Machine learning for computationally efficient electrical loads estimation in consumer washing machines
Machine learning for computationally efficient electrical loads estimation in consumer washing machines
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DOI：
10.1007/s00521-021-06138-9
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发表时间：
2021-06
影响因子：
6
通讯作者：
Vittorio Casagrande;G. Fenu;F. A. Pellegrino;Gilberto Pin;Erica Salvato;Davide Zorzenon
中科院分区：
文献类型：
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作者：
Vittorio Casagrande;G. Fenu;F. A. Pellegrino;Gilberto Pin;Erica Salvato;Davide Zorzenon
在不诉诸大量传感器的情况下估计家用电器的单个电气部件的磨损对于确保制造商的适当水平的维护是期望的。深度学习技术可以成为根据相对较差的测量进行此类估计的有效工具，但在实际部署时必须仔细考虑其计算需求。在这项工作中，我们采用一维卷积神经网络和长短期记忆网络来推断不同型号洗衣机的一些电气部件的状态，从插头处测量的电信号。这些工具的训练和测试的一个大的数据集（502洗涤周期1000小时），从四个不同的洗衣机收集，并精心设计，以符合内存的限制所施加的可用硬件选择一个真实的实施。该方法是端到端的;即，除了电信号的谐波分解之外，它不需要任何特征提取，因此它可以容易地推广到其他设备。
Estimating the wear of the single electrical parts of a home appliance without resorting to a large number of sensors is desirable for ensuring a proper level of maintenance by the manufacturers. Deep learning techniques can be effective tools for such estimation from relatively poor measurements, but their computational demands must be carefully considered, for the actual deployment. In this work, we employ one-dimensional Convolutional Neural Networks and Long Short-Term Memory networks to infer the status of some electrical components of different models of washing machines, from the electrical signals measured at the plug. These tools are trained and tested on a large dataset (502 washing cycles1000 h) collected from four different washing machines and are carefully designed in order to comply with the memory constraints imposed by available hardware selected for a real implementation. The approach is end-to-end; i.e., it does not require any feature extraction, except the harmonic decomposition of the electrical signals, and thus it can be easily generalized to other appliances.