Practical neural network recipes in C++
Practical neural network recipes in C++
A data mining tool for learning from manufacturing systems
Proceedings of the 21st international conference on Computers and industrial engineering
Fault diagnosis using Rough Sets Theory
Computers in Industry
Rough Sets: Mathematical Foundations
Rough Sets: Mathematical Foundations
Using Data Mining Technology to Design an Intelligent CIM System for IC Manufacturing
SNPD-SAWN '05 Proceedings of the Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks
A novel manufacturing defect detection method using association rule mining techniques
Expert Systems with Applications: An International Journal
Review: A review of data mining applications for quality improvement in manufacturing industry
Expert Systems with Applications: An International Journal
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
An intelligent manufacturing process diagnosis system using hybrid data mining
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
Multi-sensor data fusion using support vector machine for motor fault detection
Information Sciences: an International Journal
Adaptive fault detection and diagnosis using an evolving fuzzy classifier
Information Sciences: an International Journal
Information Sciences: an International Journal
Multiple sensor fault diagnosis by evolving data-driven approach
Information Sciences: an International Journal
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This paper presents an evaluation of various methodologies used to determine relative significances of input variables in data-driven models. Significance analysis applied to manufacturing process parameters can be a useful tool in fault diagnosis for various types of manufacturing processes. It can also be applied to building models that are used in process control. The relative significances of input variables can be determined by various data mining methods, including relatively simple statistical procedures as well as more advanced machine learning systems. Several methodologies suitable for carrying out classification tasks which are characteristic of fault diagnosis were evaluated and compared from the viewpoint of their accuracy, robustness of results and applicability. Two types of testing data were used: synthetic data with assumed dependencies and real data obtained from the foundry industry. The simple statistical method based on contingency tables revealed the best overall performance, whereas advanced machine learning models, such as ANNs and SVMs, appeared to be of less value.