Reliability Analysis and Prediction for Product Design Based on Feature Similarity

Pan

2020

Autex Research Journal

Reliability prediction of spinning machines can result in a time-saving and cost-saving development process with high reliability. Based on an analysis of failure times among systems and subsystems, a simulation method for reliability prediction of spinning machines is proposed by using the Monte Carlo simulation model. Firstly, factor weights are determined according to the fuzzy scoring and analytic hierarchy process. According to the status of reliability growth, growth coefficients are proposed based on reliability influencing factor weights and fuzzy scoring. To achieve the prediction of reliability distribution law, reliability index, and fault frequency, the reliability prediction model is constituted by combining the reliability growth coefficient and the Monte Carlo simulation model. Simulation results for spinning machines are obtained via the model thus built, which are confirmed with a practical example.

Section: Introductionmentioning

confidence: 99%

Simulation of Reliability Prediction Based on Multiple Factors for Spinning Machine

Pan

2020

Autex Research Journal

Advances in Mechanical Engineering

“…Because of the similarity between machine tools before and after the implementation of reliability growth technology, the idea of similar devices is used to deal with the uncertain information. 14,15 However, it is a knotty problem to accurately assess the degree of difference between similar products in reliability from the perspective of whole machine. 16 Fortunately, with the development of mechanical products toward the direction of modularization, the degree of difference in reliability between certain parts of the similar products which are defined as subsystems of products is not difficult to distinguish.…”

Section: Introductionmentioning

confidence: 99%

Reliability growth rate calculation method for hydrostatic spindle of heavy-duty machine tool using analytic network process

Tian

Yang

Chen

et al. 2018

Aiming at the problem that the difference of the equipment between before and after implementation of reliability growth technology is not considered in calculating reliability growth rate of computer numerical control machine tools, this article takes the hydrostatic spindle of the heavy-duty machine tool as an example and introduces the concept of “net growth rate” to propose a new method to calculate reliability growth rate. First, based on the value of mean time between failures of the whole machine before implementation of reliability growth technology, a model of the “net growth rate” of the whole machine is established. Second, the reliability relationship between the whole machine and the subsystems is established through the reliability block diagram, so the problem is transformed into solving the value of mean time between failures of the subsystems. Then, a superposition model of “net growth rate” of subsystems is established by making full use of experimental data and product information. When dealing with product information, multiple factors including composition structure, design requirement, manufacture and assembly, and use environment which influence equipment reliability are considered comprehensively and analytic network process is employed to obtain the weights of the influencing factors. Based on the weights, the reliability comprehensive scores which can reflect the reliability level in the corresponding design, manufacturing, and use environment are calculated and the reliability growth rate caused by the differences of the equipment is solved. In order to add ambiguity of human judgment, interval numbers are applied to network analysis process models. Finally, this article verifies the feasibility of the proposed method with an example.

“…Zhao, Wen, and Duan (2004b) introduce a multi-stage FSA approach to predict the reliability of aeroengines. Yang, Lin, He, and Chen (2003) develop a similarity-based reliability prediction method via combining both qualitative and quantitative fuzzy similarity approaches. Ling, Song, and Sun (2011) develop a reliability prediction method which incorporates the product defect information from the failure mode and effects analysis (FMEA).…”

Section: Introductionmentioning

confidence: 99%

Reliability prediction for evolutionary product in the conceptual design phase using neural network-based fuzzy synthetic assessment

Huang

International Journal of Systems Science

2013

Reliability prediction plays an important role in product lifecycle management. It has been used to assess various reliability indices (such as reliability, availability and mean time to failure) before a new product is physically built and/or put into use. In this article, a novel approach is proposed to facilitate reliability prediction for evolutionary products during their early design stages. Due to the lack of sufficient data in the conceptual design phase, reliability prediction is not a straightforward task. Taking account of the information from existing similar products and knowledge from domain experts, a neural network-based fuzzy synthetic assessment (FSA) approach is proposed to predict the reliability indices that a new evolutionary product could achieve. The proposed approach takes advantage of the capability of the back-propagation neural network in terms of constructing highly non-linear functional relationship and combines both the data sets from existing similar products and subjective knowledge from domain experts. It is able to reach a more accurate prediction than the conventional FSA method reported in the literature. The effectiveness and advantages of the proposed method are demonstrated via a case study of the fuel injection pump and a comparative study.