Abstract

The Stream of Variation (SoV) model and control chart are combined to study the fault diagnosis method of flexible materials R2R manufacturing system. Based on the analysis of the correlation between the fault source and product quality in the manufacturing process and also the statistical distribution rule of the processing quality characteristic vector Li and the fault source fi, SoV model under controlled or uncontrolled states and the mathematical model of the probability distribution of the statistic Ti,m2 of the quality characteristic variable Li are deduced. And the calculation equation of the centerline, the upper limit, and the lower limit of the control chart are deduced. The experimental results show that, under controlled or uncontrolled condition, when the program runs to 500 steps, the Average Run Length (ARL) of the performance parameters tends to be stable; and when program reaches 1000 steps, the actual ARL value is almost the same as the theoretical value. The fault diagnosis experiment shows that, under the condition when the fault source is strongly correlated or the fault source correlation coefficient is the same, using the control chart established in this paper can simply and quickly determine the fault location in the system.

Highlights

  • R2R manufacturing system is a typical multistation continuous manufacturing system [1]

  • Since the factors that affect the quality of R2R are caused by many related process characteristics such as manufacturing system faults or motion abnormalities, it is difficult for the conventional prediction method to determine the fault source when a manufacturing quality problem occurs

  • The fault diagnosis method based on the quality control chart classifies the various patterns of control charts from processing quality data, establishes an abnormal pattern set and a fault set, and correlates the abnormal pattern set and the fault set in order to diagnose the fault source of the manufacturing system [2]

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Summary

Introduction

R2R manufacturing system is a typical multistation continuous manufacturing system [1]. The existing quality control chart fault diagnosis methods are univariate control chart [3], multivariate control chart [4], regression adjustment control chart [5], and so on When using these methods to monitor multistation systems, the control charts have a high false alarm rate. Based on the analysis of the correlation between each station of R2R manufacturing system, combining the physical analysis and data-driven method, this paper establishes the relation equation describing the process deviation of multistation and the final quality of the product, constructs a SoV model under controlled or uncontrolled manufacturing systems, works out corresponding quality control chart for product quality characteristic variables to monitor autocorrelation data, and detects and isolates multiple faults. This paper lays a theoretical foundation for the subsequent intelligent maintenance of R2R manufacturing system

Fault Diagnosis Based on Quality
Verification Experiment
Conclusion
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