Search

This study presents an innovative fault diagnosis methodology that integrates a convolutional neural network (CNN) with a bidirectional gated recurrent unit (BiGRU) and an attention mechanism (AM). Distinct from traditional techniques that primarily concentrate on enhancing diagnostic efficacy, this method additionally evaluates the impact of employing or omitting fast Fourier transform (FFT) preprocessing, as well as the positioning of the AM in feature extraction, on the accuracy of fault diagnosis. The diagnostic models are constructed using CNN-AM-BiGRU, CNN-BiGRU, and CNN-BiGRU-AM configurations. Subsequently, the AM assigns weights to the spatial and temporal features, enriching their depth and comprehensiveness. The final stage involves a comparative analysis of the probability values generated by CNN-AM-BiGRU, CNN-BiGRU, and CNN-BiGRU-AM across a specific iteration. This comparison is conducted under two distinct scenarios: one involving FFT preprocessing and the other without it, alongside varying positions of the AM in the feature extraction process. Meanwhile, the computational costs of the proposed method are compared to the state-of-the-art methods. The results indicate that the proposed fault diagnosis method significantly decreases computational time and effectively discerns the trend of probability values across diverse iterations. [ABSTRACT FROM AUTHOR]

This study introduces the active learning strategy and Kriging model into the reliability assessment of spur gear wear. First, the wear model of spur gears is constructed based on Archard's model, and its wear life is approximately estimated. Next, the uncertainty factors affecting the wear life are analyzed and quantified, which causes the reliability problem of spur gear wear. Then, the active learning Kriging model is introduced to estimate the wear reliability for highly efficient and highly accurate computation. Finally, the result analysis for a spur gear is provided, and the relevant results reveal the evolutionary law of spur gear wear depth with working pressure angle. Meanwhile, the wear reliability of spur gears is estimated by different learning functions for comparative analysis, and it is eventually estimated as approximately 99.45 %, which guides decision makers in engineering practice. In addition, Monte Carlo simulation is adopted to validate the accuracy of the estimated wear reliability of spur gears. [ABSTRACT FROM AUTHOR]

High-power lasers have severe thermal effects. The heat buildup during long-term operation will increase the threshold current of the laser and the internal defects, which will seriously affect the lifetime of the device. A temperature sensor is difficult to install on the laser rangefinder due to the small size of the solid-state laser device. Considering this situation, this work analyses the thermal mechanism of the laser rangefinder and proposes a temperature calculation method for the crystal working substance inside the laser rangefinder based on the finite element simulation to obtain the crystal center temperature and thermal model with the working time. Then, the prediction model of the maximum continuous working time of the laser can be obtained. The proposed model can provide an early scientific warning for the remaining continuous working time of the solid-state laser and improve the single-shot utilization of the solid-state laser to a certain extent. A comprehensive health management framework of laser rangefinders including FMEA is also proposed. [ABSTRACT FROM AUTHOR]

In this work, an uncertainty propagation method for the probabilistic fatigue life prediction is proposed on the basis of the generalized polynomial chaos theory, which takes into account the uncertainty of material parameters and can effectively estimate the probability density function and cumulative distribution function of the fatigue life of rolling bearings. First, posterior distributions of the material parameters are fitted by Bayesian theory and based on grey bootstrap sampling fatigue test data. Then, the collocation approximation method is used to solve the expansion coefficients. Furthermore, the polynomial chaos expansion under each stress level is substituted into the bearing fatigue life prediction model for the probabilistic fatigue life prediction. Finally, the proposed method is validited by rolling bearing fatigue tests under two loading conditions. Results demonstrated that the probabilistic fatigue lives of rolling bearings are well predicted by the proposed method under constant and variable loading conditions. [ABSTRACT FROM AUTHOR]

