Lithium Ion (Li-ion) batteries have gained widespread popularity across
various industries, from powering portable electronic devices to propelling
electric vehicles and supporting energy storage systems. A central challenge in
managing Li-ion batteries effectively is accurately predicting their Remaining
Useful Life (RUL), which is a critical measure for proactive maintenance and
predictive analytics. This study presents a novel approach that harnesses the
power of multiple denoising modules, each trained to address specific types of
noise commonly encountered in battery data. Specifically we use a denoising
auto-encoder and a wavelet denoiser to generate encoded/decomposed
representations, which are subsequently processed through dedicated
self-attention transformer encoders. After extensive experimentation on the
NASA and CALCE datasets, we are able to characterize a broad spectrum of health
indicator estimations under a set of diverse noise patterns. We find that our
reported error metrics on these datasets are on par or better with the best
reported in recent literature.

利用多个去噪模块的力量，本研究提出了一种新方法，用于准确预测锂离子电池的剩余寿命 (RUL)，并通过去噪自编码器和小波去噪器生成编码 / 分解表示，然后通过专用的自注意力变换编码器进行处理，通过 NASA 和 CALCE 数据集上的大量实验证明，我们能够在多种噪声模式下表征广谱的健康指标估计，而且我们报告的误差指标与最近文献中的最佳结果持平或更好。

De-SaTE: 去噪自注意力变压器编码器用于锂离子电池健康预测

De-SaTE: Denoising Self-attention Transformer Encoders for Li-ion  Battery Health Prognostics

While diffusion models have achieved promising performances in data
synthesis, they might suffer error propagation because of their cascade
structure, where the distributional mismatch spreads and magnifies through the
chain of denoising modules. However, a strict analysis is expected since many
sequential models such as Conditional Random Field (CRF) are free from error
propagation. In this paper, we empirically and theoretically verify that
diffusion models are indeed affected by error propagation and we then propose a
regularization to address this problem. Our theoretical analysis reveals that
the question can be reduced to whether every denoising module of the diffusion
model is fault-tolerant. We derive insightful transition equations, indicating
that the module can't recover from input errors and even propagates additional
errors to the next module. Our analysis directly leads to a consistency
regularization scheme for diffusion models, which explicitly reduces the
distribution gap between forward and backward processes. We further introduce a
bootstrapping algorithm to reduce the computation cost of the regularizer. Our
experimental results on multiple image datasets show that our regularization
effectively handles error propagation and significantly improves the
performance of vanilla diffusion models.

扩散模型中的错误传播问题及其解决方法的实验和理论验证，提出了一种一致性正则化方案来降低前向和后向过程之间的分布差异，并引入了引导算法来降低计算成本。在多个图像数据集上的实验结果表明，该正则化策略有效地处理了错误传播问题，并显著提高了传统扩散模型的性能。