Abstract
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.
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Ann Smith received the M. Sc. degree in applied statistics from University of Hud-dersfield, UK in 1997, the Ph. D. degree in engineering from University of Huddersfield, UK in 2017. She began lecturing in theoretical and applied mathematics and statistics to a wide range of students across the University in 1992 following her award of PGCE(FE) (postgraduate certificate in education, higher education). She is a Fellow of the HEA (the Higher Education Academy) and IMA (the Institute of Mathematics and its Applications.).
Her research interests include condition monitoring, model-ling system behaviours, predictive analytics for applied process monitoring, non-linear systems approaches to detection of deviant events, autonomous abnormality assessment and evidence based health care.
Fengshou Gu received the B. Sc. and M. Sc. degrees in mechanical engineering from Taiyuan University of Technology, China in 1985, and the Ph. D. degree in mechanical engineering from University of Manchester, UK in 2009. He is a professor in diagnostic engineering, working as the head of Measurement and Data Analytics Research Group (MDARG) and the deputy director of the Centre for Efficiency and Performance Engineering (CEPE), University of Huddersfield, UK.
His research interests include machine dynamics, tribology dynamics, advanced signal processing, measurement system and sensor development, artificial intelligence and related fields.
Andrew D. Ball received the B. Sc. de-gree in mechanical engineering from Uni-versity of Leeds, UK in 1987, his degree having been sponsored by BICC Electronic Cables. He went on to work for Ruston Gas Turbines and then gained a sponsor-ship from WM Engineering and the Royal Navy, enabling him to join the Total Tech-nology Scheme at University of Manchester, UK, from which he graduated in 1991 with a Ph. D. in Machinery Condition Monitoring. He took the shell sponsored lectureship in maintenance engineering at University of Manchester, UK in 1991 and was promoted to professor of maintenance engineering in 1999. He was the head of School of the Manchester School of Engineering from 2003 to 2004, and he became dean of Graduate Education in 2005. In late 2007, he moved to University of Huddersfield, UK as professor of diagnostic engineering and pro-vice-chancellor for research and enterprise. His personal research expertise is in the detection and diagnosis of faults in mechanical, electrical and electro-hydraulic machines, in data analysis and signal processing, and in measurement systems and sensor development. He is the author of over 300 technical and professional publications, and he has spent a large amount of time lecturing and consulting to industry in all parts of the world. Andrew has to date graduated almost 100 research degrees, in the fields of mechanical, electrical and diagnostic engineering. He has acted as external examiner at over 30 overseas institutions, he holds visiting and honorary positions at 4 overseas universities, he sits on 3 large corporate scientific advisory boards, and he is also a registered expert witness in 3 countries.
His research interests include machinery condition monitoring, vibration analysis, signal processing, fault detection and acoustics.
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Smith, A., Gu, F. & Ball, A.D. An Approach to Reducing Input Parameter Volume for Fault Classifiers. Int. J. Autom. Comput. 16, 199–212 (2019). https://doi.org/10.1007/s11633-018-1162-7
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DOI: https://doi.org/10.1007/s11633-018-1162-7