Root cause identification method of product early failure based on dimensionality reduction and support vector machine

Abstract

A method for identifying the root cause of early product failures based on dimensionality reduction and support vector machines. The steps are as follows: 1. Construct a conceptual model of a fault feature-oriented association tree; 2. Construct a big data model of fault root cause nodes; 3. Product life cycle Quality and reliability data collection; 4. Construction of principal component analysis technology model; 5. Association tree construction based on principal component scores; 6. Construction of support vector machine technology classification model; 7. Node priority ranking based on support vector machine technology classification ; 8. Results analysis. The present invention starts from the big data from the perspective of product reliability, breaks through the redundancy problem of high-dimensional big data, and fundamentally makes up for the inaccurate and misjudgment of early failure mechanism understanding caused by ignoring the feature space of high-dimensional data in the traditional sense. It improves the efficiency and accuracy of early fault root cause identification in the big data environment, and provides manufacturers with clear goals and targets to implement active early fault control strategies in engineering applications.

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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