奧迪A6型汽車故障數據的分析和處理
摘要
現代社會，產品質量就是企業的生命線，汽車制造業更是如此，如何及時掌握汽車0部件的質量成為汽車生產廠家深為關注的問題。當汽車廠家得到準確的汽車部件維修數據時已經是汽車出廠多年，廠家不能及時掌握產品的質量也就不能對產品做出指導改進。本課題的研究在1定的程度上可以彌補汽車0部件維修數據向廠家決策層反饋的延時。為廠家高層及時掌握產品質量情況提供參考，從而推動產品質量以及售后服務質量的提高。
本文的研究從奧迪-A6轎車某1維修站點在某段時間內的維修數據表出發，通過對現有的某0件的維修數據表預測未來某個時間此0件的千車故障數。主要解決了橫向數據與縱向數據的合理性分析，數據擬合與殘差分析以及對未來千車故障數的預測。維修數據表主要包含哪個批次生產的轎車（即生產月份）、售出時間、維修時間、維修部位、損壞原因及程度、維修費用等等。通過這樣的數據可以全面了解所有部件的質量情況，若從不同的需求角度出發科學整理數據庫中的數據，可得到不同用途的信息，從而實現不同的管理目的。通過對數據分析和預測，就能逐步確定此種0件總的壽命，而更好的指導廠家對質量的改進，也能指導0件維修站對某些0件做提前準備，以免在緊急狀況下，因缺貨而導致信譽下降，更重要的是給用戶帶來了損失。
關鍵詞：反饋的延時 數據擬合 殘差分析 維修數據表
Abstract
In modern society, the quality of products is the lifeblood of enterprises, and it is the same truth with automobile manufacturing, so manufacturers attach great importance to how to grasp of the quality of a motor vehicle’s parts and components timely. The manufacturers of motor vehicle parts get the accurate identification data of maintenance vehicle only many years after the identification of the vehicles. The manufacturers can not make guiding improvement on products now that they can not control the quality of the products timely. The thesis can compensate, to some extent, the delay of feedback to policymakers on the maintenance data of the automotive components and it can provide the top manufacturers with timely information about the quality of products, thereby promoting the quality of products and enhancing the quality of after-sale service. The thesis studies from the maintenance data tables of Audi -A6 cars in a repair site within a certain period of time, and it forecasts the potential breakdown of one thousand cars in the future through the maintenance data sheet of existing parts. This thesis focuses on analyzing the horizontal and vertical resolution of the main data reasonably, data fitting, residual analysis and the projections of the potential breakdown of one thousand cars in the future and it also illustrates the main batch production cars (namely, the production month), sold time, maintenance time, maintenance parts, causes and extent of damage, repair costs, etc in the maintenance of data tables. The managers can master comprehensively the quality of all the components through such data, and they can attain different information to achieve different management purposes if they collate the data scientifically from different perspectives of demand. Through data analysis and projections, we can gradually establish the total life of such components, and provide better guidance for manufacturers to improve the quality, at the same time, prepare parts depots for some parts in advance, in case the shortage of parts in emergency situations causes the declining of credibility, and more importantly, a loss of the users.
Key words: the delay of feedback data fitting residual analysis maintenance data table
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