Safety integrity level (SIL), as defined in IEC 61511, is a widely used safety performance measure for safety instrumented functions. Shale gas station is an important part of shale gas gathering and transportation system. To ensure the safety of its instrument system is of great significance to the safe and stable operation of the whole gathering and transportation system. This paper is motivated to conduct a case study on safety integrity analysis for shale gas station by using the layer of protection analysis method (LOPA). Firstly, the equipment under control (EUC) is identified. It is followed by the analysis of reaction reasons on safety instrumented function (SIF) from safety instrumented system (SIS). Finally, according to the identification of independent protection layer (IPL) and probability of failure on demand (PFD), the SIL analysis for every SIF is carried out. The result shows that the SIS of this shale gas station has reached the required of SIL and there is no need for further increase in SIS. Utilizing this method can accurately determine the reliability of the SIS in the station, and it can also be the reference of safe operation for this shale gas station. [ABSTRACT FROM AUTHOR]

The internal clearance is a decisive parameter for life prediction and reliability analysis of rolling bearings. In this paper, a comprehensive reliability analysis model of rolling bearings is proposed by considering the internal clearance of rolling bearings. The model of bearings' working clearance was established after investigating the effect mechanisms of effective interference, temperature, and centrifugal force on the clearance. The validation of the results is carried out by the reliability analysis of a real rolling bearing through the modeling of working clearance probability distribution and the research of life factor. The results indicate that the proposed methodology provides an additional reference way for the reliability analysis of rolling bearings. [ABSTRACT FROM AUTHOR]

A new structural reliability analysis framework is developed to extend the performance of first-order reliability method, which is low robustness and poor accuracy when dealing with highly nonlinear functions. Initially, an improved conjugate sensitivity factor method is proposed to find the most probable point. The method enhances its robustness of convergence by introducing a conjugate gradient direction based sensitivity factor technique and improves its computational efficiency by putting forward a hybrid conjugate gradient factor and an adaptive step length strategy. Subsequently, the dimension reduction-based saddlepoint approximation method is developed, which uses the dimension reduction approach to construct the limit state function as the additive univariate quadratic functions and applies saddlepoint approximation to obtain the result with higher precision. A comparison analysis from five mathematical and structural examples illustrates that the proposed method is better than most existing methods in terms of robustness, efficiency and accuracy for estimating the failure probability of structures. [ABSTRACT FROM AUTHOR]

Trajectory similarity-based prediction (TSBP) is an emerging real-time remaining useful life (RUL) prediction method that has drawn considerable attention in the field of data-driven prognostics. TSBP is fast, and the corresponding model is easy to train. However, TBSP only provides a point estimation of RUL, which is insufficient for some specific prognostic applications. Hence, this study introduces an improved TSBP method to handle the issue of prognostic uncertainty. On the basis of an adaptive kernel density estimation technique and β-criterion, the improved TSBP method not only provides an accurate and precise point prediction of RUL but also specifies the confidence interval of RUL prediction. The capability of obtaining the confidence interval of RUL can enhance the TSBP method for uncertainty management. The effectiveness of the proposed method is validated through two cases studies, which are related to turbofan engine prognostics. [ABSTRACT FROM AUTHOR]

The wafer stage, as a key subsystem of the dual-stage lithographic tool, possesses the characteristic of high moving accuracy. The fault tree analysis (FTA), which plays an important role in the design stage of the wafer stage, is performed to distinguish the critical events leading to the overall transmission error. However, in view of the complexity of the wafer stage's structure and operation mechanism, the binary decision diagrams and dynamic fault tree analysis methods are implemented in this paper to obtain a simplified calculation process and a supplement with the dynamic characteristic respectively. This is not achieved by the traditional FTA method. The event "wafer stage's repetitive error along X axis exceeds 5 nm" is viewed as the top event of the fault tree. In this paper, we identify the critical factors affecting the kinematic accuracy of the wafer stage. [ABSTRACT FROM AUTHOR]

