Researching | Fault diagnosis of marine electric thruster gearbox based on MPDCNN under strong noisy environments

Chinese Journal of Ship Research, Volume. 20, Issue 2, 30(2025)

Fault diagnosis of marine electric thruster gearbox based on MPDCNN under strong noisy environments

Qianming SHANG, Wanying JIANG, Yi ZHOU, Zhengqiang WANG, and Yubo SUN

Author Affiliations

School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China

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References(11)

[1] MIAO Y H, ZHANG B Y, YI Y G et al. Application of improved reweighted singular value decomposition for gearbox fault diagnosis based on built-in encoder information[J]. Measurement, 168, 108295(2021).

[6] JIA F, LEI Y G, LIN J et al. Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data[J]. Mechanical Systems and Signal Processing, 72-73, 303-315(2016).

[8] WANG Z J, ZHAO W L, DU W H et al. Data-driven fault diagnosis method based on the conversion of erosion operation signals into images and convolutional neural network[J]. Process Safety and Environmental Protection, 149, 591-601(2021).

[9] JANSSENS O, SLAVKOVIKJ V, VERVISCH B et al. Convolutional neural network based fault detection for rotating machinery[J]. Journal of Sound and Vibration, 377, 331-345(2016).

[10] EREN L, INCE T, KIRANYAZ S. A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier[J]. Journal of Signal Processing Systems, 91, 179-189(2019).

[12] ZHAO L, HUANG D R, QIN Y. Fault diagnosis for gearbox based on improved empirical mode decomposition[J]. Shock and Vibration, 2015, 215806(2015).

[14] XUE L Y, JIANG Z Y, FU T T et al. Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis[J]. European Radiology, 30, 2973-2983(2020).

[16] LI G Q, WU J, DENG C et al. Parallel multi-fusion convolutional neural networks based fault diagnosis of rotating machinery under noisy environments[J]. ISA Transactions, 128, 545-555(2022).

[19] ABDUL Z K, AL-TALABANI A K, RAMADAN D O. A hybrid temporal feature for gear fault diagnosis using the long short term memory[J]. IEEE Sensors Journal, 20, 14444-14452(2020).

[20] HAN H R, WANG H, LIU Z L et al. Intelligent vibration signal denoising method based on non-local fully convolutional neural network for rolling bearings[J]. ISA Transactions, 122, 13-23(2022).

[21] JIA L S, CHOW T W S, YUAN Y X. GTFE-Net: a Gramian time frequency enhancement CNN for bearing fault diagnosis[J]. Engineering Applications of Artificial Intelligence, 119, 105794(2023).

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Qianming SHANG, Wanying JIANG, Yi ZHOU, Zhengqiang WANG, Yubo SUN. Fault diagnosis of marine electric thruster gearbox based on MPDCNN under strong noisy environments[J]. Chinese Journal of Ship Research, 2025, 20(2): 30

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Paper Information

Category: Ship Intelligent O&M, and Fault Diagnosis

Received: Mar. 8, 2024

Accepted: --

Published Online: May. 15, 2025

The Author Email:

DOI:10.19693/j.issn.1673-3185.03814

Topics

laser devices and laser physics

Lasers and Laser Optics

laser manufacturing

Instrumentation, Measurement and Metrology