This article presents a novel approach to the diagnosis of unbalanced faults in a trolling motor under stationary operating conditions. The trolling motor being typically of that used as the propulsion system for an unmanned surface vehicle, the diagnosis approach is based on the use of discrete wavelet transforms as a feature extraction tool and a time-delayed neural network for fault classification. The time-delayed neural network classifies between healthy and faulty conditions of the trolling motor by analysing the stator current and vibration. To overcome feature redundancy, which affects diagnosis accuracy, several feature reduction methods have been tested, and the orthogonal fuzzy neighbourhood discriminant analysis approach is found to be the most effective method. Four faulty conditions were analysed under laboratory conditions, where one of the blades causing damage to the trolling motor is cut into 10%, 25%, half and then into full to simulate the effects of propeller blades being damaged partly or fully. The results obtained from the real-time simulation demonstrate the effectiveness and reliability of the proposed methodology in classifying the different faults faster and accurately.
In recent years, there has been a growing interest in the use of fault analysis techniques in unmanned marine vehicles (UMVs) owing to their significant impact on marine operations. This study presents a novel approach to the diagnosis of unbalanced load (blades damage) faults in an electric thruster motor in UMV propulsion systems based on orthogonal fuzzy neighbourhood discriminative analysis for feature dimensionality reduction. The diagnosis approach is based on the use of discrete wavelet transforms as a feature extraction tool and the optimal number of mother wavelet function and levels of resolution by analysing the vibration and current signals. As a result of analysis and comparisons, the Deubechies 12 (db12) wavelet and level 8 were chosen. A dynamic recurrent neural network was chosen for fault classification and level of fault severity prediction was implemented. Four faulty conditions were analysed under laboratory conditions and these were recreated by damaging the blades of a motor. The results obtained from the simulation demonstrate the effectiveness and reliability of the proposed methodology in classifying the different faults with greater speed and accuracy compared to existing methods.
This paper presents a novel approach to the diagnosis of blade faults in an electric thruster motor of unmanned underwater vehicles (UUVs) under stationary operating conditions. The diagnostic approach is based on the use of discrete wavelet transforms (DWT) as a feature extraction tool and a dynamic neural network (DNN) for fault classification. The DNN classifies between healthy and faulty conditions of the trolling motor by analyzing the stator current and vibration signals. To overcome feature redundancy, which affects diagnosis reliability, the Orthogonal Fuzzy Neighbourhood Discriminant Analysis (OFNDA) approach is found to be the most effective. Four faulty conditions were analyzed under laboratory conditions, and the results obtained from experiment demonstrate the effectiveness and reliability of the proposed methodology in classifying the different faults faster and more accurately.
The demand for electrical power has been increasing rapidly due to higher industrial output and deregulation. The concerns have been raised about the ability of distribution networks to provide adequate power for the customers with an appropriate level of quality and reliability. To ameliorate the performance of the radial distribution system (RDS), the optimal capacitor placement (OCP), and the distribution system reconfiguration (DSR) strategies have been implemented in the current work to achieve the highest power quality and system reliability in a balanced manner at the same time. Three different scenarios were implemented, the first scenario of dual sequential (OCP after DSR), the second scenario of dual sequential (DSR after OCP), and the third scenario of dual simultaneous (DSR with OCP). These scenarios were tested on typical 33 and 69 bus IEEE RDS using the binary salp swarm algorithm (BSSA) based on the multiobjective functions (MOFs), in order to identify the most effective scenario performance that achieved the highest power quality and system reliability. The MOF was formulated to improve the power quality by increasing the voltage buses and reducing the power losses. While the constraints include limits of system reliability indices to provide optimal constraints on negative interactions of power quality. The simulation results demonstrate that the second scenario of dual sequential (DSR after OCP) provide superior in comparison with the first scenario of dual sequential (OCP after DSR) for enhancing the RDS reliability indices, voltage buses, and reducing power losses. Finally, the best result can be realized with dual simultaneous (DSR and OCP) in the third scenario compared to the dual sequential scenarios.
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