A Novel 1D-Convolutional Spatial-Time Fusion Strategy for Data-Driven Fault Diagnosis of Aero-Hydraulic Pipeline Systems

Yang, Tongguang; Li, Guanchen; Wang, Tongyu; Yuan, Sen; Yang, Xin; Yu, Xiaoli; Han, Qingkai

doi:10.3390/math11143113

Cited by 12 publications

(4 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the actual industrial scene, the pipeline was disturbed by strong noise, which leaded to a very complex failure mechanism. An aeroengine hydraulic pipeline diagnosis strategy based on onedimensional convolutional space-time fusion was proposed [83]. Firstly, 1DCNN was improved to expand the perceptual input field and extract richer short sequence features effectively.…”

Section: Feature-level Fusionmentioning

confidence: 99%

Intelligent fault diagnosis methods for hydraulic components based on information fusion: review and prospects

Guan,

Ren,

Tang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Hydraulic component faults have the characteristics of nonlinear time-varying signal, strong concealment, and difficult feature extraction, etc. Timely and accurately fault diagnosis of hydraulic components is helpful to curb economic losses and accidents, so researches have carried out a lot of research on hydraulic components. Information fusion technology can combine multi-source data from multiple dimensions to mine fault data characteristics, which effectively improves the accuracy and reliability of fault diagnosis results. However, there is currently a lack of a comprehensive and systematic review in this domain. Therefore, in this paper, the hydraulic components information fusion fault diagnosis technologies are summarized and analyzed, including the main process information fusion fault diagnosis and the research status of information fusion fault diagnosis of hydraulic system. The methods and techniques involved in the fusion process, data source and fusion method of fault diagnosis of hydraulic components information fusion are described and summarized. The problems of information fusion in fault diagnosis of hydraulic components are analyzed, the solutions are discussed, and the research ideas of improving information fusion fault diagnosis are put forward. Finally, digital twin technology is introduced, and the advantages and research status of intelligent fault diagnosis based on digital twin are summarized. On this basis, the intelligent fault diagnosis of hydraulic components based on information fusion is summarized, and the challenges and future research ideas of applying information fusion and digital twin to intelligent fault diagnosis of hydraulic components are put forward and analyzed comprehensively.

show abstract

Section: Feature-level Fusionmentioning

confidence: 99%

Intelligent fault diagnosis methods for hydraulic components based on information fusion: review and prospects

Guan,

Ren,

Tang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…The Gated Recurrent Unit network (GRU) is a cyclic neural network RNN that solves the loss of long-distance information [30][31][32][33]. It is mostly used to process timing information and solve the problems of gradient explosion and gradient disappearance in the RNN structure.…”

Section: Bigru-dnn Modulementioning

confidence: 99%

An Intrusion Detection Method Based on Hybrid Machine Learning and Neural Network in the Industrial Control Field

Sun,

Zhang,

Jin

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Aiming at the imbalance of industrial control system data and the poor detection effect of industrial control intrusion detection systems on network attack traffic problems, we propose an ETM-TBD model based on hybrid machine learning and neural network models. Aiming at the problem of high dimensionality and imbalance in the amount of sample data in the massive data of industrial control systems, this paper proposes an IG-based feature selection method and an oversampling method for SMOTE. In the ETM-TBD model, we propose a hyperparameter optimization method based on Bayesian optimization used to optimize the parameters of the four basic machine learners in the model. By introducing a multi-head-attention mechanism, the Transformer module increases the attention between local features and global features, enabling the discovery of the internal relationship between features. Additionally, the BiGRU is used to preserve the temporal features of the dataset, while the DNN is used to extract deeper features. Finally, the SoftMax classifier is used to classify the output. By analyzing the results of the comparison and ablation experiments, it can be concluded that the F1-score of the ETM-TBD model on a robotic arm dataset is 0.9665 and the model has very low FNR and FPR scores of 0.0263 and 0.0081, respectively. It can be seen that the model in this paper is better than the traditional single machine learning algorithm as well as the algorithm lacking any of the modules.

show abstract

“…The external hydraulic pipeline of the aero-engine is an important part of the engine [1,2]. The aero-pipeline systems have multi-field coupled vibration excitation [3,4].…”

Section: Introductionmentioning

confidence: 99%

The BT-SAM-Net: a new framework of end-to-end periodic time-series fault diagnosis for aero-pipelines systems

Yang,

Dang,

Duan

et al. 2024

Phys. Scr.

View full text Add to dashboard Cite

Accurate and scientific recognition of aero-pipeline faults is of great practical significance for engine maintenance costs and downtime. Pipeline signals have a strong periodic time series correlation under strong pump source pressure pulsation stimulation. The features of the pipeline fault will be gradually transferred during the pump source pulsation cycle when fault occurs. However, very few studies have considered the correlation of features at pulsation period time points. Additionally, it is challenging to realize intelligent fault diagnosis of weak characteristics of pipeline faults due to the influence of external environmental noise and vibration-noise coupling of aero-engines. At the same time, enhancing the generalization ability of the aero-pipeline diagnosis model has become an urgent issue in academia and engineering. The Bi-Time-Self-Attention Mechanism (BT-SAM-Net), a newly created fault detection framework end-to-end time-series and anti-noise, is presented for the aero-engine hydraulic pipeline in order to close the aforementioned research gaps. The primary goal of the proposed framework is to accomplish end-to-end feature extraction and classification tasks by using the measured aero-pipeline raw data as the model input, without any data processing. Firstly, a two-way time series information fusion model is creatively designed, which is the first attempt to study and analyze the difference in time series correlation characteristics of different types of faults and different working conditions of aero-pipelines. Secondly, The BT-SAM-Net framework incorporates the self-attention mechanism as an optimization tool to enhance the ultimate decision-making accuracy of the framework. Thirdly, the BT-SAM-Net framework was compared with 7 other methods. The results show the superiority and stability by demonstrating the BT-SAM-Net framework can identify various aero-hydraulic fault states with greater accuracy.

show abstract

A Novel 1D-Convolutional Spatial-Time Fusion Strategy for Data-Driven Fault Diagnosis of Aero-Hydraulic Pipeline Systems

Cited by 12 publications

References 51 publications

Intelligent fault diagnosis methods for hydraulic components based on information fusion: review and prospects

Intelligent fault diagnosis methods for hydraulic components based on information fusion: review and prospects

An Intrusion Detection Method Based on Hybrid Machine Learning and Neural Network in the Industrial Control Field

The BT-SAM-Net: a new framework of end-to-end periodic time-series fault diagnosis for aero-pipelines systems

Contact Info

Product

Resources

About