The performance of a manufacturing system relies on its four types of elements: operators, machines, computer system and material handling system. To ensure the performance of these elements, operational data containing various aspects of information are collected for monitoring and analysis. This dissertation focuses on the operator performance evaluation and machine failure prediction. The proposed research work is motivated by the following challenges in analyzing operational data. (i) the complex relationship between the variables, (ii) the implicit information important to failure prediction, and (iii) data with outliers, missing and erroneous measurements. To overcome these challenges, the following research has been conducted. To compare operator performance, a methodology combining regression modeling and multiple comparisons technique is proposed. The regression model quantifies and removes the complex effects of other impacting factors on the operator performance. A robust zero-inflated Poisson (ZIP) model is developed to reduce the impacts of the excessive zeros and outliers in the performance metric, i.e. the number of defects (NoD), on regression analysis. The model residuals are plotted in non-parametric statistical charts for performance comparison. The estimated model coefficients are also used to identify under-performing machines. To detect temporal patterns from operational data sequence, an algorithm is proposed for detecting interval-based asynchronous periodic patterns (APP). The algorithm effectively and efficiently detects pattern through a modified clustering and a convolution-based template matching method. To predict machine failures based on the covariates with erroneous measurements, a new method is proposed for statistical inference of proportional hazard model under a mixture of classical and Berkson errors. The method estimates the model coefficients with an expectation-maximization (EM) algorithm with expectation step achieved by Monte Carlo simulation. The model estimated with the proposed method will improve the accuracy of the inference on machine failure probability. The research work presented in this dissertation provides a package of solutions to improve manufacturing system performance. The effectiveness and efficiency of the proposed methodologies have been demonstrated and justified with both numerical simulations and real-world case studies.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/301673 |
| Date | January 2013 |
| Creators | Wang, Zhenrui |
| Contributors | Liu, Jian, Son, Young-Jun, Dessureault, Sean, Niu, Yue, Liu, Jian |
| Publisher | The University of Arizona. |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | text, Electronic Dissertation |
| Rights | Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. |
Page generated in 0.0022 seconds