A comparative study of maintenance data classification based on neural networks, logistic regression and support vector machines

Abstract

Purpose

The purpose of this paper is to compare the effectiveness of different analytical approaches, namely artificial neural networks, logistic regression and support vector machines to assess the health of a strainer located at the suction side of the pump.

Design/methodology/approach

Data used for simulation included exemplars from clean (represented by datasets after cleaning the suction strainer) and faulty conditions (represented by datasets prior to cleaning the suction strainer). The same datasets were used for modeling in order to compare how different techniques perform when fed with the same information.

Findings

Principal component analysis‐based artificial neural networks proved to be better than other techniques in classifying maintenance datasets and predicting flow resistance from a clogged suction strainer.

Originality/value

The work highlights the comparative effectiveness of three predictive analytical techniques in classifying real plant data from a suction strainer. This will provide an opportunity for maintenance experts to see the effectiveness of different techniques as well as revealing valuable information about the relationship between the condition of the suction strainer and the overall performance of the pump.

Citation

Raza, J., Liyanage, J.P., Al Atat, H. and Lee, J. (2010), "A comparative study of maintenance data classification based on neural networks, logistic regression and support vector machines", Journal of Quality in Maintenance Engineering, Vol. 16 No. 3, pp. 303-318. https://doi.org/10.1108/13552511011072934