Abstract: An ultrasonic method and system for simultaneously measuring lubrication film thickness and liner wear of sliding bearings. The method includes: installing an ultrasonic sensor on a bearing bush; sending, by a processor, signals to an ultrasonic pulser-receiver to generate voltage pulses to excite the ultrasonic sensor to generate ultrasonic pulses; collecting an echo signal of an unworn liner-air interface as a reference signal Ba(f); collecting an echo signal of worn liner-lubrication film interface as to-be-measured signal Bow(f); obtaining an amplitude spectrum |Ba(f)| and a phase spectrum ?Baof Ba(f), an amplitude spectrum |Bow(f)| and a phase spectrum ?Bow(f) of Bow(f) by FFT; calculating an amplitude spectrum |Rw(f)|, and a phase spectrum ?Rw(f) of a reflection coefficient; based on |Rw(f)|, calculating lubrication film thickness d via a resonance model or a spring model; and based on ?Rw(f), calculating liner worn thickness via wear model under different film thicknesses.

Abstract: A method and system of enhancing online reflected light ferrograph images.

Abstract: A deep partial transfer method weighted by a domain asymmetry factor for rolling bearing fault diagnosis includes: first, extracting the deep transfer fault features from the monitoring data of the source rolling bearing and the target rolling bearing by a deep residual network; second, training the domain confusion network by using the deep transfer fault feature, and calculating the domain asymmetric factor; next, calculating the maximum mean discrepancy implanted by a multiple polynomial kernels of the fault features of the adaptation layer of the deep residual network, and using the domain asymmetry factor weighting to suppress the contribution of outlier fault features of the source rolling bearing; and finally, building the objective function using the weighted maximum mean discrepancy implanted by the multiple polynomial kernels to train the deep residual network.

Abstract: A fault transfer diagnosis method for rolling element bearings based on polynomial kernel induced feature distribution adaptation includes: inputting the data set of the source rolling element bearings and the monitoring data set from the target rolling element bearings into the deep residual network; extracting the transferrable fault features of the source and the transferrable fault features of the target layer by layer; minimizing the distribution discrepancy by the polynomial kernel induced feature adaptation; inputting the transferrable fault features of the target into the Softmax classifier to obtain the probability distribution of the specific state of the target samples; converting the probability distribution into the pseudo labels of the target samples; training the transfer diagnosis model; inputting the monitoring data of the target bearings into the trained diagnostic model, and outputting the label probability distribution corresponding to the data samples.