Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network

Guo, Li-Hong; Yang, Lai-Ming; Cheng, Junsheng; Guo, Yong

doi:10.1155/2022/5005263

Cited by 5 publications

(6 citation statements)

References 30 publications

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“…Recently, intelligent assessment methods based on machine learning [17] [18] have been used for crane safety assessments. For instance, Guo et al [19] developed a fault identification model for low-speed crane hub bearings based on wideband mode decomposition and a backpropagation neural network. However, machine-learning methods require numerous learning samples and are primarily used for fault diagnosis and identification of lifting mechanical parts.…”

Section: Literature Review On Crane Safety Evaluationmentioning

confidence: 99%

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Crane Operation Hazard Evaluation Based on Z-Number and CPT

2024

IEEE Access

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Crane operation hazard evaluation plays an important role in theo prevention of accidents and casualties. Therefore, formulating effective measures to ensure the safety of cranes is vital. However, most studies on crane safety evaluation overlook the risk attitudes of decision makers. To overcome the limitations of the current research, this study proposes a novel risk prioritization method based on the Znumber and cumulative prospect theory (CPT) for crane operation hazard evaluation. First, considering the uncertainty and unreliability of decision makers' assessments, the Z-number is utilized to capture the decision information with regard to crane operation hazards. Second, to consider decision makers' psychological behavior in crane operation hazard evaluation, CPT is used in the Z-environment to rank the identified hazards. Third, grade discrimination rules and cumulative prospect values are integrated to classify the danger level of a hazard. Finally, a case study involving a crane operation hazard evaluation based on the Z-number and CPT is employed to verify the practicability of the proposed model. Sensitivity analyses and comparative studies are conducted to validate the proposed method.

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Section: Literature Review On Crane Safety Evaluationmentioning

confidence: 99%

“…In Table V, the decision matrix for hazards is presented as R. The risk factors (O, S, and D) were considered as cost indices. In the next step, the decision matrix of the hazards was normalized using (19). For example, the normalized rating for H1 for risk factor S is given as follows.…”

Section: ) Normalize Decision-making Matrixmentioning

confidence: 99%

Crane Operation Hazard Evaluation Based on Z-Number and CPT

2024

IEEE Access

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“…With the development of machine learning, BP neural network, SVM, and DBN are applied to intelligent fault classification of bearings [18][19][20]. Guo et al [21] extracted fault information using the modulated broadband mode decomposition method. Inputting fault information into BP neural network to diagnose the fault of low-speed hub bearing.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Based on Optimized Feature Mode Decomposition and Improved Deep Belief Network

Jia,

Meng,

Qin

2024

SDHM

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The vibration signals of rolling bearings exhibit nonlinear and non-stationary characteristics under the influence of noise. In intelligent fault diagnosis, unprocessed signals will lead to weak fault characteristics and low diagnostic accuracy. To solve the above problem, a fault diagnosis method based on parameter optimization feature mode decomposition and improved deep belief networks is proposed. The feature mode decomposition is used to decompose the vibration signals. The parameter adaptation of feature mode decomposition is implemented by improved whale optimization algorithm including Levy flight strategy and adaptive weight. The selection of activation function and parameters is crucial in the application of deep belief networks. The improved deep belief networks are proposed based on Hard swish activation function and the improved whale optimization algorithm. The optimized diagnosis model is applied to weak fault diagnosis of bearings. The experimental results show that this method has faster convergence speed and anti-noise performance under the interference of −5 db noise, and effectively improves the adaptive feature extraction ability of the model and the accuracy of fault diagnosis. Under different conditions, the average accuracy reached 97.8% and 97.6%, with good generalization performance.

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“…In addition, some scholars proposed a damage prediction method based on the "natural frequency change square ratio," which achieved better results than using the frequency alone. Guo et al [3] used a BP neural network combined with a modulated broadband mode decomposition (MBMD) method to monitor crane-bearing parts and concluded that the BP neural network has good performance in feature extraction and fault recognition. Rastin et al [4] presented a novel two-stage technique based on generative adversarial networks (GANs) for unsupervised structural health monitoring and damage identifcation.…”

Section: Introductionmentioning

confidence: 99%

Research on Vibration Fatigue Damage Locations of Offshore Oil and Gas Pipelines Based on the GA-Improved BP Neural Network

Xie

Gao

Wang

et al. 2023

Shock and Vibration

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To study vibration fatigue damage localization of offshore oil and gas pipelines, aiming at the location error caused by uncertainty of the initial parameters in backpropagation (BP) neural network training, an improved BP neural network based on the genetic algorithm (GA) is proposed to locate pipeline damage. This approach was verified by experiments and simulations. First, a BP neural network for structural damage location was constructed, and a method to optimize the BP neural network parameters based on the GA was established. Then, a finite element model was established based on the measured data from modal tests of a physical pipeline model, and a large number of vibration neural network training samples were obtained using the finite element model. Finally, to show that the improved BP neural network based on the GA had a better damage location accuracy, the location results of the original BP and GA BP methods were compared for cases with no noise and with 5% noise. The results show that the average error of the BP neural network based on the GA was less than 3%, which was 11.6% lower than that of the original BP neural network.

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Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network

Cited by 5 publications

References 30 publications

Crane Operation Hazard Evaluation Based on Z-Number and CPT

Crane Operation Hazard Evaluation Based on Z-Number and CPT

Bearing Fault Diagnosis Based on Optimized Feature Mode Decomposition and Improved Deep Belief Network

Research on Vibration Fatigue Damage Locations of Offshore Oil and Gas Pipelines Based on the GA-Improved BP Neural Network

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