Field reliability assessment and prediction is critical for the estimation, operation and health management of CNC machine tools. The classical methods for field reliability of CNC Machine Tools assessment and prediction are challenged with the issues of expensive reliability tests, small sample size and unit non-homogeneity. In order to solve these problems, this paper introduces a degradation analysis based reliability assessment method for CNC machine tools under performance testing. Since the degradation is an independent increment process, the gamma process is employed to characterize the degradation process of CNC machine tools. The random effects are introduced to accommodate performance degradation model with unit non-homogeneity. The parameters of model are updated by Bayesian estimation approach. As a case study, the CNC Machine Tools is studied to illustrate the approach. And the proposed method is demonstrated precise for practical use. [ABSTRACT FROM AUTHOR]

Mean stress is known to exert significant effects on fatigue life prediction. Although numerous adjustments have been developed to explain the influence of mean stress, only a few of such adjustments account for mean stress sensitivity. The Smith-Watson-Topper (SWT) model is one of the most widely used models that can provide satisfactory predictions. It is regarded as a case of a Walker model when the material parameter γ = 0.5. The Walker equation considers the mean stress effect and sensitivity, and it can generate accurate predictions in many fatigue programs. In this work, a modified model that accounts for the mean stress effect and sensitivity is proposed to estimate fatigue life. Several sets of experimental data are used to validate the applicability of the proposed model. The proposed model is also compared with the SWT model and the Morrow model. Results show that the proposed model yields more accurate predictions than the other models. The proposed model is applied to predict the fatigue life of a low-pressure turbine blade. [ABSTRACT FROM AUTHOR]

The Assumed mode method (AMM) and Finite element method (FEM) were used. Their results were compared to investigate the coupled shaft-torsion, disk-transverse, and blade-bending vibrations in a flexible-disk rotor system. The blades were grouped with a spring. The flexible-disk rotor system was divided into three modes of coupled vibrations: Shaft-disk-blade, disk-blade, and blade-blade. Two new modes of coupled vibrations were introduced, namely, lacing wires-blade and lacing wires-disk-blade. The patterns of change of the natural frequencies and mode shapes of the system were discussed. The results showed the following: first, mode shapes and natural frequencies varied, and the results of the AMM and FEM differed; second, numerical calculation results showed three influencing factors on natural frequencies, namely, the lacing wire constant, the lacing wire location, and the flexible disk; lastly, the flexible disk could affect the stability of the system as reflected in the effect of the rotational speed. [ABSTRACT FROM AUTHOR]

Mean stress effects significantly influence the fatigue life of components. In general, tensile mean stresses are known to reduce the fatigue life of components, whereas compressive mean stresses are known to increase it. To date, various methods that account for mean stress effects have been studied. In this research, considering the high accuracy of mean stress correction and the difficulty in obtaining the material parameter of the Walker method, a practical method is proposed to describe the material parameter of this method. The test data of various materials are then used to verify the proposed practical method. Furthermore, by applying the Walker material parameter and the Smith-Watson-Topper (SWT) parameter, a modified strain-life model is developed to consider sensitivity to mean stress of materials. In addition, three sets of experimental fatigue data from super alloy GH4133, aluminum alloy 7075-T651, and carbon steel are used to estimate the accuracy of the proposed model. A comparison is also made between the SWT parameter method and the proposed strainlife model. The proposed strain-life model provides more accurate life prediction results than the SWT parameter method. [ABSTRACT FROM AUTHOR]

We created three-dimensional models of turbine disc and blades of a type of aero-engine according to their geometries. Extant studies show that low cycle fatigue is the main failure mechanism of turbine disc. By using a non-linear elastic-plastic finite element method, the static stress and strain state of the critically high stress regions, which can lead to operational failures, are determined. On the basis of stress-strain relation, a modified Walker strain life prediction model is proposed with no required knowledge on the mean stress correction. Then the predicted life data from the modified Walker strain model are compared with the testing results. After the verification, the modified Walker strain life prediction model and Miner's rule were applied to predict the lifetime of the turbine disc under various operating conditions. [ABSTRACT FROM AUTHOR]

