用于电化学电池故障预测的自监督编码器
Self-Supervised Encoder for Fault Prediction in Electrochemical Cells
摘要：
在故障发生之前进行预测有助于避免潜在的安全隐患。此外，提前规划所需的维护措施可降低运营成本。在这篇文章中，重点是电化学电池。为了预测电池的故障，典型的方法是估计健康电池将呈现的预期电压，并将其与电池的实时测量电压进行比较。这种方法是可行的，因为当故障即将发生时，电池的测量电压与相同操作条件下的预期电压不同。然而，估计预期电压是具有挑战性的，因为健康电池的电压也会受到其退化的影响-一个未知的参数。专家定义的参数模型目前用于此估计任务。相反，我们提出了一个神经网络模型的编码器-解码器架构的基础上使用。网络接收操作条件作为输入。编码器的任务是找到电池退化的忠实表示，并将其传递给解码器，解码器进而预测预期的电池电压。由于没有标记的退化数据提供给网络，我们认为我们的方法是一个自我监督的编码器。结果表明，我们能够预测多个电池的电压，同时减少了53%的预测误差，得到的参数模型。这一改进使我们的网络能够在故障发生前31小时预测故障，与参数模型相比，反应时间增加了64%。此外，可以绘制编码器的输出，从而为神经网络模型增加可解释性。
英文摘要：
Predicting faults before they occur helps to avoid potential safety hazards. Furthermore, planning the required maintenance actions in advance reduces operation costs. In this article, the focus is on electrochemical cells. In order to predict a cell's fault, the typical approach is to estimate the expected voltage that a healthy cell would present and compare it with the cell's measured voltage in real-time. This approach is possible because, when a fault is about to happen, the cell's measured voltage differs from the one expected for the same operating conditions. However, estimating the expected voltage is challenging, as the voltage of a healthy cell is also affected by its degradation -- an unknown parameter. Expert-defined parametric models are currently used for this estimation task. Instead, we propose the use of a neural network model based on an encoder-decoder architecture. The network receives the operating conditions as input. The encoder's task is to find a faithful representation of the cell's degradation and to pass it to the decoder, which in turn predicts the expected cell's voltage. As no labeled degradation data is given to the network, we consider our approach to be a self-supervised encoder. Results show that we were able to predict the voltage of multiple cells while diminishing the prediction error that was obtained by the parametric models by 53%. This improvement enabled our network to predict a fault 31 hours before it happened, a 64% increase in reaction time compared to the parametric model. Moreover, the output of the encoder can be plotted, adding interpretability to the neural network model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
