Condition monitoring signals provide sufficient information about the health of machines and, therefore, are widely used for fault diagnosis, prognosis, and health management. Existing approaches generally extract one or more degradation features from original signals collected in a time interval and predict the remaining useful life of machinery based on a selected or fused feature under a pre-specified threshold. However, using a single feature is often inadequate in terms of the accuracy of fault prognosis due to the interval-based extraction procedure. To overcome the shortcoming, a new prognosis framework is proposed for machinery based on original condition monitoring signals in this paper. Technically, the Box-Cox transformation is first performed on the original signals point by point to construct a series of degradation features without losing information. Then, the neural basis expansion analysis for time series (N-BEATS) that has robust performance in time-series prediction is utilized to predict the future evolution for each feature with high volatility. By leveraging the similarity of multiple Box-Cox features, a parameter-based transfer learning method is proposed to reduce the computation complexity. Finally, we reconstruct the future original signals of machinery through the inverse Box-Cox transformation. Since the reconstructed original signals are noisy, a new failure criterion suitable for decision-level fault prognosis is defined from an industrial application perspective. An application on high speed train wheels and two follow-up simulations are used to illustrate the performance of our proposed framework in machinery fault prognosis.

状态监测信号提供了足够的机器健康信息，因此被广泛用于故障诊断、预测和健康管理。现有方法通常从在一个时间间隔内收集的原始信号中提取一个或多个退化特征，并在预先指定的阈值下基于选择或融合的特征来预测机器的剩余使用寿命。然而，由于基于间隔的提取过程，就故障预测的准确性而言，使用单个特征通常是不够的。为了克服这一缺点，本文提出了一种新的基于原始状态监测信号的机械预测框架。从技术上讲，首先对原始信号逐点进行Box-Cox变换，在不丢失信息的情况下构造一系列退化特征。然后，利用在时间序列预测中具有稳健性能的时间序列神经基础扩展分析（N-BEATS）来预测具有高波动性的每个特征的未来演化。通过利用多个 Box-Cox 特征的相似性，提出了一种基于参数的迁移学习方法来降低计算复杂度。最后，我们通过逆 Box-Cox 变换重构未来的机械原始信号。由于重建的原始信号有噪声，从工业应用的角度定义了一种适用于决策级故障预测的新故障准则。