Method for identifying early failure root causes of products based on dimensionality reduction and support vector machine

Abstract

Provided is a method for identifying the early failure root causes of products based on dimensionality reduction and a support vector machine (SVM). The method comprises steps of: 1, constructing a relevance tree conceptual model oriented to a fault feature; 2, constructing a failure root cause node big data model; 3, collecting product life cycle quality and reliability data; 4, constructing a principal component analysis technical model; 5, constructing a relevance tree based on the score of the principal components; 6, constructing a SVM technical classification model; 7, ranking the priorities of the nodes based on the SVM technical classification model; and 8, analyzing results. The method breaks through the redundant problem of high-dimensional big data from the point of view of product reliability formation big data, and fundamentally makes up for the imprecision and misjudgment of early failure mechanism cognition caused by ignorance of high-dimensional data characteristic space in a traditional sense, improves the recognition efficiency and accuracy of early failure root cause in big data environment, and provides manufacturers with clear objectives and objects to implement active early failure control strategy in engineering application.

Description

technical field [0001] The invention provides a product early failure root cause identification method based on dimensionality reduction and support vector machine, which relates to an analysis method of product early failure root cause based on dimensionality reduction and support vector machine, which belongs to reliability modeling and analysis technology field. Background technique [0002] Excessive early failures have always been the technical bottleneck restricting the batch production of equipment. The manufactured products from the design to the manufacturing end of the output enter the early stage of use, and show a high early failure rate under the influence of environmental stress, which has attracted widespread attention from customers. There is no good way to optimize early failures and quickly eliminate peaks from the source of manufacturing and design. The key lies in the lack of systematic research on early failure mechanisms of products from the perspective...

AI Technical Summary

Problems solved by technology

[0004] Due to the complexity of the manufacturing process and the presence of numerous uncontrolled operational factors, manufactured products often exhibit an exceptionally high rate of early failures, and the identification of root causes of early product failures has become a challenging issue for manufacturers

Especially in the era of big data, it is easier to obtain a large amount of data on the product life cycle. Those high-dimensional big data always carry a lot of irrelevant noise information, resulting in not only the accuracy is not obvious, but also the current small data-driven method makes the model Redundancy in training time

In addition, traditional small data-oriented analytical techniques are not suitable for the new big data environment

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