Shock Pulse Index and Its Application in the Fault Diagnosis of Rolling Element Bearings

Abstract

Properties of time domain parameters of the vibration signal have been extensively studied for the fault diagnosis of rolling element bearings (REB). Parameters like kurtosis and Envelope Harmonic-to-Noise Ratio are most widely applied in this field and some important progress has been made. However, since only one-sided information is contained in these parameters respectively, problems still exist in practice when the signals collected are of complicated structure and/or contaminated by strong background noises. A new parameter, named Shock pulse index (SPI), is proposed in this paper. It integrates the mutual advantage of both parameters above and can help effectively identify fault related impulse components under the interference of strong background noises, unrelated harmonic components and random impulses. The SPI optimizes the parameters of Maximum Correlated Kurtosis Deconvolution (MCKD), which is used to filter the signals under consideration. Finally, the interested transient information contained in the filtered signal can be highlighted through demodulation with Teager Energy Operator (TEO). Fault related impulse components can therefore be extracted accurately. Simulations and experiment analyses verify the effectiveness and correctness of the SPI.

Intelligent identification of incipient rolling bearing faults based on VMD and PCA-SVM

“…In order to identify the incipient faults of rolling bearings accurately and efficiently, all kinds of advanced signal processing techniques are employed to analyze the vibration signals of rolling bearings. [4][5][6][7][8] The novel time-frequency analysis methods such as empirical mode decomposition (EMD), 9 empirical wavelet transform (EWT), 10 and variational mode decomposition (VMD) 11 are powerful and effective tools for processing non-stationary signals, and these methods have been widely used for vibration signal processing, condition monitoring, and fault identification of rolling bearings in the past several years. 12,13 However, EMD has inherent modal aliasing phenomenon and endpoint effect, 9 and also it is difficult to explicitly construct the frequency bands of EWT.…”

Reviews of bearing vibration measurement using fast Fourier transform and enhanced fast Fourier transform algorithms

“…The kurtogram is based on the spectral kurtosis that has been used in characterizing nonstationary signals, especially bearing fault signals. 30 However, an analytic bearing fault signal from spectral kurtosis needs to be constructed from either a complex filter or Hilbert transform or filtered by the STFT. Also, its performance efficiency is low in the presence of a low signal-to-noise ratio and non-Gaussian noise.…”

Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery

“…Once we acquire the vibration signal, there are mainly two categories of diagnostic approaches: signal processing-based approaches and pattern recognition-based approaches (Guo et al, 2016). In the first class, fault pattern can be identified by detecting the feature of vibration waveforms or the fault characteristic frequency using advanced signal processing methods, such as wavelet transform (WT), empirical mode decomposition (EMD) and spectral kurtosis (SK) (Han et al, 2016; Sun et al, 2017; Tang et al, 2016; Yaqub and Loparo, 2016), while this procedure requires the operator to grasp a good deal of expertise, which may bring difficulties for online diagnosis. Consequently, as the second ones, the data-driven diagnostic techniques, which can realize the automated and intelligent diagnosis with pattern recognition methods, receive wide attention and develop rapidly in recent years (Bogoevska et al, 2017; Cheng et al, 2016; Han et al, 2017; Liu et al, 2014).…”