Degradation feature extraction of quay side crane lifting gearbox based on Katz fractal dimensional-Singular value decomposition

Wang, Ming; Wang, Bing

doi:10.1109/wcmeim54377.2021.00087

Cited by 1 publication

(2 citation statements)

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“…Due to the unique advantages of stable transmission performance and high transmission efficiency, gearboxes are widely used in key transmission systems of various machinery, such as wind turbines automobile sectors, machining machines, robots, quayside container cranes (QCCs), etc. [1][2][3] As the key mechanical equipment of cargo transportation ports, the QCC is specially used for loading and unloading. 2 The QCC is located in a harsh working environment, which increases the failure rate of gearboxes that bears a huge dynamic load for a long time.…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3] As the key mechanical equipment of cargo transportation ports, the QCC is specially used for loading and unloading. 2 The QCC is located in a harsh working environment, which increases the failure rate of gearboxes that bears a huge dynamic load for a long time. Typical common gear faults include gear wear, tooth root crack, gear fatigue and tooth breaking.…”

Section: Introductionmentioning

confidence: 99%

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Robust fault diagnosis of quayside container crane gearbox based on 2D image representation in frequency domain and CNN

Zhang

Qin

et al. 2023

Structural Health Monitoring

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To accurately diagnose the quayside container crane (QCC) gearbox faults, this article proposes a method that combines the frequency-domain Markov transformation field (FDMTF) and multi-branch residual convolutional neural network (MBRCNN). Firstly, the gearbox vibration signal is converted into the frequency domain to reveal the components and amplitude of signals stably and concisely. Then, the one-dimensional frequency signal is encoded into the two-dimensional image by the Markov transformation field to capture the dynamic characteristics of signals. Thirdly, the MBRCNN network is constructed, which can extract multi-scale features and alleviate the problems caused by the deep network structure. Finally, the FDMTF image is fed into the constructed MBRCNN model for pattern recognition. The effectiveness of the proposed FDMTF–MBRCNN method is verified by two case studies. In Case 1, the diagnosis results of a benchmark dataset achieve 100% accuracy, better than seven state-of-the-art methods published in recent 3 years. In Case 2, the diagnosis results of the dataset collected from a 1:4 scaled test rig achieve 98.85% accuracy, better than eleven encoding methods and four convolutional neural network methods. It also can obtain a recognition accuracy of more than 94% under the conditions of small sample, different network hyper-parameters, or variable loads, which verifies its robustness. These case studies show that the FDMTF–MBRCNN method is expected to be applied to the actual fault diagnosis of QCC gearboxes.

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