The value of exponent d in Corten-Dolan's model is generally considered to be a constant. Nonetheless, the results predicted on the basis of this statement deviate significantly from the real values. In consideration of the effects of damage and stress state on fatigue life prediction, Corten-Dolan's model is improved by redefining the exponent d used in the traditional model. The improved model performs better than the traditional one with respect to the demonstration of a fatigue failure mechanism. Predictions of fatigue life on the basis of investigations into three metallic specimens indicate that the errors caused by the improved model are significantly smaller than those induced by the traditional model. Meanwhile, predictions derived according to the improved model fall into a narrower dispersion zone than those made as per Miner's rule and the traditional model. This finding suggests that the proposed model improves the life prediction accuracy of the other two models. The predictions obtained using the improved Corten-Dolan's model differ slightly from those derived according to a model proposed in previous literature; a few life predictions obtained on the basis of the former are more accurate than those derived according to the latter. Therefore, the improved model proposed in this paper is proven to be rational and reliable given the proven validity of the existing model. Therefore, the improved model can be feasibly and credibly applied to damage accumulation and fatigue life prediction to some extent. [ABSTRACT FROM AUTHOR]

Estimation of remaining useful life (RUL) is helpful to manage life cycles of machines and to reduce maintenance cost. Support vector machine (SVM) is a promising algorithm for estimation of RUL because it can easily process small training sets and multi-dimensional data. Many SVM based methods have been proposed to predict RUL of some key components. We did a literature review related to SVM based RUL estimation within a decade. The references reviewed are classified into two categories: improved SVM algorithms and their applications to RUL estimation. The latter category can be further divided into two types: one, to predict the condition state in the future and then build a relationship between state and RUL; two, to establish a direct relationship between current state and RUL. However, SVM is seldom used to track the degradation process and build an accurate relationship between the current health condition state and RUL. Based on the above review and summary, this paper points out that the ability to continually improve SVM, and obtain a novel idea for RUL prediction using SVM will be future works. [ABSTRACT FROM AUTHOR]

Based on kernel density estimation (KDE) and Kullback-Leibler divergence (KLID), a new data-driven fault diagnosis method is proposed from a statistical perspective. The ensemble empirical mode decomposition (EEMD) together with the Hilbert transform is employed to extract 95 time- and frequency-domain features from raw and processed signals. The distance-based evaluation approach is used to select a subset of fault-sensitive features by removing the irrelevant features. By utilizing the KDE, the statistical distribution of selected features can be readily estimated without assuming any parametric family of distributions; whereas the KLID is able to quantify the discrepancy between two probability distributions of a selected feature before and after adding a testing sample. An integrated Kullback- Leibler divergence, which aggregates the KLID of all the selected features, is introduced to discriminate various fault modes/damage levels. The effectiveness of the proposed method is demonstrated via the case studies of fault diagnosis for bevel gears and rolling element bearings, respectively. The observations from the case studies show that the proposed method outperforms the support vector machine (SVM)-based and neural network-based fault diagnosis methods in terms of classification accuracy. Additionally, the influences of the number of selected features and the training sample size on the classification performance are examined by a set of comparative studies. [ABSTRACT FROM AUTHOR]

Reliability-based multidisciplinary design optimization (RBMDO) has received increasing attention in engineering design for achieving high reliability and safety in complex and coupling systems (e.g., multidisciplinary systems). Mean-value first-order saddlepoint approximation (MVFOSA) is introduced in this paper and is combined with the collaborative optimization (CO) method for reliability analysis under aleatory uncertainty in RBMDO. Similar to the mean-value first-order second moment (MVFOSM) method, MVFOSA approximated the performance function with the first-order Taylor expansion at the mean values of random variables. MVFOSA uses saddlepoint approximation rather than the first two moments of the random variables to estimate the probability density and cumulative distribution functions. MVFOSA-based CO (MVFOSA-CO) is also formulated and proposed. Two examples are provided to show the accuracy and efficiency of the MVFOSA-CO method. [ABSTRACT FROM AUTHOR]

