Accurate prediction of the Remaining Useful Life (RUL) of rolling bearings is
crucial in industrial production, yet existing models often struggle with
limited generalization capabilities due to their inability to fully process all
vibration signal patterns. We introduce a novel multi-input autoregressive
model to address this challenge in RUL prediction for bearings. Our approach
uniquely integrates vibration signals with previously predicted Health
Indicator (HI) values, employing feature fusion to output current window HI
values. Through autoregressive iterations, the model attains a global receptive
field, effectively overcoming the limitations in generalization. Furthermore,
we innovatively incorporate a segmentation method and multiple training
iterations to mitigate error accumulation in autoregressive models. Empirical
evaluation on the PMH2012 dataset demonstrates that our model, compared to
other backbone networks using similar autoregressive approaches, achieves
significantly lower Root Mean Square Error (RMSE) and Score. Notably, it
outperforms traditional autoregressive models that use label values as inputs
and non-autoregressive networks, showing superior generalization abilities with
a marked lead in RMSE and Score metrics.

利用多输入自回归模型将振动信号与先前预测的健康指标值相结合，通过特征融合输出当前窗口的健康指标值，从而解决滚动轴承寿命预测中通用性有限的问题。通过自回归迭代，该模型具有全局感受野，有效克服通用化的限制。在 PMH2012 数据集上的实证评估表明，与使用类似自回归方法的其他骨干网络相比，我们的模型在均方根误差（RMSE）和得分（Score）方面表现明显更低，优于传统的自回归模型和非自回归网络，具有出色的通用性能力。

利用多输入自回归模型进行轴承剩余使用寿命预测

Utilizing Multiple Inputs Autoregressive Models for Bearing Remaining  Useful Life Prediction

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。