Adaptive thresholding and coordinate attention-based tree-inspired network for aero-engine bearing health monitoring under strong noise

Zhao, Dezun; Cai, Wenbin; Cui, Lingli

doi:10.1016/j.aei.2024.102559

Cited by 22 publications

(2 citation statements)

References 31 publications

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“…As a first approach, standardization is utilized, and it is the method involved with deducting your information by the process for all perceptions and partitioning the outcome by the standard deviation, followed by scaling your perceptions [ 75 ]. In AI, the accompanying recipe is utilized for normalization.…”

Section: Proposed Systemmentioning

confidence: 99%

Advancements in intrusion detection: A lightweight hybrid RNN-RF model

Khan,

Mohmand,

Rehman

et al. 2024

PLoS ONE

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Computer networks face vulnerability to numerous attacks, which pose significant threats to our data security and the freedom of communication. This paper introduces a novel intrusion detection technique that diverges from traditional methods by leveraging Recurrent Neural Networks (RNNs) for both data preprocessing and feature extraction. The proposed process is based on the following steps: (1) training the data using RNNs, (2) extracting features from their hidden layers, and (3) applying various classification algorithms. This methodology offers significant advantages and greatly differs from existing intrusion detection practices. The effectiveness of our method is demonstrated through trials on the Network Security Laboratory (NSL) and Canadian Institute for Cybersecurity (CIC) 2017 datasets, where the application of RNNs for intrusion detection shows substantial practical implications. Specifically, we achieved accuracy scores of 99.6% with Decision Tree, Random Forest, and CatBoost classifiers on the NSL dataset, and 99.8% and 99.9%, respectively, on the CIC 2017 dataset. By reversing the conventional sequence of training data with RNNs and then extracting features before applying classification algorithms, our approach provides a major shift in intrusion detection methodologies. This modification in the pipeline underscores the benefits of utilizing RNNs for feature extraction and data preprocessing, meeting the critical need to safeguard data security and communication freedom against ever-evolving network threats.

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Section: Proposed Systemmentioning

confidence: 99%

Advancements in intrusion detection: A lightweight hybrid RNN-RF model

Khan,

Mohmand,

Rehman

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

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“…The authors compared both simulated and experimental results to demonstrate the diagnosis's effectiveness. In another study, adaptive thresholding and coordinate attention-based tree-inspired network was proposed to detect healthy conditions of bearings [11]. Moreover, a frequency-chirprate synchrosqueezing operator was introduced to analyze the time-frequency representation of bearings faults.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Enhanced Small-Sample Bearing Fault Analysis Using Q-Transform and HOG Image Features in a GRU-XAI Framework

Dave,

Borade,

Agrawal

et al. 2024

Machines

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Timely prediction of bearing faults is essential for minimizing unexpected machine downtime and improving industrial equipment’s operational dependability. The Q transform was utilized for preprocessing the sixty-four vibration signals that correspond to the four bearing conditions. Additionally, statistical features, also known as attributes, are extracted from the Histogram of Oriented Gradients (HOG). To assess these features, the Explainable AI (XAI) technique employed the SHAP (Shapely Additive Explanations) method. The effectiveness of the GRU, LSTM, and SVM models in the first stage was evaluated using training and tenfold cross-validation. The SSA optimization algorithm (SSA) was employed in a subsequent phase to optimize the hyperparameters of the algorithms. The findings of the research are rigorously analyzed and assessed in four specific areas: the default configuration of the model, the inclusion of selected features using XAI, the optimization of hyperparameters, and a hybrid technique that combines SSA and XAI-based feature selection. The GRU model has superior performance compared to the other models, achieving an impressive accuracy of 98.2%. This is particularly evident when using SSA and XAI-informed features. The subsequent model is the LSTM, which has an impressive accuracy rate of 96.4%. During tenfold cross-validation, the Support Vector Machine (SVM) achieves a noticeably reduced maximum accuracy of 84.82%, even though the hybrid optimization technique shows improvement. The results of this study usually show that the most effective model for fault prediction is the GRU model, configured with the attributes chosen by XAI, followed by LSTM and SVM.

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