Product early-fault root cause recognition method based on fuzzy data processing

Abstract

The invention discloses a product early-fault root cause recognition method based on fuzzy data processing. The method comprises the following steps of 1, constructing a product early-fault root cause relevance tree layer model; 2, constructing a potential fault root cause data model; 3, collecting product service life period quality and reliability data; 4, on the basis of the fault relevance tree layer model, a process target node and data fuzziness analysis are determined, and then node influence factors and fuzzy values are determined; 5, constructing a product early-fault root cause fuzzy data envelopment analysis model; 6, estimating an efficiency evaluation value of a fault relevance tree node; 7, fault relevance node relative weights are divided, and node priorities are ranked; 8, results are analyzed, and fault root cause recognition is completed. Development of the early fault root cause recognition technology under the early fault mechanism recognition cognition fuzzy environment is broken through, prevention measures are adopted for product design, technological design stage and other early fault forming stages, afterward treatment is changed into beforehand prevention, and the new idea is provided for early fault prevention and rectification.

Description

technical field [0001] The invention provides a method for identifying the root cause of product early failure based on fuzzy data processing, which relates to a method for identifying the root cause of product early failure based on association tree and fuzzy data envelope analysis, and belongs to the technical field of reliability modeling and analysis. Background technique [0002] The initial stage of putting into use the products that are directly output from the manufacturing end is often a key stage for customers to define the quality of products and form consumer trust. In the early stage of product delivery and use, frequent failures make the early failure rate remain high, which has become a criticism that companies and customers urgently need to avoid. Facing the failure characteristics of the whole life cycle of the product, the sensitivity of customers to early failure determines the importance and urgency of the research on the root cause identification of the ...

AI Technical Summary

Problems solved by technology

The early failure rate is a measure of the final reliability of the product. At this stage, on the one hand, the analysis of the early failure of the product in the use stage is generally attributed to improper design, raw material and manufacturing defects, etc. This general understanding leads to the identification of the root cause of the failure. The ambiguity of mechanism cognition on the problem is not suitable for accurately locating the root cause of early failure (key design and manufacturing parameters)

On the other hand, due to the functional parameters from the design, manufacture and use of the three major modules, data such as process parameters and process variables often appear in an imprecise way, and when the data is lacking, it often depends on the experience of experts to obtain the data , which will inevitably lead to ambiguity

At this time, the ambiguity of the data is mostly reflected in the large number of parameters, scattered relationships, and the confusion of cognition standards. The description of these data is not accurate, and most of them are some vague concepts. For example, in the evaluation of parameter design, there will be "important, general important , very important, not important”, etc. The uncertainty of these big data from the product life cycle reflects the ambiguity of the root cause data, and it is not easy to quantitatively analyze the root cause

At the same time, in the actual process, the evaluation of weights mostly depends on the subjective evaluation of experts or experience judgment, which will bring more judgments of human subjective factors, and cannot give an objective evaluation standard, thus affecting the root causes of early product failures. The effectiveness of the analysis of the reasons, resulting in the ambiguity of the weight evaluation

Therefore, the ambiguity of mechanism cognition, data ambiguity, and ambiguity brought about by traditional evaluation methods have seriously led to unsystematic identification and analysis of the root cause of failures, and the inability to accurately locate key design and manufacturing parameters, hindering targeted solutions to root causes. sexual problems

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