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AuthorGowid, Samer
AuthorDixon, Roger
AuthorGhani , Saud
Available date2020-09-03T11:40:58Z
Publication Date2017
Publication NameJournal of Dynamic Systems, Measurement and Control, Transactions of the ASME
ResourceScopus
URIhttp://dx.doi.org/10.1115/1.4035458
URIhttp://hdl.handle.net/10576/15944
AbstractThis 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 fast Fourier transform (FFT)-based segmentation, feature selection, and fault identification (FS2FI) algorithm and neural network (NN). Multilayer perceptron (MLP) 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 RPMs. 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 NN.
Languageen
PublisherAmerican Society of Mechanical Engineers (ASME)
Subjectcentrifugal equipment
condition monitoring
condition-based monitoring
fast Fourier transform
fault detection
feature selection
neural network
TitlePerformance Comparison Between Fast Fourier Transform-Based Segmentation, Feature Selection, and Fault Identification Algorithm and Neural Network for the Condition Monitoring of Centrifugal Equipment
TypeArticle
Issue Number6
Volume Number139
dc.accessType Abstract Only


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