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Title: Performance comparison between FFT-based segmentation, feature selection and fault identification algorithm and neural network for the condition monitoring of centrifugal equipment
Authors: Gowid, Samer S.A.A.
Dixon, Roger
Ghani, Saud
Keywords: Condition based monitoring
Condition monitoring
Feature selection
Neural network
Centrifugal equipment
Fault detection
FFT
Issue Date: 2017
Publisher: © American Society of Mechanical Engineers (ASME)
Citation: GOWID, S., DIXON, R. and GHANI, S., 2017. Performance comparison between FFT-based segmentation, feature selection and fault identification algorithm and neural network for the condition monitoring of centrifugal equipment. Journal of Dynamic Systems, Measurement, and Control, 139 (6), 061013.
Abstract: This paper compares and evaluates the performance of two major feature selection and fault identification methods utilized for the Condition Monitoring (CM) of centrifugal equipment, namely FFT-based Segmentation, Feature Selection, and Fault Identification (FS2FI) algorithm and Neural Network (NN). Mutli-Layer Perceptron is the most commonly used NN model for fault pattern recognition. Feature-selection and Trending play an important role in pattern recognition, and hence, affect the performance of CM systems. The technical and developmental challenges of both methods were investigated experimentally on a Paxton industrial centrifugal air blower system with a rotational speed of 15,650 RPM‟s. Five different machine conditions were experimentally emulated in the laboratory. A low training-to-testing ratio of 50% was utilized to evaluate the performance of both methods. In order to maximize fault identification accuracy and minimize computing time and cost, a near-optimal NN configuration was identified. The results showed that both techniques operated with a fault identification accuracy of 100%. However, the FS2FI algorithm showed a number of advantages over NN. These advantages include the ease of implementation and a reduction of cost and time in development and computing, as it processed the data from the first trial in less than 6.2% of the time taken by the neural network.
Description: This paper is in closed access until 6th Dec 2017.
Version: Accepted for publication
DOI: 10.1115/1.4035458
URI: https://dspace.lboro.ac.uk/2134/23657
Publisher Link: http://dx.doi.org/10.1115/1.4035458
ISSN: 0022-0434
Appears in Collections:Closed Access (Mechanical, Electrical and Manufacturing Engineering)

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