Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.768812
Title: Automatic fault diagnosis of centrifugal pumps using wavelet and artificial intelligence
Author: Al Tobi, Maamar Ali Saud
ISNI:       0000 0004 7655 5627
Awarding Body: Glasgow Caledonian University
Current Institution: Glasgow Caledonian University
Date of Award: 2018
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Abstract:
Centrifugal pumps are rotating machines which are widely used in process operations and other applications. Efficient and failure-free operation of these pumps is important for effective plant operation and productivity. However, continuous operation can lead to failure and maintenance requirements. Therefore, a centrifugal pump is considered in this work for the fault diagnosis and classification using Artificial Intelligence (AI) methods combined with Wavelet Transform (WT) and Genetic Algorithm (GA). The proposed approach is implemented in three stages: data acquisition stage in which the vibration signals of different pump conditions, namely a healthy condition, five mechanical faulty conditions and one hydraulic faulty condition are acquired from a centrifugal pump rig using appropriate instrumentations and software, including an accelerometer, data acquisition device and LabVIEW; the second stage is the preprocessing and feature extraction, where the signals of the different pump conditions are pre-processed and the features are extracted using WT methods, and the third stage is the pump condition classification and diagnosis in which two AI classifiers are harnessed with the involvement of GA for the optimisation and training. In this work, three types of wavelet transform are used; Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT), and Wavelet Packet Transform (WPT) are tested and applied as pre-processing methods to extract significant wavelet transform features to be used after that as input vectors for the artificial neural network system with the proper learning algorithm in order to approach an automatic detection and diagnosis of centrifugal pump faults. Two artificial intelligence systems are investigated for this study, namely, Multilayer Perceptron (MLP) and Support Vector Machine (SVM). MLP is trained using its traditional learning algorithm (Back Propagation (BP)), and also with a hybrid training method using Genetic Algorithm (GA) combined with BP. Furthermore, GA is also proposed as an optimisation method for the network architecture of MLP, and particularly for the selection of hidden layers and neurons. This approach has novel contributions starting from the centrifugal pump rig which is specifically designed and built for this project; testing and investigating three different WT methods in which WPT is applied for the first time for the centrifugal pump fault detection; and applying GA in optimisation and training of MLP is also a new application for centrifugal pumps. Furthermore, a new integrated diagnostic system is proposed for the centrifugal pumps, which can be further developed and commercialised.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.768812  DOI: Not available
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