The prognosis of bearings is vital for condition-based maintenance of rotating machinery. This article proposes a systematic prognostic scheme for rolling element bearings. The proposed scheme infers the degradation progression by developing a novel health indicator (HI). This novel HI, derived from the spectral correlation, Wasserstein distance, and linear rectification, can reflect the changes in the probability distribution of all cyclic power-spectra over time. In other words, any form of variation in modulation characteristics can be revealed through the proposed novel indicator, even for the weak information buried by the internal or external noise. Furthermore, the developed HI can eliminate random fluctuations that often impair the remaining useful life (RUL) prediction accuracy. Then, a 3 ${\boldsymbol{\sigma }}$ criterion-based technique is introduced to divide health stages. After that, the gated recurrent unit network is employed to predict the RUL of the bearing system, integrated with the Bayesian optimization algorithm to tune the optimal hyperparameters adaptively. This renders the establishment of an intelligent prognosis model with high prediction accuracy and generalization ability. Finally, experimental validations are conducted using the run-to-failure datasets of bearings. The obtained results demonstrate that the proposed HI has better monotonicity, and the proposed prognostic scheme can predict the RUL with high accuracy.

Zero-sample diagnostic methods have gained recognition in addressing the scarcity of gearbox fault samples, thereby being regarded as a promising technique to guarantee gearbox safety. However, historical zero-sample approaches typically neglect the use of multi-modal non-contact sensing data and rarely consider the interpretability of the diagnostic process. This oversight limits their application in industrial environments that require high reliability or operate under extreme conditions. Therefore, this paper presents a composite neuro-fuzzy system-guided cross-modal zero-sample diagnostic framework, termed FCZD-IA, which employs infrared thermography and acoustic data to monitor gearbox conditions. Specifically, FCZD-IA uses a proposed composite neural system as a decision-maker in the diagnostic task, while integrating a deep backbone network to discriminatively learn high-level fault features from multi-modal data. Moreover, a specific training strategy is designed to guide the learning process of the FCZD-IA to promote robust and interpretable zero-sample diagnostics. Comprehensive experimental results validate the effectiveness of the proposed framework and its superiority over other competitive methods.