustment robust optimization (OLK-VARO) to ease the computational burden of previous VARO approach.
Design/methodology/approach
In OLK-VARO, Kriging metamodels are constructed for replacing robust evaluations of the design alternative delivered from the outer level, reducing the nested optimization structure of previous VARO approach into a single loop optimization structure. An on-line updating mechanism is introduced in OLK-VARO to exploit the obtained data from previous iterations.
Findings
One nonlinear numerical example and two engineering cases have been used to demonstrate the applicability and efficiency of the proposed OLK-VARO approach. Results illustrate that OLK-VARO is able to obtain comparable robust optimums as to that obtained by previous VARO, while at the same time significantly reducing computational cost.
Practical implications
The proposed approach exhibits great capability for practical engineering design optimization problems under interval uncertainty.
Originality/value
The main contribution of this paper lies in the following: an OLK-VARO approach under interval uncertainty is proposed, which can significantly ease the computational burden of previous VARO approach.
Details
Keywords
Ping Jiang, Qi Zhou, Xinyu Shao, Ren Long and Hui Zhou
The purpose of this paper is to present a modified bi-level integrated system collaborative optimization (BLISCO) to avoid the non-separability of the original BLISCO. Besides, to…
Abstract
Purpose
The purpose of this paper is to present a modified bi-level integrated system collaborative optimization (BLISCO) to avoid the non-separability of the original BLISCO. Besides, to mitigate the computational burden caused by expensive simulation codes and employ both efficiently simplified and expensively detailed information in multidisciplinary design optimization (MDO), an effective framework combining variable fidelity metamodels (VFM) and modified BLISCO (MBLISCO) (VFM-MBLISCO) is proposed.
Design/methodology/approach
The concept of the quasi-separable MDO problems is introduced to limit range of applicability about the BLISCO method and then based on the quasi-separable MDO form, the modification of BLISCO method without any derivatives is presented to solve the problems of BLISCO. Besides, an effective framework combining VFM-MBLISCO is presented.
Findings
One mathematical problem conforms to the quasi-separable MDO form is tested and the overall results illustrate the feasibility and robustness of the MBLISCO. The design of a Small Waterplane Area Twin Hull catamaran demonstrates that the proposed VFM-MBLISCO framework is a feasible and efficient design methodology in support of design of engineering products.
Practical implications
The proposed approach exhibits great capability for MDO problems with tremendous computational costs.
Originality/value
A MBLISCO is proposed which can avoid the non-separability of the original BLISCO and an effective framework combining VFM-MBLISCO is presented to efficiently integrate the different fidelities information in MDO.
Details
Keywords
Xiaoke Li, Haobo Qiu, Zhenzhong Chen, Liang Gao and Xinyu Shao
Kriging model has been widely adopted to reduce the high computational costs of simulations in Reliability-based design optimization (RBDO). To construct the Kriging model…
Abstract
Purpose
Kriging model has been widely adopted to reduce the high computational costs of simulations in Reliability-based design optimization (RBDO). To construct the Kriging model accurately and efficiently in the region of significance, a local sampling method with variable radius (LSVR) is proposed. The paper aims to discuss these issues.
Design/methodology/approach
In LSVR, the sequential sampling points are mainly selected within the local region around the current design point. The size of the local region is adaptively defined according to the target reliability and the nonlinearity of the probabilistic constraint. Every probabilistic constraint has its own local region instead of all constraints sharing one local region. In the local sampling region, the points located on the constraint boundary and the points with high uncertainty are considered simultaneously.
Findings
The computational capability of the proposed method is demonstrated using two mathematical problems, a reducer design and a box girder design of a super heavy machine tool. The comparison results show that the proposed method is very efficient and accurate.
Originality/value
The main contribution of this paper lies in: a new local sampling region computational criterion is proposed for Kriging. The originality of this paper is using expected feasible function (EFF) criterion and the shortest distance to the existing sample points instead of the other types of sequential sampling criterion to deal with the low efficiency problem.
Details
Keywords
Xueshu Shan, Jinan Shao, Xinyu Zhao and Yongyi Shou
Despite the increasingly salient role of smart manufacturing (SM) in revolutionizing operational processes, little research has explored the dynamics of corporate executives’…
Abstract
Purpose
Despite the increasingly salient role of smart manufacturing (SM) in revolutionizing operational processes, little research has explored the dynamics of corporate executives’ decision-making on SM projects after their initiation. To fill this research gap, this study examines the configurations of project complexities (i.e. technological breadth and organizational breadth) and industry conditions (i.e. industry growth and industry competition) that shape managerial decisions to complete or terminate SM projects.
Design/methodology/approach
Using secondary data of 125 SM projects implemented in 106 manufacturers during the 2011–2020 period, we conduct a fuzzy-set qualitative comparative analysis to empirically derive equifinal configurations that lead to the completion or termination of SM projects.
Findings
The results reveal that project complexities and industry conditions work together in configurations where manufacturers will complete or terminate SM projects. We employ prospect theory to elucidate the findings and offer propositions.
Originality/value
Our study extends the extant SM literature by revealing the configurations of project complexities and industry conditions that shape managerial decisions on the completion or termination of SM projects after their initiation. It contributes to the prospect theory literature by accounting for the roles of both decision content and decision context and providing empirical evidence on their joint effects on managerial risk-taking decisions that alter the subjective value and probability weight of decision outcomes.
Details
Keywords
Xinyu Lin and Weihua Liu
To ensure the safety of aircraft fuel tanks, the FAA issued an airworthiness clause (25.981(b)) suggesting that the risk of combustion and explosion be reduced by installing a…
Abstract
Purpose
To ensure the safety of aircraft fuel tanks, the FAA issued an airworthiness clause (25.981(b)) suggesting that the risk of combustion and explosion be reduced by installing a Flammability Reduction Means or an Ignition Mitigation Means. The airflow distribution method has a significant effect on the inerting performance. Therefore, this study aims to determine an optimum airflow distribution method of the inerting system.
Design/methodology/approach
This paper establishes the calculation model of the oxygen concentration in the ullage of a multi-bay fuel tank, calculates the oxygen concentration in the ullage of an aircraft tank in single-flow and dual-flow modes under series and parallel ventilation methods and analyses the inerting performance of the tank under different airflow distribution methods.
Findings
The results show that: (1) the bleed flow rate required to achieve whole process inerting of multi-bay fuel tank in dual-flow mode is lower than that in single-flow mode; (2) under the parallel ventilation method, the decrease of oxygen concentration and the uniformity of each bay are better than that in the series ventilation method; (3) dual-flow mode staged ventilation method can be used to achieve the whole process inerting of the tanks under the minimum engine bleed consumption.
Originality/value
The novelty of this paper is to analyze and optimize the airflow distribution method of the inerting system under the whole flight envelope to minimize the engine bleed consumption.