Application of python libraries for variance, normal distribution and Weibull distribution analysis in diagnosing and operating production systems

Leszek Klich,Andrzej Chmielowiec

doi:10.29354/diag/144479

Abstract

1. Aigner R, Leitner M, Stoschka M. Fatigue strength characterization of Al-Si cast material incorporating statistical size effect. In G. Henaff, editor, 12th International Fatigue Congress (FATIGUE 2018), volume 165 of MATEC Web of Conferences, 2018. https://doi.org/10.1051/matecc.... CrossRef Google Scholar

Highlights

Serial production reaching tens or even hundreds of millions of pieces a year is not unusual these days
By writing down the state of the company in successive moments of time t1 < t2 < ⋯ < tr we obtain a matrix that reflects the changes in the production process: s1,t1 s2,t1 ... sm,t1 =
Increase k2,i for all i ∈ {i − h, ... , i + h} It should be noted that it makes sense to determine either the vector of the parameters of the normal distribution N or the vector of parameters of the Weibull distribution W

Summary

Introduction

Serial production reaching tens or even hundreds of millions of pieces a year is not unusual these days. The level of automation requires proper control of the production processes. It is usually connected with the necessity of installation of a large number of sensors that record the condition of machines and the quality of manufactured products. The number of sensors is as high as 103 in the case of a medium-sized company, and even 105 in the case of very large factories. The production state at time t can be described as series of measurement data St = By writing down the state of the company in successive moments of time t1 < t2 < ⋯ < tr we obtain a matrix that reflects the changes in the production process: s1,t1 s2,t1 ... By writing down the state of the company in successive moments of time t1 < t2 < ⋯ < tr we obtain a matrix that reflects the changes in the production process: s1,t1 s2,t1 ... sm,t1

Methods

Results

Conclusion