Aiming at reducing the production downtime and maintenance cost, prognostics and health management (PHM) of rotating machinery often includes the remaining useful life (RUL) prediction of bearings. In this paper, a method combining the generalized Weibull failure rate function (WFRF) and radial basis function (RBF) neural network is developed to deal with the RUL prediction of bearings. A novel indicator, namely, the power value on the sensitive frequency band (SFB), is proposed to track bearing degradation process. Generalized WFRF is used to fit the degradation indicator series to reduce the effect of noise and avoid areas of fluctuation in the time domain. RBF neural network is employed to predict the RUL of bearings with times and fitted power values at present and previous inspections as input. Meanwhile, the life percentage is selected as output. The performance of the proposed method is validated by an accelerated bearing run-to-failure experiment, and the results demonstrate the advantage of this method in achieving more accurate RUL prediction.

Rolling bearing is a key component of rotating machinery, and its remaining useful life (RUL) estimation is also a critical technique in prognostics health management activities. In this paper, we propose a two-stage strategy for bearing health monitoring, where the bearing health process is divided into two stages: normal stage and degeneration stage, and a new method is proposed to estimate the bearing RUL by combining a new health indicator (HI) and particle filtering (PF). First, the structural information of the spectrum (SIOS) algorithm is employed to construct the HI called SIOS-based indicator (SIOSI) for bearing degeneration monitoring. Second, the initial degenerate point (IDP) is evaluated by a novel index calculated with self-zero space observer in order to conduct the two-stage division of normal and degeneration stages. Third, after detecting the IDP, the bearing RUL is estimated using the SIOSI and the PF-based algorithm with the help of a degeneration model. The effectiveness of the proposed methodology is validated using vibration data collected from bearing run-to-failure tests. Experimental results have shown that the bearing RUL could be estimated acceptably with the proposed method, and its performance is greatly superior to that presented by L ₁₀ life formula.

High quality (Q) factors guided mode resonances (GMRs) are important platform for enhancing light–matter interactions. Conventional GMRs are excited by embedding periodic nanoholes in planar thin films, where the size of the holes determines the Q-factors. These control methods are relatively limited. In this work, we study multiple high-Q band folding GMRs in the near-infrared region and explore their sensing characteristics. By constructing a nanohole dimer metasurface, five band folding ultrahigh-Q GMRs are formed and corresponding high-Q GMRs are obtained by changing the size of one nanohole to break the mirror symmetry of the structure and thus manipulate the energy radiation of the modes. These resonance modes exhibit greater stability in momentum space, and their excitation is not strictly dependent on perpendicularly incident light, which facilitates experimental testing. We fabricate a series of samples to confirm these high-Q GMRs, with experimental Q-factors reaching 5.0 × 103. Next, we investigate the sensing characteristics of these GMRs, and due to the significant differences in their field distributions, TM0 mode has the best sensing performance among the five modes. Here, by spin-coating photoresists on the surface of the devices, we examine their sensing properties. It is proved that the specificity of the eigenfield localization of TM0 mode results in an excellent performance of the sensing properties of this mode, with an experimental sensitivity and figure of merit of 124 nm/RIU and 105, respectively. This work provides a route for the realization of metasurfaces with high Q-factors, which has potential applications in nanophotonics.

To address the challenges of poor performance in traditional diagnosis methods and two-dimensional (2-D) feature based approaches, this paper proposes a novel fault diagnosis approach based on multi-scale spectrum feature images and deep learning. Firstly, the vibration signal is preprocessed through mean removal processing and then converted to multi-length spectrum with fast Fourier transform (FFT). Secondly, a novel 2-D feature called multi-scale spectral image (MSSI) is constructed by multi-length spectrum paving scheme. Finally, a deep learning framework, convolutional neural network (CNN), is formulated to diagnose the bearing faults. Two experimental cases are utilized to verify the effectiveness of the proposed method. Experimental results demonstrate that the proposed method significantly improves the accuracy of fault diagnosis.