Intelligent Process Abnormal Patterns Recognition and Diagnosis Based on Fuzzy Logic

Shi-Wang Hou,Hui Wang,Shunxiao Feng

doi:10.1155/2016/8289508

Shi-Wang Hou, Hui Wang + Show 1 more

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https://doi.org/10.1155/2016/8289508

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Abstract

Locating the assignable causes by use of the abnormal patterns of control chart is a widely used technology for manufacturing quality control. If there are uncertainties about the occurrence degree of abnormal patterns, the diagnosis process is impossible to be carried out. Considering four common abnormal control chart patterns, this paper proposed a characteristic numbers based recognition method point by point to quantify the occurrence degree of abnormal patterns under uncertain conditions and a fuzzy inference system based on fuzzy logic to calculate the contribution degree of assignable causes with fuzzy abnormal patterns. Application case results show that the proposed approach can give a ranked causes list under fuzzy control chart abnormal patterns and support the abnormity eliminating.

Highlights

Control charts are widely used in abnormity monitoring and control in manufacturing and other processes
This paper considered four common abnormal patterns defined by the following rules: (1) Out of Control Limit (OCL): one or more points go beyond three sigma control limit
The output variables of six fuzzy inference modules were denoted by F1∼F6, valued within the range [0-1], and measured the contribution degree of corresponding assignable causes according to the occurrence degree of abnormal patterns

Summary

Introduction

Control charts are widely used in abnormity monitoring and control in manufacturing and other processes. The former is inherent in the process and is hard to eliminate; the latter indicates that there are some special sources of exceptions, which can be detected and eliminated or limited in certain range The latter can be represented in control chart as a form of abnormal pattern. The input variables were the degree of membership of a point in each zone represented by fuzzy sets, and the output was the process state, mapped by eleven fuzzy If- rules This approach provided improved results in terms of interpretation of data and consistency, as the numeric output from the fuzzy system indicated whether or not action should be taken, if the process was out of control. The results showed the proposed approach could detect abnormal shifts in the process especially in small shifts All these approaches use fuzzy logic only in analyzing the control chart patterns to determine the process state.

Problem Description

Case Study

Conclusions