Advanced feature extraction and dimensionality reduction for unmanned underwater vehicle fault diagnosis

Abstract

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.

Aircraft Actuator Performance Analysis Based on Dynamic Neural Network

“…It is typical to conclude that the networks will perform consistently well for the predicted demand trends once validated on the test data. Te suggested technique in the current study has been tested with various applications [15][16][17][18] and does not require dynamic BP to compute the network gradient static MLP network. Te TDNN, such as other neural networks, operates on multiple interconnected layers of perceptron and is implemented as a feed-forward neural network in which all neurons (at each layer) receive input from neuron outputs, where x (t) and y (t) are the input and output, respectively.…”

Underwater propeller fault diagnosis based on deep learning

“…At present, the methods for diagnosing underwater robot thruster faults can be mainly divided into three categories: qualitative analysis diagnosis methods, analytical model diagnosis methods and signal processing diagnosis methods [3] . Wang Xuan [4] et al proposed a fault diagnosis method based on convolutional neural network and sliding window. Wathiq Abed [5] et al proposed a new method for diagnosing unmanned underwater vehicle (UUV) electric propeller blade faults under stationary operating conditions.…”

Fault diagnosis in autonomous underwater vehicle propeller in the transition stage based on GP-RPF

“…With the help of sensors, the AUV’s fault is diagnosed through the analysis of its status; for example, Abed et al 1 analysed the vibration and current signals to derive fault severity prediction regarding damage of the blades and Filaretov et al 2 applied data fusion on AUV fault detection and localization. Chu and Zhang 3 reconstructed the fault with the help of a terminal sliding observer.…”