The inverse Gaussian process is recently introduced as an attractive and flexible stochastic process for degradation modeling. This process has been demonstrated as a valuable complement for models that are developed on the basis of the Wiener and gamma processes. We investigate the optimal design of the degradation tests on the basis of the inverse Gaussian process. In addition to an optimal design with pre-estimated planning values of model parameters, we also address the issue of uncertainty in the planning values by using the Bayesian method. An average pre-posterior variance of reliability is used as the optimization criterion. A trade-off between sample size and number of degradation observations is investigated in the degradation test planning. The effects of priors on the optimal designs and on the value of prior information are also investigated and quantified. The degradation test planning of a GaAs Laser device is performed to demonstrate the proposed method. [ABSTRACT FROM AUTHOR]

Reliability based design optimization (RBDO) has been widely implemented in engineering practices for high safety and reliability. It is an important challenge to improve computational efficiency. Sequential optimization and reliability assessment (SORA) has made great efforts to improve computational efficiency by decoupling a RBDO problem into sequential deterministic optimization and reliability analysis as a single-loop method. In this paper, in order to further improve computational efficiency and extend the application of the current SORA method, an enhanced SORA (ESORA) is proposed by considering constant and varying variances of random design variables while keeping the sequential framework. Some mathematical examples and an engineering case are given to illustrate the proposed method and validate the efficiency. [ABSTRACT FROM AUTHOR]

The conceptual design selection, which aims at choosing the best or most desirable design scheme among several candidates for the subsequent detailed design stage, oftentimes requires a set of tools to conduct design evaluation. Using computational intelligence techniques, such as fuzzy logic, neural network, genetic algorithm, and physical programming, several design evaluation methods are put forth in this paper to realize the conceptual design selection under different scenarios. Depending on whether an evaluation criterion can be quantified or not, the linear physical programming (LPP) model and the RAOGA-based fuzzy neural network (FNN) model can be utilized to evaluate design alternatives in conceptual design stage. Furthermore, on the basis of Vanegas and Labib's work, a multi-level conceptual design evaluation model based on the new fuzzy weighted average (NFWA) and the fuzzy compromise decision-making method is developed to solve the design evaluation problem consisting of many hierarchical criteria. The effectiveness of the proposed methods is demonstrated via several illustrative examples. [ABSTRACT FROM AUTHOR]

Reliability-based design optimization (RBDO) has been receiving increasing attention for achieving high safety and reliability in engineering design. Sequential optimization and reliability assessment (SORA), as one of the efficient single-loop methods, decouples an RBDO problem into sequential deterministic optimization and reliability analysis. An enhanced SORA (ESORA) method is proposed with the aim of further improving the computational efficiency for RBDO, considering both cases of constant and varying variances of random design inputs while keeping the single-loop framework. Vehicle side impact example is used to test and compare the efficiency of the proposed method with existing approaches. [ABSTRACT FROM AUTHOR]

Box-booms are widely used in some cranes. The strength of box-booms would continuously deteriorate with the use of time. In this paper, gamma process is employed to describe strength degradation when dealing with the overall stability of the box-boom. Furthermore, the total working time is not deterministic but random because of the random arrivals of tasks and the random working time of every task. By accounting for strength degradation and the random total working time, a time-dependent reliability-based design optimization (RBDO) model for the box-boom is proposed. An engineering case is used to illustrate the proposed model. [ABSTRACT FROM AUTHOR]

Within the competitive market environment, understanding customer requirements is crucial for all corporations to obtain market share and survive competition. Only the products exactly meeting customer requirements can win in the market place. Therefore, customer requirements play a very important role in the evaluation and decision process of conceptual design schemes of products. In this paper, an evaluation and decision method based on customer requirements is presented. It utilizes the importance of customer requirements, the satisfaction degree of each evaluation metric to the specification, and an evaluation metric which models customer requirements to evaluate the satisfaction degree of each design scheme to specific customer requirements via the proposed BP neural networks. In the evaluation and decision process, fuzzy sets are used to describe the importance of customer requirements, the relationship between customer requirements and evaluation metrics, the satisfaction degree of each scheme to customer requirements, and the crisp set is used to describe the satisfaction degree of each metric to specifications. The effectiveness of the proposed method is demonstrated by an example of front suspension fork design of mountain bikes. [ABSTRACT FROM AUTHOR]

