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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]

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]

Abstract: Rolling element bearing faults are among the main causes of rotating machines breakdown. It is important to distinguish the incipient fault before the bearings step into serious failure. Based on the traditional singular value decomposition (SVD) theory, short-time matrix series (STMS) and singular value ratio (SVR) are introduced to the vibration signal processing. The proposed signal processing method is called S-SVDR (STMS based SVD method using SVR) and it has been proved to have a good local identification capability in the rolling bearing fault diagnosis. The detailed description of applying S-SVDR methods to rolling bearing fault diagnosis is given through the artificial fault signal processing in experiment 1. In experiment 2, rolling element bearing accelerated life test is performed in Hangzhou Bearing Test & Research Center (HBRC). The experimental result shows that the incipient fault can be well detected through S-SVDR processing method. However, the envelope analysis of original signal cannot detect the fault due to the existence of signal interference. A conclusion can be made that the proposed S-SVDR method has a good effect on de-noising and eliminating the signal interference of rolling bearing for the fault diagnosis. [Copyright &y& Elsevier]

Vibration signals are widely used for bearing fault detection and diagnosis. When signals are acquired in the field, usually, the faulty periodic signal is weak and is concealed by noise. Various de-noising methods have been developed to extract the target signal from the raw signal. Stochastic resonance (SR) is a technique that changed the traditional denoising process, in which the weak periodic fault signal can be identified by adding an expression, the potential, to the raw signal and solving a differential equation problem. However, current SR methods have some deficiencies such us limited filtering performance, low frequency input signal and sequential search for optimum parameters. Consequently, in this study, we explore the application of SR based on the FitzHug-Nagumo (FHN) potential in rolling bearing vibration signals. Besides, we improve the search of the SR optimum parameters by the use of particle swarm optimization (PSO). The effectiveness of the proposed method is verifie d by using both simulated and real bearing data sets. [ABSTRACT FROM AUTHOR]