Information Fusion Fault Diagnosis Method for Deep-Sea Human Occupied Vehicle Thruster Based on Deep Belief Network

Zhu, Daqi; Cheng, Xiuzhen; Yang, Lei; Chen, Yunsai; Yang, Simon X.

doi:10.1109/tcyb.2021.3055770

Cited by 39 publications

(10 citation statements)

References 27 publications

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“…In this system, the eigenvector of the largest eigenvalue corresponding to the judgment evaluation matrix is normalized to reflect the relative importance ranking [24]. Although this construction can reduce the interference of other factors and objectively reflect the difference of influence, it will inevitably lead to a certain degree of misclassification in the process of English feature recognition, which may lead to inaccurate results [25]. If the misclassification factor with low probability is not considered, when the solution value of the objective function is the smallest.…”

Section: Construction Of English Feature Recognition Evaluation Function Based On Deep Belief Neural Network Classificationmentioning

confidence: 99%

[Retracted] Feature Recognition of English Based on Deep Belief Neural Network and Big Data Analysis

Liu

2021

Computational Intelligence and Neuroscience

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Realizing accurate recognition of Chinese and English information is a major difficulty in English feature recognition. Based on this difficulty, this paper studies the English feature recognition model based on deep belief network classification algorithm and Big Data analysis. First, the basic framework based on deep belief network classification algorithm and Big Data analysis is proposed. Combined with the Big Data analysis training model, the English feature information is processed. Through the recognition of different English text features, the recognition and matching of English features are realized. Then the errors of deep belief network classification algorithm and Big Data analysis are evaluated. Second, this paper describes the quantitative evaluation of deep belief network classification algorithm and Big Data analysis in this system. In the evaluation, the language feature evaluation method is used to improve the evaluation function. At the same time, the deep belief network classification algorithm and Big Data analysis are used to self-study the model, and the English feature recognition method with strong applicability is established. Finally, the effectiveness of the recognition system is verified by the experiment.

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Section: Construction Of English Feature Recognition Evaluation Function Based On Deep Belief Neural Network Classificationmentioning

confidence: 99%

[Retracted] Feature Recognition of English Based on Deep Belief Neural Network and Big Data Analysis

Liu

2021

Computational Intelligence and Neuroscience

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“…In iteration, a softmax classifier is superimposed on the rightmost layer to classify different health conditions according to the mined features. Compared with shallow neural networks, deeper stacked networks [6] are better in feature extraction and have obvious advantages in processing high-dimensional data. The stacked network structure is shown in figure 1.…”

Section: Stacked Normalized Sparse Autoencodermentioning

confidence: 99%

Diagnosis of Interturn Short Circuit of Permanent Magnet Synchronous Motor Based on Stacked Normalized Sparse Autoencoder

Chen

Li²

2022

J. Phys.: Conf. Ser.

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Regarding the problems such as the traditional auto-encoders’ tendency to learn the similar features during the feature extraction and limited capability of feature learning in the shallow network models, the paper puts forward an interturn short circuit diagnosis method for Permanent Magnet Synchronous Motor (PMSM) based on the Stacked Normalized Sparse Autoencoder (SNSAE). The method is to guarantee the representativeness of the extracted features, acquire stronger anti-interference capability, and is more suitable for training diversified and large-capacity mechanical equipment failure data. Four types of Inter-Turn Short-Circuit (ITSC) fault data were collected on the PMSM fault diagnosis experimental platform for testing. The experimental outcome suggests that the SNSAE algorithm can effectively extract the fault features, and the fault diagnosis effect is better than the traditional intelligent diagnosis method, and its diagnosis accuracy is as high as 98.91%.

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“…Zhu et al [14] imported Deep Belief Network (DBN) into the multi-sensor information fusion model to identify some unfamiliar fault modes in rotating machinery in an engineering environment and achieved good diagnostic results. Xu et al [15] developed a hybrid DL model based on the Convolutional Neural Network (CNN) and multi-Grained Cascade Forest (GC-Forest).…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Imbalanced Data of Rotating Machinery Based on Time Domain Signal Prediction and SC-ResNeSt

et al. 2023

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In an actual engineering environment, some rotating machines are usually in normal operation, but their time in a fault state is very short, which leads to a serious imbalance in the fault diagnosis datasets for rotating machinery, and gives the traditional network model the shortcomings of poor stability and low accuracy in practical engineering applications. To solve this problem, we propose a fault diagnosis method based on the combination of a new Dual-stage Attention-based Recurrent Neural Network (DA-RNN) and depth residual dispersion self-calibration convolution network (SC-ResNeSt). Firstly, a novel DA-RNN network with a gated cycle unit (GRU) as a coding-decoding unit was designed, and the network was used to predict and expand the scarce fault signals. Secondly, to make full use of the time domain information of vibration signals, a new image coding method, namely, Gram Angle Product Field (GAPF), was proposed. Then, because the traditional convolution layer lacks a dynamic receptive field to extract more representative features, self-calibrated convolution modules were introduced on the basis of the distraction network (ResNeSt), and a new network model, SC-ResNeSt, was established. Finally, the expanded vibration signal is converted into GAPF, which is used as the input for the SC-ResNeSt network to classify the fault types. To check the performance of the model, the Case Western Reserve University rolling bearing dataset and planetary gearbox dataset were used for testing. Ultimately, good results were obtained in a prediction experiment for bearing fault samples and a subsequent fault diagnosis experiment for bearings and gears, which verified the feasibility and practicability of the model.

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Information Fusion Fault Diagnosis Method for Deep-Sea Human Occupied Vehicle Thruster Based on Deep Belief Network

Cited by 39 publications

References 27 publications

[Retracted] Feature Recognition of English Based on Deep Belief Neural Network and Big Data Analysis

[Retracted] Feature Recognition of English Based on Deep Belief Neural Network and Big Data Analysis

Diagnosis of Interturn Short Circuit of Permanent Magnet Synchronous Motor Based on Stacked Normalized Sparse Autoencoder

Fault Diagnosis Method for Imbalanced Data of Rotating Machinery Based on Time Domain Signal Prediction and SC-ResNeSt

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