An Approach to Reducing Input Parameter Volume for Fault Classifiers

doi:10.1007/s11633-018-1162-7

CASIA OpenIR > 学术期刊 > Machine Intelligence Research

	An Approach to Reducing Input Parameter Volume for Fault Classifiers
	Ann Smith; Fengshou Gu; Andrew D. Ball
发表期刊	International Journal of Automation and Computing
ISSN	1476-8186
	2019
卷号	16 期号:2 页码:199-212
摘要	As condition monitoring of systems continues to grow in both complexity and application, an overabundance of data is amassed. Computational capabilities are unable to keep abreast of the subsequent processing requirements. Thus, a means of establishing computable prognostic models to accurately reflect process condition, whilst alleviating computational burdens, is essential. This is achievable by restricting the amount of information input that is redundant to modelling algorithms. In this paper, a variable clustering approach is investigated to reorganise the harmonics of common diagnostic features in rotating machinery into a smaller number of heterogeneous groups that reflect conditions of the machine with minimal information redundancy. Naïve Bayes classifiers established using a reduced number of highly sensitive input parameters realised superior classification powers over higher dimensional classifiers, demonstrating the effectiveness of the proposed approach. Furthermore, generic parameter capabilities were evidenced through confirmatory factor analysis. Parameters with superior deterministic power were identified alongside complimentary, uncorrelated, variables. Particularly, variables with little explanatory capacity could be eliminated and lead to further variable reductions. Their information sustainability is also evaluated with Naïve Bayes classifiers, showing that successive classification rates are sufficiently high when the first few harmonics are used. Further gains were illustrated on compression of chosen envelope harmonic features. A Naïve Bayes classification model incorporating just two compressed input variables realised an 83.3% success rate, both an increase in classification rate and an immense improvement volume-wise on the former ten parameter model.
关键词	Fault diagnosis classification variable clustering data compression big data.
DOI	10.1007/s11633-018-1162-7
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/42331
专题	学术期刊_Machine Intelligence Research
作者单位	Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK
推荐引用方式 GB/T 7714	Ann Smith,Fengshou Gu,Andrew D. Ball. An Approach to Reducing Input Parameter Volume for Fault Classifiers[J]. International Journal of Automation and Computing,2019,16(2):199-212.
APA	Ann Smith,Fengshou Gu,&Andrew D. Ball.(2019).An Approach to Reducing Input Parameter Volume for Fault Classifiers.International Journal of Automation and Computing,16(2),199-212.
MLA	Ann Smith,et al."An Approach to Reducing Input Parameter Volume for Fault Classifiers".International Journal of Automation and Computing 16.2(2019):199-212.

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IJAC-2018-01-003.pdf（1085KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览