As a kind of complicated mechanical component, rolling element bearing plays a significant role in rotating machines, and bearing fault detection benefits decision-making of maintenance and avoids undesired downtime cost. However, extraction of fault signatures from a collected signal in a practical working environment is always a great challenge. This paper proposes an improved combination of the Hilbert and wavelet transforms to identify early bearing fault signatures. Real rail vehicle bearing and motor bearing data were used to validate the proposed method. A traditional combination of Hilbert and wavelet transforms was employed for comparison purpose. An indicator to evaluate fault detection capability of methods was developed in this research. Analysis results showed that the extraction capability of bearing fault signatures is greatly enhanced by the proposed method. [ABSTRACT FROM AUTHOR]

The article aims to estimate the uncertainty of possible failure events of redundancy systems based on the cross-entropy (CE) method. Failure events of subsystems and components always result in the incomplete or complete failure of engineering systems, yet optimal condition monitoring of a complex system is heavily dependent on the accuracy analysis of all the failure events of subsystems and components and their interaction effects. The CE method is a versatile tool for estimating probabilities of rare events in complex systems with the least bias beyond conditional constraints. In this paper we introduce the CE method for analyzing the system reliability with the highest uncertainty among all possibilities satisfying supplied moment constraints, and developed numerical CE algorithms capable of estimating the uncertainty of failure modes in an M-dimensional redundancy system domain with moment constraints of order up to N. A general computational framework of event estimation and condition monitoring of redundancy systems is illustrated in which the Monte Carlo simulations and CE optimization algorithms are combined. Numerical results indicate potential improvements in the measure of the uncertainty of redundancy systems that would lead to the best-fit analysis of all the complete or incomplete failure events. [ABSTRACT FROM AUTHOR]

Conventional D-S evidence theory has an unavoidable disadvantage in that it will give counter-intuitive result when fusing high conflict information. This paper proposes an improved method to solve this problem. By reassigning weight factors before fusing, the method can give reasonable results especially when the initial weight factors of conflict evidences are almost equal. It gives an adjustable factor to adjust the reassigning force. An example is given to illustrate these advantages. [ABSTRACT FROM AUTHOR]

Machinery health monitoring is a key step in the implementation of Condition-based Maintenance in industry. In this procedure, a quantitative description of machine health condition is necessary for maintenance decision-making. In this paper, we applied singularity analysis with wavelet for data processing and a new concept, Lipschitz exponent function, was proposed based on wavelet transform. A kurtosis based health index was defined, which can be used for maintenance decision-making. The proposed method was validated with two sets of gearbox vibration data in comparison with three other indexes. The results show that kurtosis based health index demonstrates excellent performance. [ABSTRACT FROM AUTHOR]

Condition classification is an important step in machinery fault detection, which is a problem of pattern recognition. Currently, there are a lot of techniques in this area and the purpose of this paper is to investigate two popular recognition techniques, namely hidden Markov model and support vector machine. At the beginning, we briefly introduced the procedure of feature extraction and the theoretical background of this paper. The comparison experiment was conducted for gearbox fault detection and the analysis results from this work showed that support vector machine has better classification performance in this area. [ABSTRACT FROM AUTHOR]

Workflow systems have benefited the enterprise and customer in many aspects. But with the increasing complexity of the system, workflow design becomes a complicated and time-consuming process. In this paper, we model the system based on the Hierarchical Colored Petri Nets (HCPN) to avoid the complexity of the system, and the super nets of the model are abstracted as independent components to increase the flexibility, acceptability and maintainability of the system. Another important characteristic of this model is its convenience for reuse and workflow mining. The development process of the overhead traveling crane was given to demonstrate the proposed method. [ABSTRACT FROM AUTHOR]

