Enhancing assessment of Personalized Multi-Agent System through ConvLSTM

Trifa, Amal; Hedhili, Aroua; Chaari, Wided Lejouad

doi:10.1016/j.procs.2017.08.239

Cited by 8 publications

(1 citation statement)

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“…As a deep learning model unit, convolutional long short-term memory (ConvLSTM) combined CNN and LSTM is specially designed for spatiotemporal sequence [37] and the structure is shown in Figure 2. LSTM has shown considerable power in dealing with temporal correlation issues, but it contains too much spatial redundancy.…”

Section: Architecture Of the Proposed Networkmentioning

confidence: 99%

A Reliable Prognosis Approach for Degradation Evaluation of Rolling Bearing Using MCLSTM

Huang

et al. 2020

Sensors

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Prognostics and health management technology (PHM), a measure to ensure the reliability and safety of the operation of industrial machinery, has attracted attention and application adequately. However, how to use the monitored information to evaluate the degradation of rolling bearings is a significant issue for its predictive maintenance and autonomic logistics. This work presents a reliable health prognosis approach to estimate the health indicator (HI) and remaining useful life (RUL) of rolling bearings. Firstly, to accurately capture the degradation process, a novel health index (HI) is constructed based on correlation kurtosis for different iteration periods and a Gaussian process latency variable model (GPLVM). Then, a multiple convolutional long short-term memory (MCLSTM) network is proposed to predict HI values and RUL values. Finally, we perform experimental datasets of rolling bearings, demonstrating that the presented method surpasses other state-of-the-art prognosis approaches. The results also confirm the feasibility of the presented method in industrial machinery.

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Section: Architecture Of the Proposed Networkmentioning

confidence: 99%