As rolling bearings are the key components in rotating machinery, bearing performance degradation directly affects machine running status. A tendency prognosis for bearing performance degradation is thus required to ensure the stability of operation. This paper proposes a novel strategy for bearing performance degradation trend prognosis, including health indicator construction techniques and a performance degradation trend prediction method. To more accurately represent the degradation trend, the multiscale deep bottleneck health indicator is proposed as a new synthesized health indicator to remove high-frequency detail signals from features, which can reduce possible fluctuations in conventional synthetic health indicators. A suitable method for selecting the statistical characteristics required for fusion is also presented to solve the problem of information redundancy that affects trend representation. In addition, a stacked autoencoder network is used for deep feature extraction of selected statistical features. A bidirectional long short-term memory network prediction model is also proposed for the prediction of degradation trend, which can make full use of historical and future information to improve prediction accuracy. Finally, experiments are carried out to verify the effectiveness of the proposed method.

中文翻译：

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具有双向LSTM网络的新型多尺度深度健康指标，用于轴承性能下降趋势预测

由于滚动轴承是旋转机械中的关键部件，轴承性能下降直接影响机器的运行状态。因此，需要确保轴承性能下降的趋势预测，以确保操作的稳定性。本文提出了一种轴承性能下降趋势预测的新策略，包括健康指标构建技术和性能下降趋势预测方法。为了更准确地表示退化趋势，提出了多尺度深瓶颈健康指标作为一种新的综合健康指标，以消除特征中的高频细节信号，从而可以减少常规综合健康指标中可能出现的波动。还提出了一种选择融合所需统计特征的合适方法，以解决影响趋势表示的信息冗余问题。另外，堆叠式自动编码器网络用于对选定统计特征进行深度特征提取。还提出了一种双向的长期短期记忆网络预测模型来预测退化趋势，该模型可以充分利用历史和未来信息来提高预测精度。最后，通过实验验证了所提方法的有效性。还提出了一种双向的长期短期记忆网络预测模型来预测退化趋势，该模型可以充分利用历史和未来信息来提高预测精度。最后，通过实验验证了所提方法的有效性。还提出了一种双向的长期短期记忆网络预测模型来预测退化趋势，该模型可以充分利用历史和未来信息来提高预测精度。最后，通过实验验证了所提方法的有效性。