Concurrent Engineering (CE) is a popular method employed in product development. It treats the whole product design process by the consideration of product quality, cost, rate of progress, and demands of customers. The development of computer and network technologies provides a strong support to the realization of CE in practice. Aiming at the characteristics of CE and network collaborative design, this paper built network collaborative design system frame. Through the analysis of the network collaborative design modes based on CE, this paper provided a novel network collaborative design integration model. This model can integrate the product design information, design process, and knowledge. Intelligent collaboration was considered in the proposed model. The study showed that the proposed model considered main factors such as information, knowledge, and design process in collaborative design. It has potential application in CE fields. [ABSTRACT FROM AUTHOR]

Interactive Fuzzy Physical Programming (IFPP) developed in this paper is a new efficient multi-objective optimization method, which retains the advantages of physical programming while considering the fuzziness of the designer’s preferences. The fuzzy preference function is introduced based on the model of linear physical programming, which is used to guide the search for improved solutions by interactive decision analysis. The example of multi-objective optimization design of the spindle of internal grinder demonstrates that the improved preference conforms to the subjective desires of the designer. [ABSTRACT FROM AUTHOR]

Conceptual design is the first step in the overall process of product design. Its intrinsic uncertainty, imprecision, and lack of information lead to the fact that current conceptual design activities in engineering have not been computerized and very few CAD systems are available to support conceptual design. In most of the current intelligent design systems, approach of principle synthesis, such as morphology matrix, bond graphic, or design catalogues, is usually adopted to deal with the concept generation, in which optional concepts are generally combined and enumerated through function analysis. However, as a large number of concepts are generated, it is difficult to evaluate and optimize these design candidates using regular algorithm. It is necessary to develop a new approach or a tool to solve the concept generation. Generally speaking, concept generation is a problem of concept synthesis. In substance, this process of developing design candidate is a combinatorial optimization process, viz., the process of concept generation can be regarded as a solution for a state-place composed of multi-concepts. In this paper, genetic algorithm is utilized as a feasible tool to solve the problem of combinatorial optimizalion in concept generation, in which the encoding method of morphology matrix based on function analysis is applied, and a sequence of optimal concepts are generated through the search and iterative process which is controlled by genetic operators, including selection, crossover, mutation, and reproduction in GA. Several crucial problems on GA are discussed in this paper, such as the calculation of fitness value and the criteria for heredity termination, which have a heavy effect on selection of better concepts. The feasibility and intellectualization of the proposed approach are demonstrated with an engineering case. In this work concept generation is implemented using GA, which can facilitate not only generating several better concepts, but also selecting the best concept. Thus optimal concepts can be conveniently developed and design efficiency can be greatly improved. [ABSTRACT FROM AUTHOR]

The preliminary design optimization of multi-stage spur gear reduction units has been a subject of considerable interest, since many high-performance power transmission applications (e.g., automotive and aerospace) require high-performance gear reduction units. There are multiple objectives in the optimal design of multi-stage spur gear reduction unit, such as minimizing the volume and maximizing the surface fatigue life. It is reasonable to formulate the design of spur gear reduction unit as a multi-objective optimization problem, and find an appropriate approach to solve it. In this paper an interactive physical programming approach is developed to place physical programming into an interactive framework in a natural way. Class functions, which are used to represent the designer’s preferences on design objectives, are fixed during the interactive physical programming procedure. After a Pareto solution is generated, a preference offset is added into the class function of each objective based on whether the designer would like to improve this objective or sacrifice the objective so as to improve other objectives. The preference offsets are adjusted during the interactive physical programming procedure, and an optimal solution that satisfies the designer’s preferences is supposed to be obtained by the end of the procedure. An optimization problem of three-stage spur gear reduction unit is given to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In view of the lack of efficient coordination of interdependent task in the collaborative design system, the mechanisms for temporal and resource coordination problems are established based on Petri Nets, respectively. Both of the mechanisms are encapsulated and implemented in the coordination component so as to increase the flexibility and acceptability of the system. We model the CSCW system based on Petri Nets for simulation, analysis and optimization. A case study on the overhead traveling crane is given to demonstrate and validate our theory. [ABSTRACT FROM AUTHOR]