Rolling bearing is key component of rotating machinery and its fault diagnosis is of great significance for reliable operation of machine. In this paper, an intelligent fault diagnosis method of rolling bearing based on FCM clustering of vibration images obtained by EMD-PWVD is presented. Firstly, vibration signals with different fault degrees are transformed into contour time-frequency images by EMD-PWVD. Secondly, vibration images are divided into sections and their energy distribution values are used as image feature. Then, feature vectors are constructed for known signals, which are standardized as inputs of FCM clustering to obtain classification matrix and clustering center. Finally, proximity between tested samples and clustering centers of known samples are calculated to realize identification of bearing faults. Experimental results show that identification accuracy of this proposed method is high. When adding noise, the proposed method is more stable than other vibration images such as grayscale and symmetrical polar coordinate image, and when the added noise with SNR of 5, the reduction rate of identification accuracy is obviously smaller than those of other two methods.
Rolling bearing is a key element of rotating machine in safe and reliable operation, and its fault diagnosis is a research focus. When a single bearing fault fails to be addressed in time, it will cause the progressive composite faults between bearing and other elements. In this paper, the different composite fault cases of bearing and rotor are considered. First, an Information Fusion-Empirical Mode Decomposition-Angle Adaptive Distribution of Polar Coordinates Image(IF-EMD-AADPCI) method is proposed, which has an adaptive image expression ability of tested vibration signal, and then can provide the high-quality vibration image samples for the model training. Second, an intelligent diagnosis model combining Convolutional Neural Network(CNN) and Support Vector Machine(SVM) is proposed, which has an excellent generalization ability to recognize the different composite faults. Third, the different compound faults between rolling bearing and rotor are fabricated, tested and then diagnosed. The results show the test accuracy of the proposed method is higher than the conventional method and simple in the image transform, which proves that this work is effective for the composite fault diagnosis of rolling bearing and rotor.
Unlike traditional magnetic thrust bearings, a new type of permanent magnet biased bearing structure made of soft magnetic composites in the bevel gear coupled rotor system was proposed that requires both lower eddy currents and greater thrust force. This paper presents the optimization of structure parameters, analysis of magnetic field and dynamic stiffness for two kinds of permanent magnet biased bearing. Firstly, the structural parameters for the bearing were optimized based on the adaptive particle swarm optimization. Secondly, the dynamic magnetic flux distribution for the permanent magnet biased bearing made of carbon steel at 1000 Hz was obtained by finite element method. Then, the equivalent reluctance models for the new type of permanent magnet biased bearing considering the effect of eddy currents are derived. Finally, based on the equivalent reluctance models, the dynamic force–current factor and force–displacement factor are given. The results show that the permanent magnet biased bearing made of soft magnetic composites can provide more stable electromagnetic force and larger bandwidth in the frequency range of 1000 Hz, and have greater electromagnetic force at the same frequency comparing with that made of carbon steel.
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