A Deep Learning Method for Automatic Visual Attention Detection in Older Drivers

Chikhaoui, Belkacem; Ruer, Perrine; Vallières, Évelyne F.

doi:10.1007/978-3-030-32785-9_5

Cited by 1 publication

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chen et al [3] offered a network based on the Attention mechanism autoencoder framework to predict the remaining useful life (RUL) value. For the automatic drive, the Attention mechanism has also made significant progress in recent research work [4]. At present, the Attention mechanism is not widely used in the fault diagnosis of bearings.…”

Section: Introductionmentioning

confidence: 99%

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

Li¹,

Su²,

He³

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%