Neural network fault diagnosis of a trolling motor based on feature reduction techniques for an unmanned surface vehicle

Abed, Wathiq; Sharma, Sanjay; Sutton, Robert I.

doi:10.1177/0959651815581095

Cited by 13 publications

(11 citation statements)

References 33 publications

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“…A fully connected layer (FCN) was used to predict the motor conditions. Abed et al [31] used different blade-breaking conditions to monitor a trolling motor. A discrete wavelet transform was used to extract the features from the current and vibration signals.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning

Tsai

Wang

Chung

et al. 2021

Sensors

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With the rapid development of unmanned surfaces and underwater vehicles, fault diagnoses for underwater thrusters are important to prevent sudden damage, which can cause huge losses. The propeller causes the most common type of thruster damage. Thus, it is important to monitor the propeller’s health reliably. This study proposes a fault diagnosis method for underwater thruster propellers. A deep convolutional neural network was proposed to monitor propeller conditions. A Hall element and hydrophone were used to obtain the current signal from the thruster and the sound signal in water, respectively. These raw data were fast Fourier transformed from the time domain to the frequency domain and used as the input to the neural network. The output of the neural network indicated the propeller’s health conditions. This study demonstrated the results of a single signal and the fusion of multiple signals in a neural network. The results showed that the multi-signal input had a higher accuracy than the one-signal input. With multi-signal inputs, training two types of signals with a separated neural network and then merging them at the end yielded the best results (99.88%), as compared to training two types of signals with a single neural network.

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Section: Introductionmentioning

confidence: 99%

Multi-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning

Tsai

Wang

Chung

et al. 2021

Sensors

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“…There are currently four kinds of fault diagnosis methods using deep learning, such as deep neural network (DNN)-based methods, deep belief network (DBN)-based methods, convolutional neural network (CNN)-based methods and long short-term memory (LSTM) neural networks-based methods. [9][10][11][12][13][14][15] Different from DNN and DBN, CNN can well extract local feature successively using convolution layer and pooling layer. LSTM does well in sequence feature extraction by designing forget gate.…”

Section: Introductionmentioning

confidence: 99%

Real-time fault diagnosis using deep fusion of features extracted by parallel long short-term memory with peephole and convolutional neural network

Zhou

Zhang

Chen

2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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Analysis of one-dimensional vibration signals is the most common method used for safety analysis and health monitoring of rotary machines. How to effectively extract features involved in one-dimensional sequence data is crucial for the accuracy of real-time fault diagnosis. This article aims to develop more effective means of extracting useful features potentially involved in one-dimensional vibration signals. First, an improved parallel long short-term memory called parallel long short-term memory with peephole is designed by adding a peephole connection before each forget gate to prevent useless information transferring in the cell. It can not only solve the memory bottleneck problem of traditional long short-term memory for long sequence but also can make full use of all possible information helpful for feature extraction. Second, a fusion network with new training mechanism is designed to fuse features extracted from parallel long short-term memory with peephole and convolutional neural network, respectively. The fusion network can incorporate two-dimensional screenshot image into comprehensive feature extraction. It can provide more accurate fault diagnosis result since two-dimensional screenshot image is another form of expression for one-dimensional vibration sequence involving additional trend and locality information. Finally, real-time two-dimensional screenshot image is fed into convolutional neural network to secure a real-time online diagnosis which is the primary requirement of the engineers in health monitoring. Validity of the proposed method is verified by fault diagnosis for rolling bearing and gearbox.

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“…10 Fault diagnosis method was proposed based on discrete wavelet transformation and neural network. 11 A thruster fault diagnosis method was proposed based on particle filter, 12 where thrusters' abnormal cases were described using a switching-mode Markov model. Zhu and Sun 5 investigated the jammed thruster fault diagnosis method based on information fusion.…”

Section: Introductionmentioning

confidence: 99%

Weak thruster fault detection for autonomous underwater vehicle based on artificial immune and signal pre-processing

Yao

Wang

Zhang

2018

Advances in Mechanical Engineering

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This article investigates weak thruster fault detection problem for autonomous underwater vehicle subject to the external disturbances. A weak thruster fault detection method is developed based on the combination of artificial immune system and single pre-processing. The number of measurable autonomous underwater vehicle's signals is limited and the weak thruster fault is easy to be compensated by the closed-loop control system. In this developed method, signal preprocessing is first used in the original autonomous underwater vehicle signals, including signal transformation, data fusion based on Dempster-Shafer evidence theory, and feature extraction based on isometric mapping algorithm, to reduce the external disturbance effect and prominently reflect thruster fault. Then artificial immune system is applied to conduct anomaly detection based on the processed autonomous underwater vehicle signals. In the process of fault detection, it includes two stages, training stage and testing stage. In training stage, the detectors are generated at first based on the autonomous underwater vehicle signals in healthy condition based on negative selection and positive selection. And then in testing stage, the generated detectors are used to detect whether fault occurs or not based on the principle of self and non-self discrimination. Finally, to evaluate the performance of the developed fault detection method, Beaver 2 autonomous underwater vehicle is used to conduct experiments. The comparative results demonstrate the effectiveness of the developed method.

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Neural network fault diagnosis of a trolling motor based on feature reduction techniques for an unmanned surface vehicle

Cited by 13 publications

References 33 publications

Multi-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning

Multi-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning

Real-time fault diagnosis using deep fusion of features extracted by parallel long short-term memory with peephole and convolutional neural network

Weak thruster fault detection for autonomous underwater vehicle based on artificial immune and signal pre-processing

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