Development of an Intelligent Diagnosis and Maintenance System
Date Issued
2005
Date
2005
Author(s)
Chen, Ching-Shun
DOI
en-US
Abstract
In manufacturing industry, how to promote the equipment’s utility and then keep high throughput to achieve the order fulfillment is the primary requirement. The purpose of this thesis is aimed at developing an intelligent diagnosis and maintenance system to keep high availability of a machine. Moreover, accommodating the necessary of the immediate diagnostics for shop floor engineers, PDA based on the traditional and emerging technology RFID is employed for constructing mobile diagnosis system.
First of all, for effective and sensitive monitoring operation condition of a machine, PCA-based SPC with Nelson Rules is proposed. The experiment results support the feasibility of this approach. BPNN, SVM and CART are used to fault diagnosis. The highest accurate rates reach to 98.3%, 98.21%, and 97.44% respectively. The diagnostic ranked by the decision support system is sent to the related engineers to perform predictive maintenance with the message-passing platform, including SMS, e-mail, and on-line dialogue. MSMQ is applied to message transmission between monitoring server and diagnosing server for the computation workload balance.
In the mobile diagnosis system, PDA equipped with RFID CF Reader Card can easily acquire data characterizing the status of a machine from RFID sensors. In order to get more reliable and accurate information, the multi-sensor fusion algorithm is applied to integrate the sensor information. First, Fuzzy Logic is adopted for sensor validation and Decision Tree will make rudimentary diagnosis. Xbar Chart is used to observe the operation condition of the machine. Subsequently, a further analysis can be executed over remote diagnosis server, if necessary. Besides, by IEEE 802.11b wireless network, diagnosing server can send active warning notification to PDA and engineers can hold an on-line dialogue for troubleshooting.
Subjects
主成分分析
統計製程控制
到傳遞類神經網路
支向量機器
分類回歸決策樹
無線射頻識別
多感測融合技術
Principal Component Analysis
Statistical Process Control
Back Propagation Neural Network
Support Vector Machine
Classification and Regression Tree
Radio Frequency Identification
Multi-Sensor Fusion Algorithm
Type
thesis
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