Capsule Network Based on Double-layer Attention Mechanism and Multi-scale Feature Extraction for Remaining Life Prediction

图层(电子) 计算机科学 比例(比率) 机制(生物学) 人工智能 特征提取 萃取(化学) 特征(语言学) 模式识别(心理学) 材料科学 色谱法 化学 纳米技术 物理 地图学 地理 语言学 哲学 量子力学
作者
Zhiwu Shang,Zehua Feng,Wanxiang Li,Zhihua Wu,Hongchuan Cheng
出处
期刊:Neural Processing Letters [Springer Science+Business Media]
卷期号:56 (3) 被引量:1
标识
DOI:10.1007/s11063-024-11651-8
摘要

Abstract The era of big data provides a platform for high-precision RUL prediction, but the existing RUL prediction methods, which effectively extract key degradation information, remain a challenge. Existing methods ignore the influence of sensor and degradation moment variability, and instead assign weights to them equally, which affects the final prediction accuracy. In addition, convolutional networks lose key information due to downsampling operations and also suffer from the drawback of insufficient feature extraction capability. To address these issues, the two-layer attention mechanism and the Inception module are embedded in the capsule structure (mai-capsule model) for lifetime prediction. The first layer of the channel attention mechanism (CAM) evaluates the influence of various sensor information on the forecast; the second layer adds a time-step attention (TSAM) mechanism to the LSTM network to weigh the contribution of different moments of the engine's whole life cycle to the prediction, while weakening the influence of environmental noise on the prediction. The Inception module is introduced to perform multi-scale feature extraction on the weighted data to capture the degradation information to the maximum extent. Lastly, we are inspired to employ the capsule network to capture important position information of high and low-dimensional features, given its capacity to facilitate a more effective rendition of the overall features of the time-series data. The efficacy of the suggested model is assessed against other approaches and verified using the publicly accessible C-MPASS dataset. The end findings demonstrate the excellent prediction precision of the suggested approach.
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