A new neural network-based control scheme for fault detection and fault diagnosis in fuzzy multivariate multinomial data. (2015)

• Record Type: Journal Article
• Authors: Maleki, Mohammad Reza; Mousavi, Seyed Meysam; Amiri, Amirhossein
• Is Part Of: International journal of applied decision sciences. Volume 8:Number 2(2015)
• Page Start: 127
• Page End: 144
• Publication Date: 2015
• Subjects: linguistic form -- fuzzy set theory -- multi-attribute processes -- multilayer perceptron -- MLP neural networks -- fault detection -- fault diagnosis -- multivariate control charts -- SPC -- statistical process control -- fuzzy multinomial data -- fuzzy logic; Decision making -- Periodicals; Management science -- Periodicals; 658.403005
• Languages: English
• ISSNs: 1755-8077

Abstract

In some multivariate statistical control applications, the data of the process cannot be precise and defined linguistically in practice. Using multivariate control charts in such situations with non-precise data leads to misleading results. In this paper, a new neural network-based monitoring scheme is presented by considering fuzzy multivariate multinomial data. The proposed approach is also able to identify the attribute(s) that cause an out-of-control signal. An application example is provided to evaluate the performance of the proposed approach in detecting different shifts as well as diagnosing the out-of-control attribute quality characteristic(s). The results of applying the proposed approach in both fault detection and the fault diagnosis are satisfactory.