Research on Product Lifetime Prediction Models Based on Computer Technology

This article introduces the comparison between exploratory data analysis techniques and traditional experimental techniques. By adopting advanced data pattern recognition and utilizing data fitting and extrapolation methods, it avoids assuming a statistical distribution model for the data beforehand. This approach circumvents the potential inaccuracy in data analysis caused by incorrect model assumptions used in traditional experiments, reduces the time required for life testing and the number of test devices, and lowers the cost of testing. By combining exploratory data analysis techniques with product physical failure analysis, the article completes the construction of a life-related test system, sensitive parameter monitoring, data processing and model analysis, as well as the establishment of a life prediction model. The life prediction model is then validated, confirming its accuracy and effectiveness in guiding accurate assessments of a product's long-term operational and storage lifespan.