遗忘
计算机科学
搜索引擎索引
人工智能
人工神经网络
特征提取
量化(信号处理)
编码
方位(导航)
深度学习
断层(地质)
机器学习
模式识别(心理学)
语音识别
计算机视觉
基因
哲学
语言学
生物化学
化学
地震学
地质学
作者
Jing Zheng,Hui Xiong,Yuchang Zhang,Kaige Su,Zheyuan Hu
出处
期刊:Machines
[MDPI AG]
日期:2022-05-06
卷期号:10 (5): 338-338
被引量:5
标识
DOI:10.3390/machines10050338
摘要
In recent years, deep-learning schemes have been widely and successfully used to diagnose bearing faults. However, as operating conditions change, the distribution of new data may differ from that of previously learned data. Training using only old data cannot guarantee good performance when handling new data, and vice versa. Here, we present an incremental learning scheme based on the Repeated Replay using Memory Indexing (R-REMIND) method for bearing fault diagnosis. R-REMIND can learn new information under various working conditions while retaining older information. First, we use a feature extraction network similar to the Inception-v4 neural network to collect bearing vibration data. Second, we encode the features by product quantization and store the features in indices. Finally, the parameters of the feature extraction and classification networks are updated using real and reconstructed features, and the model did not forget old information. The experiment results show that the R-REMIND model exhibits continuous learning ability with no catastrophic forgetting during sequential tasks.
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