Wenbing Chang

Technical decomposition approach of critical to quality characteristics for Product Design for Six Sigma

Product Design for Six Sigma (DFSS) approach is a structural and disciplined methodology driven by critical to quality characteristics (CTQs). How to identify and decompose the CTQs is the kernel part in the DFSS process. Traditional method only depends on the quality function deployment (QFD) matrix to flow down CTQs roughly. The paper puts forward a novel technical approach for CTQs decomposition from customer requirements into critical technical parameters based on the relational tree. Specifically, this approach emphasizes the systematic process and quantitative computation on quality relation weight. In order to specify the object of product DFSS, the connotation and evolution model of CTQs are created first. Then along the product development process, a decomposition measure for relational tree of CTQs is studied based on the functional and physical trees in Axiomatic Design (AD). And the quality relation weight computation of its nodes by means of Rough Set and fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is explored. Finally, an application on a car body noise vibration harshness (NVH) improvement, as an example, is given, and the decomposition process of NVH related with the functional and physical trees as well as its node weights computation algorithm are expounded in detail. Copyright

A technical framework of the taguchi system design method based on axiomatic design and TRIZ

70–80% of product quality is determined in the concept design process, concept design stage has become the “bottleneck” of the lifecycle quality control. Taguchi method is an effective design quality control approach, its focuses are on the parameter and tolerance design, and there is short of technique solution for system design. In this paper, based on the design axiom of Axiomatic Design (AD) and solution to contradictions in Theory of Inventive Problem Solving (TRIZ), a technical framework of Taguchi system design is put forward. The framework is a component of an evaluation method of product scheme based on AD and a solution flow of latent design contradictions based on TRIZ, and thus it can identify and solve the latent coupling defects laid in design scheme. Finally, a typical mechanical product design example is provided to validate the effectiveness and correctness of this technical framework.

An improved MCDM model with cloud TOPSIS method

The paper aims to develop an improved MCDM model with cloud TOPSIS method. In the past literature, many methods were used to deal with the complexity and uncertainty of the world in Multiple-criteria decision-making process, such as the linguistic variable, fuzzy set and so on. However, linguistic concept, as an effective tool to describe human cognition, has both fuzziness and randomness, which cannot be dealt with very well by traditional methods. Cloud Model, the very method to handle both fuzziness and randomness of the linguistic concept, is specially imported into the TOPSIS to solve the fuzziness and randomness in decision-making. In order to achieve the Cloud TOPSIS, the difference of Cloud is proposed; meanwhile PIC (Positive Ideal Cloud) and NIC (Negative Ideal Cloud) are defined. Finally, the method of Cloud TOPSIS is demonstrated applicable and effective, compared with the TOPSIS method based on interval data. The result suggests that the improved model has better distinction degree.

The establishment of a preventive maintenance period optimum model

Based on analysis of the reliability of weapons system under the conditions of variable maintenance period, the number of maintenance and failure under the conditions of the minimum acceptable reliability, and the relationship between the preventive maintenance cost and the corrective maintenance cost, an optimization model of equipment maintenance period is established. A computational experiment shows that this model provides a rational approach to determine reasonable maintenance period and maintenance management decision-making.

Research on supply chain risk assessment with intuitionistic fuzzy information

The paper suggests an intuitionistic fuzzy operator to assess supply chain risk. With the global economic integration trend, more and more enterprises adopt the modern management model of supply chain in order to have access to the advantage of responding quickly to the market demand. Through the implementation of supply chain management, their management efficiency has been significantly improved which led to brought good economic benefits. However, at the same time risk is becoming an unavoidable problem for the enterprises in supply chain, because of the uncertainty of the environment of the supply chain and the ever-increasing complexity of its own. The management of supply chain risk assessment has attracted increasing attention of theoretical and business research. The study investigates the supply chain risk assessment with intuitionistic fuzzy information, and then proposes a dependent intuitionistic fuzzy Hamacher weighted geometric (DIFHWG) operator. This operator is used to design an algorithm for supply chain risk assessment with intuitionistic fuzzy numbers. To demonstrate the effectiveness of this approach, several experiments are conducted to verify the developed method.

