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Rolling bearings are key components that support the rotation of motor shafts, operating with a quite high failure rate among all the motor components. Early bearing fault diagnosis has great significance to the operation security of motors. The main contribution of this paper is to illustrate Gaussian white noise in bearing vibration signals seriously masks the weak fault characteristics in the diagnosis based on the Teager–Kaiser energy operator envelope, and to propose improved TKEO taking both accuracy and calculation speed into account. Improved TKEO can attenuate noise in consideration of computational efficiency while preserving information about the possible fault. The proposed method can be characterized as follows: a series of band-pass filters were set up to extract several component signals from the original vibration signals; then a denoised target signal including fault information was reconstructed by weighted summation of these component signals; finally, the Fourier spectrum of TKEO energy of the resulting target signal was used for bearing fault diagnosis. The improved TKEO was applied to a vibration signal dataset of run-to-failure rolling bearings and compared with two advanced diagnosis methods. The experimental results verify the effectiveness and superiority of the proposed method in early bearing fault detection.

Strengthening photocatalytic performance of graphitic carbon nitride (g-C3N4) that simultaneously enhance the original π-conjugated system and promote the intramolecular charges transfer has attracted considerable attention. In this work, the small...

Multi-source heterogeneous information will cause access burden to the power network, resulting in poor performance of multi-point hop communication. Therefore, a multi-point hop communication system based on information fusion model is designed. The IEC61970/61968CIM is selected as the integrated bus general data acquisition scheme of the information fusion model. The local-global distributed information fusion mechanism is used to realize the communication of the power multi-point hop communication system. In this paper, the artificial neural network algorithm is used to fuse local information features, and according to D-S evidence theory, the global decision-level fusion is carried out from both the spatial domain and the time domain. Through the information fusion model, the multi-point hop communication information in the power network realizes efficient transmission. The experimental results show that the application of the system in the power network to implement multi-point hop communication, the packet loss rate is less than 0.25%, the transmission delay is less than 30ms, and the communication performance of the power network is improved