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During the gear hobbing machine process, the hob performance degradation assessment is significant for optimizing the tool changing frequency and improving machining efficiency. It is challenging to recognize hob wear state through vibration analysis and signal processing. Especially in the condition of intense vibration and noise interference, extracting signal features that reflect the wear state is challenging work. This paper proposes a feature extraction method called Cyclic Statistical Energy (CSE) to obtain the hob wear characteristics by tracking the sensitive frequency band. For the method, the vibration signal model of hob spindle is established first based on the vibration-generation mechanism of the machining process. Then, an index E is proposed to track the wear resonance frequency band of the vibration signal. Furthermore, the cyclic statistical order is analyzed and discussed. The hob performance degradation assessment can be realized by analyzing the energy index E with time variation. The experimental setup has been designed, and the two tests have been conducted in the production line: (1) impact hammer test and (2) hob whole life cutting test. The impact hammer test is set to obtain the modal parameters of the hob. Based on this, the whole life cutting test is designed and realized by which the vibration data of whole life with different wear states can be acquired. The proposed method CSE is successfully applied into online industry experiments test. The results show that the proposed method can give a more accurate performance degradation assessment curve for tool condition monitoring compared with traditional methods such as Wavelet Packet Decomposition (WPD). [ABSTRACT FROM AUTHOR]

Ye et al. [[5]] proposed a simultaneous fault diagnosis method based on hierarchical multi-label classification and a sparse Bayesian extreme learning machine to identify the single failure and simultaneous failures of the main reducer and determine the probability of each failure mode. 10.3390/app13084655 4 Gu Y., Zhang Y., Yang M., Li C. Motor On-Line Fault Diagnosis Method Research Based on 1D-CNN and Multi-Sensor Information. Gu et al. [[4]] proposed a model for motor fault diagnosis based on deep learning and multi-sensor information fusion, including the steps of data layer fusion, feature layer fusion, and fault classification, and finally realized the fast and accurate diagnosis of motor faults. [Extracted from the article]

As one of the most important components in rotating machinery, it’s necessary and essential to monitor the rolling bearing operating condition to prevent equipment failure or accidents. However, in vibration signal processing, the bearing initial fault detection under background noise is quite difficult. Therefore, in this paper a new feature extraction method combining sparse reconstruction and Multiscale Dispersion Entropy (MDErms) is proposed. Firstly, the Sliding Matrix Sequences (SMS) truncation and sparse reconstruction by Hankel-matrix are applied to the vibration signal. Then MDErms is utilized as a characteristic index of vibration signal, which is suitable for a short time series. Additionally, the MDErms is employed in the sparse reconstructed matrix sequences to achieve the Multiscale Fusion Entropy Value Sequence (MFEVS). The MFEVS keeps the fault potential feature information in different scales and is superior in distinguishing fault periodic impulses from heavy background noise. Finally, the designed FIR bandpass filter based on the MFEVS, shows prominent features in denoising and detecting weak bearing faults, which is separately verified by simulation studies and artificial fault experiments in different cases. By comparison with traditional methods like EEMD, Wavelet Packet (WP), and fast kurtogram, it can be concluded that the proposed method has a remarkable ability in removing noise and detecting rolling bearing faint fault. [ABSTRACT FROM AUTHOR]

The failure of rolling bearings is one of the most important factors for rotating machinery breakdown. The detection of initial fault in rolling bearings is crucial for the further prevention of equipment malfunction and failure. In this study, a new rolling bearing fault diagnosis method based on the singular value decomposition, slip Hankel matrix series construction and maximum singular value energy analysis is proposed. It has been validated that the proposed method has an excellent impulse recognition capacity, which can be further applied to design the optimal band‐pass filter for rolling bearing fault diagnosis. Then, the minimum entropy deconvolution (MED) technique is introduced to improve the fault extraction ability of the proposed method. Simulated signals and artificial fault tests are used to prove the capacity of the new method for rolling bearing fault detection. Furthermore, the result of accelerated life test indicates the initial bearing fault can be recognised by the proposed method, while the envelope spectrum cannot directly distinguish the failure type because of the redundant frequency interference. It can be concluded that the proposed method has the effectiveness of initial fault identification and redundant frequency elimination for rolling bearing fault diagnosis. [ABSTRACT FROM AUTHOR]