The performance of wireless networks can be greatly influenced by the radiation pattern and polarization of the antennas at the nodes, especially when they are under non-line-of-sight (NLOS) channel environments. In this study, we designed a planar quasi-isotropic antenna based on the combination of a meandered electric dipole and an electrically small loop at a frequency of 2.45 GHz. Its electrical size (ka) is 0.47 and shows a gain deviation of 3.01 dB with radiation efficiency of 82.6% per the simulation. The performance of a wireless link under the line-of-sight and NLOS channels in an indoor environment was measured using the proposed quasi-isotropic antenna as a receiving antenna after validating its radiation and impedance properties experimentally (the measured gain deviation: 5.2 dB, the measured radiation efficiency: 79.2%). This study demonstrates that better properties are achieved using the quasi-isotropic antenna. The quasi-isotropic antenna shows an improved packet delivery ratio (PDR) and received signal strength indicator (RSSI) compared to the results using omni-directional antennas as a transmitting and receiving pair in the NLOS channels. To the best of our knowledge, the experimental validation of the enhancement of wireless link reliability using a quasi-isotropic antenna has not been reported before, and was first carried out in this study.

Predicting the remaining useful life (RUL) of mechanical bearings is a challenging industrial task since RUL can differ even for the same equipment due to many uncertainties such as operating condition, model inaccuracy, and sensory noise in various industrial applications. This paper proposes the RUL prediction method combining analytical model-based and data-driven approaches to forecast when a failure will occur based on the time series data of bearings. Feature importance ranking and principal component analysis construct a reliable and predictable health indicator from various statistical time, frequency, and time–frequency domain features of the observed signal. The adaptive sliding window method then optimizes the parameters of the degradation model based on the ridge regression of the time series sequence with the sliding window. The proposed adaptive scheme provides significant performance improvement in terms of the RUL estimation accuracy and robustness against the possible errors of the degradation model compared to the traditional Bayesian approaches.