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Research on Fault Diagnosis of Highway Bi-LSTM Based on Attention Mechanism

Li Xueyi, Su Kaiyu, He Qiushi, Wang Xiangkai, Xie Zhijie

Eksploatacja i Niezawodność

2023

Vol. 25, no. 2

art. no. 162937

Deep groove ball bearings are widely used in rotary machinery. Accurate for bearing faults diagnosis is essential for equipment maintenance. For common depth learning methods, the feature extraction of inverse time domain signal direction and the attention to key features are usually ignored. Based on the long short term memory(LSTM) network, this study proposes an attention-based highway bidirectional long short term memory (AHBi-LSTM) network for fault diagnosis based on the raw vibration signal. By increasing the Attention mechanism and Highway, the ability of the network to extract features is increased. The bidirectional LSTM network simultaneously extracts the raw vibration signal in positive and inverse time-domains to better extract the fault features. Six deep groove ball bearings with different health conditions were used to validate the AHBi-LSTM method in an experiment. The results showed that the accuracy of the proposed method for bearing fault diagnosis was over 98%, which was 8.66% higher than that of the LSTM model. The AHBi-LSTM model is also better than other relevant models for bearing fault diagnosis.

Automatic cardiac arrhythmia classification based on hybrid 1-D CNN and Bi-LSTM model

Rahul Jagdeep, Sharma Lakhan Dev

Biocybernetics and Biomedical Engineering

2022

Vol. 42, no. 1

312--324

Cardiovascular diseases (CVDs) are a group of heart and blood vessel ailments that can cause chest pain and trouble breathing, especially while active. However, some patients with heart disease have no symptoms and may benefit from screening. Electrocardiogram (ECG) measures electrical activity of the heart using sensors positioned on the skin over the chest, and it can be used for the timely detection of CVDs. This work presents a technique for classification among lethal CVDs like atrial fibrillation (Afib), ventricular fibrillation (Vfib), ventricular tachycardia (Vtec), and normal (N) beats. A novel combination of Stationary wavelet transforms (SWT) and a two-stage median filter with Savitzky–Golay (SG) filter were utilised for pre-processing of the ECG signal followed by segmentation and z-score normalisation process. Next, 1-D six-layers convolutional neural network (1- D CNN) was used for automated and reliable feature extraction. After that, bidirectional long short-term memory (Bi-LSTM) was used in the back end for classification of arrhythmias. The novelty of the present work is the use of 1-D CNN and Bi-LSTM architecture followed by relevant and effective pre-processing of the ECG signal makes this technique accurate and reliable. An accuracy of 99.41 % was achieved using 10-fold cross validation, which is superior to the existing state-of-art methods. Thus, this method presents a noble, accurate, and reliable method for classification of cardiac arrhythmia beats.