A Fault Diagnosis Model for Complex Industrial Process Based on Improved TCN and 1D CNN

WANG, Mingsheng; Huang, Bo; HE, Chuanpeng; LI, Peipei; Zhang, Jiahao; Chen, Yu; TONG, Jie

doi:10.1051/wujns/2022276453

Cited by 5 publications

(4 citation statements)

References 35 publications

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“…Therefore, the fitness function and the speed update formula are the core of the PSO algorithm and the determinants of the optimal solution. In this article, the fitness function will be constructed by energy method, 34 mean absolute error (MAE), mean absolute percentage error (MAPE), and running time to form the best fitness function. The speed update formula is improved through the fitness function value to speed up the search for the optimal solution.…”

Section: Proposed Methodsmentioning

confidence: 99%

Multiscale ultra‐short‐term wind power prediction model based on GD‐IFEM‐PSO and VMD‐BP

Zhang,

Wang,

et al. 2023

Energy Science & Engineering

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Wind power prediction enables advance prediction of the future capacity of wind farms to improve production, increase capacity and reduce costs, however, wind power generation data are highly unstable, making it difficult to achieve high‐precision prediction. Signal decomposition methods and optimization algorithms allow for data smoothing and optimization of parameter settings, but the limitations of parameter dependency and the slow optimization search process prevent existing research from being applied in a practical setting. To address these issues, this article proposes a multiscale ultra‐short‐term wind power prediction model based on particle swarm optimization based on greedy dynamic and integrated fitness evaluation method (GD‐IFEM‐PSO) and variational mode decomposition and back propagation network (VMD‐BP). First, this article improves the velocity update formula in the particle swarm optimization algorithm by the proposed GD weight and sets up a fitness function using multiple evaluation metrics by the proposed integrated fitness evaluation method (IFEM), so that the improved particle swarm optimization algorithm achieves high efficiency in the optimization search while ensuring the comprehensiveness of the evaluation. Second, the variable mode decomposition (VMD) decomposition algorithm is used to decompose the historical wind power data to achieve smoothing of the wind power data, and the improved particle swarm optimization algorithm is used to optimize the K and ⍺ values in the VMD decomposition algorithm to improve the comprehensive performance of the decomposition. Then, to achieve data reduction and fast model training, the components are divided into trend components, low‐frequency vibration components, high‐frequency vibration components, and random noise components according to the central frequency, so that the model can better grasp the data trend while reducing the number of components and achieving high prediction accuracy. Finally, the different components are predicted by the BP neural network and the predicted values of the different categories of components are reconstructed into the final wind power prediction values. To demonstrate the rationality and progressiveness of the proposed model, several models are compared on several data sets, and the results show that the proposed model has faster prediction speed and higher prediction performance, and is more suitable for application in real‐world environments.

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Section: Proposed Methodsmentioning

confidence: 99%

Multiscale ultra‐short‐term wind power prediction model based on GD‐IFEM‐PSO and VMD‐BP

Zhang,

Wang,

et al. 2023

Energy Science & Engineering

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“…where O is the final output features matrix; Q, K, and V are the query matrix, the key matrix, and the value matrix, respectively; Softmax is the activation function; W Q , W K, and W V are learnable weight matrices. Secondly, the reinforcement unit also introduces the idea of dilated convolution [25,26], which belongs to the temporal convolutional network (TCN) model, to further expand the receptive field. The feature matrix containing local feature information and global feature information is fed into the TCN model to obtain a new feature matrix containing local feature information and global feature information between non-adjacent features.…”

Section: Reinforcement Unitmentioning

confidence: 99%

Anomaly detection of industrial motors under few-shot feature conditions based on causality

Cen¹,

Li²,

Cen³

et al. 2023

Meas. Sci. Technol.

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It is observed that previous research studies focusing on few-shot feature data for fault diagnosis or anomaly detection have a limitation, that is, feature extraction methods to solve few-shot feature data problems will also have scenarios where they may not always be applicable. In this paper, a motor anomaly detection model with generalization performance is proposed to meet the anomaly detection needs in the above scenarios. The model consists of a reinforcement unit and a diagnosis unit. Firstly, the reinforcement unit extracts the adjacent features with different timestamps through ensemble learning. Secondly, the temporal convolutional network (TCN) model is nested to increase the receptive field of the reinforcement unit. Additionally, a residual network is introduced to improve the generalization performance. Finally, features obtained from the reinforcement unit are used for final anomaly detection through neural networks in the diagnosis unit. Experimental results indicate that the proposed model achieve an anomaly detection accuracy of 97.96% in factory motor dataset, while the model has the superior generalization ability.

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“…Moreover, before using the depth model method, the error correction in the text to be corrected will further improve the error correction effect of the depth model, which has achieved a very good effect in the field of judicial document error correction. Inspired by the above, in the field of Chinese text error correction, this paper uses BERT model [16] and combines Temporal Convolutional Network (TCN [17] ) to propose a Chinese error correction method.…”

Section: Related Workmentioning

confidence: 99%

CTC_BERT: a chinese text correction model with multi-scale semantic feature caption

Wang,

Li,

et al. 2023

Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023)

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Text correction aims to determine whether natural language text contains grammatical errors, text errors, etc., and correct sentences. Previous work usually adopts byte pair encoding (BPE), which may lead to semantically related Chinese characters being separated. In addition, the previous models can only extract the superficial semantic features, but cannot capture the global deep semantic relationships. In this paper, we introduce the time convolutional network (TCN) to capture multi-scale semantic information, so as to promote the development of global semantic information. The CTC_BERT model uses the synonym masking strategy to reduce the semantic segmentation of related words and adds a fully connection layer as the error detection layer. In order to verify the performance of the CTC_BERT model, a comparison experiment has been carried out on SIGHAN2015+Wang271K Chinese error correction dataset. The results show that the accuracy of this model can reach 81.4%, which is better than that of BERT, BART, ConvSeq2Seq and other conventional models, and effectively improves the performance of text error correction.

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A Fault Diagnosis Model for Complex Industrial Process Based on Improved TCN and 1D CNN

Cited by 5 publications

References 35 publications

Multiscale ultra‐short‐term wind power prediction model based on GD‐IFEM‐PSO and VMD‐BP

Multiscale ultra‐short‐term wind power prediction model based on GD‐IFEM‐PSO and VMD‐BP

Anomaly detection of industrial motors under few-shot feature conditions based on causality

CTC_BERT: a chinese text correction model with multi-scale semantic feature caption

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