A Novel Bearing Multi-Fault Diagnosis Approach Based on Weighted Permutation Entropy and an Improved SVM Ensemble Classifier

Timely and accurate state detection and fault diagnosis of rolling element bearings are very critical to ensuring the reliability of rotating machinery. This paper proposes a novel method of rolling bearing fault diagnosis based on a combination of ensemble empirical mode decomposition (EEMD), weighted permutation entropy (WPE) and an improved support vector machine (SVM) ensemble classifier. A hybrid voting (HV) strategy that combines SVM-based classifiers and cloud similarity measurement (CSM) was employed to improve the classification accuracy. First, the WPE value of the bearing vibration signal was calculated to detect the fault. Secondly, if a bearing fault occurred, the vibration signal was decomposed into a set of intrinsic mode functions (IMFs) by EEMD. The WPE values of the first several IMFs were calculated to form the fault feature vectors. Then, the SVM ensemble classifier was composed of binary SVM and the HV strategy to identify the bearing multi-fault types. Finally, the proposed model was fully evaluated by experiments and comparative studies. The results demonstrate that the proposed method can effectively detect bearing faults and maintain a high accuracy rate of fault recognition when a small number of training samples are available.

Using the Capture-Recapture Method to Estimate the Incidence of Amyotrophic Lateral Sclerosis in Beijing, China

Background: To assess the total, gender-related and ageing process-related incidence rates of amyotrophic lateral sclerosis (ALS) in Beijing, China. Determine whether the decreased male to female ratio among postmenopausal age groups. Methods: We used the 2-source capture-recapture method to estimate the incidence of ALS in Beijing. The primary and secondary data sources were from diagnostic hospitals and assisted care institutions in the same area from 2010 to 2015. Results: A total of 562 cases and 283 cases were extracted from 2 data sources, and a total of 962 patients diagnosed with ALS within the 6-year period were estimated (95% CI 883–1041). The average yearly incidence was 0.77/100,000 persons (95% CI 0.71–0.83). The female to male ratio was 1.63. The incidence was associated with age and peaked in the 55–64 year age group. There was no obvious decline in the male:female ratio among postmenopausal age groups. Conclusions: The total incidence of ALS in Beijing is similar to international reports. The onset of ALS is not merely the result of ageing.

Research on detection methods based on Doc2vec abnormal comments

Abstract The purpose of this paper is to explore a method for detecting abnormal comments. With the growth of e-commerce sites and reviews sites, user reviews of messages begin to affect merchant’s revenue and impact consumer’s choice. It is meaningful to help users get real comment messages by using natural language processing means. In this paper, the doc2vec model, the clustering algorithm and emotion analysis are applied to identify abnormal comments.

An improved aircraft hard landing prediction model based on panel data clustering

This paper proposes a hard landing prediction method based on panel data clustering with flight data. The hard landing is a hazard that is critical to flight during the landing phase. It may cause damage to the aircraft structure, resulting in direct or indirect economic losses, damaging to human comfort and other adverse consequences. Firstly, based on the panel data in economics, the flight panel data is established; secondly, extracts the characteristic information of several key flight variables that affect the hard landing in each landing. The feature information includes mean, standard deviation, median, maximum, kurtosis, skewness and trend, and constitutes the eigenvectors describing the landings; then the k-means method is used to cluster the feature information. Finally, the empirical study is carried out on the 22 landing data of fixed wing unmanned aerial vehicles (UAVs). The results show that the clustering of flight panel data can be applied to hard landing prediction, and the prediction effect is obvious.

A prediction model of hard landing based on RBF neural network with K-means clustering algorithm

This paper proposes a prediction model for forecasting the hard landing problem. The landing phase has been demonstrated the most dangerous phase in flight cycle for fatal accidents. The landing safety problem has become one of the hot research problems in engineering management field. The study concentrates more on the prediction and advanced warning of hard landing. Firstly, flight data is preprocessed with data slicing method based on flight height and dimension reduction. Subsequently, the radial basis function (RBF) neural network model is established to predict the hard landing. Then, the structure parameters of the model are determined by the K-means clustering algorithm. In the end, compared with Support Vector Machine and BP neural network, the RBF neural network based on K-means clustering algorithm model is adopted and the prediction accuracy of hard landing is better than traditional ways.