A quantitative diagnosis method for rolling element bearing using signal complexity and morphology filtering

This paper considers a quantitative method for assessment of fault severity of rolling element bearing by means of signal complexity and morphology filtering. The relationship between the complexity and bearing fault severity is explained. The improved morphology filtering is adopted to avoid the ambiguity between severity fault and the pure random noise since both of them will acquire higher complexity value. According to the attenuation signal characteristics of a faulty bearing the artificial immune optimization algorithm with the target of pulse index is used to obtain optimal filtering signal. Furthermore, complexity algorithm is revised to avoid the loss of weak impact signal. After largely removing noise and other unrelated signal components, the complexity value will be mostly affected by the bearing system and therefore may be adopted as a reliable quantitative bearing fault diagnosis method. Application of the proposed approach to the bearing fault signals has demonstrated that the improved morphology filtering and the complexity of signal can be used to adequately evaluate bearing fault severity

The Recovery of Weak Impulsive Signals Based on Stochastic Resonance and Moving Least Squares Fitting

In this paper a stochastic resonance (SR)-based method for recovering weak impulsive signals is developed for quantitative diagnosis of faults in rotating machinery. It was shown in theory that weak impulsive signals follow the mechanism of SR, but the SR produces a nonlinear distortion of the shape of the impulsive signal. To eliminate the distortion a moving least squares fitting method is introduced to reconstruct the signal from the output of the SR process. This proposed method is verified by comparing its detection results with that of a morphological filter based on both simulated and experimental signals. The experimental results show that the background noise is suppressed effectively and the key features of impulsive signals are reconstructed with a good degree of accuracy, which leads to an accurate diagnosis of faults in roller bearings in a run-to failure test

Study on the Characterization Method of Ultrasonic Cavitation Field based on the Numerical Simulation of the Amplitude of Sound Pressure

In order to meet the visualization requirements of the cavitation field distribution in the ultrasonic cavitation reactor, a method based on the numerical simulation of the amplitude of sound pressure is proposed. In order to verify that the amplitude of sound pressure plays a decisive role in cavitation effect, the dynamic equation of a single cavitation bubble is established, and the influence law of the amplitude of sound pressure on cavitation motion is analyzed in principle; then, the three-dimensional model of the self-built drum type ultrasonic cavitation reactor is built using the finite element software COMSOL Multiphysics, and the amplitude distribution of the sound pressure at the longitudinal section is obtained when the liquid height was 25 mm, 60 mm and 90 mm. Through the comparison of aluminum foil corrosion experiments, it shows that the numerical simulation method based on the amplitude of sound pressure can accurately characterize the distribution area of ultrasonic cavitation field, which overcomes the disadvantage of time-consuming and labor-consuming in the traditional measurement method of cavitation field distribution, and lays a foundation for the study of the distribution law of ultrasonic cavitation field

Study on the Characterization Method of Ultrasonic Cavitation Field based on the Numerical Simulation of the Amplitude of Sound Pressure

In order to meet the visualization requirements of the cavitation field distribution in the ultrasonic cavitation reactor, a method based on the numerical simulation of the amplitude of sound pressure is proposed. In order to verify that the amplitude of sound pressure plays a decisive role in cavitation effect, the dynamic equation of a single cavitation bubble is established, and the influence law of the amplitude of sound pressure on cavitation motion is analyzed in principle; then, the three-dimensional model of the self-built drum type ultrasonic cavitation reactor is built using the finite element software COMSOL Multiphysics, and the amplitude distribution of the sound pressure at the longitudinal section is obtained when the liquid height was 25 mm, 60 mm and 90 mm. Through the comparison of aluminum foil corrosion experiments, it shows that the numerical simulation method based on the amplitude of sound pressure can accurately characterize the distribution area of ultrasonic cavitation field, which overcomes the disadvantage of time-consuming and labor-consuming in the traditional measurement method of cavitation field distribution, and lays a foundation for the study of the distribution law of ultrasonic cavitation field

Domain-Adaptive Prototype-Recalibrated Network with Transductive Learning Paradigm for Intelligent Fault Diagnosis under Various Limited Data Conditions

In real industrial scenarios, intelligent fault diagnosis based on data-driven methods has been widely researched in the past decade. However, data scarcity is widespread in fault diagnosis tasks owning to the difficulties in collecting adequate data. As a result, there is an increasing demand for both researchers and engineers for fault identification with scarce data. To address this issue, an innovative domain-adaptive prototype-recalibrated network (DAPRN) based on a transductive learning paradigm and prototype recalibration strategy (PRS) is proposed, which has the potential to promote the generalization ability from the source domain to target domain in a few-shot fault diagnosis. Within this scheme, the DAPRN is composed of a feature extractor, a domain discriminator, and a label predictor. Concretely, the feature extractor is jointly optimized by the minimization of few-shot classification loss and the maximization of domain-discriminative loss. The cosine similarity-based label predictor, which is promoted by the PRS, is exploited to avoid the bias of naïve prototypes in the metric space and recognize the health conditions of machinery in the meta-testing process. The efficacy and advantage of DAPRN are validated by extensive experiments on bearing and gearbox datasets compared with seven popular and well-established few-shot fault diagnosis methods. In practical application, the proposed DAPRN is expected to solve more challenging few-shot fault diagnosis scenarios and facilitate practical fault identification problems in modern manufacturing