Oscillatory behavior-based wavelet decomposition for the monitoring of bearing condition in centrifugal pumps

Abstract

Bearing failure is one of the reasons for centrifugal pump breakdown. Existing methods developed for bearing fault diagnosis do not work satisfactorily when the vibration signature of bearing is overlapped by the signature from other defect sources such as an impeller defect. A vibration signal processing scheme making use of ensemble empirical mode decomposition and dual Q-factor wavelet decomposition is proposed to extract information of the bearing defect in a pump. A criterion called as frequency factor is also proposed to find the best decomposition level for the given high and low Q-factor wavelet decomposition parameters. The transient impulses due to bearing defect are effectively extracted separating traces of oscillatory signature of impeller defect and the noise in the signal. The same has been demonstrated using simulation analysis and experimental study. A comparison of the proposed method with existing signal processing methods is also presented.

Non-parametric Ensemble Empirical Mode Decomposition for extracting weak features to identify bearing defects

“…Then, the resulting signal is decomposed by the EMD algorithm to generate the IMFs. By repeating the steps Ne times, the true ensemble IMFs sets are defined in order to calculate the mean of all ensemble trials obtained from the IMFs of same order [33].…”

A novel current sensor indicator enabled WAFTR model for tool wear prediction under variable operating conditions

“…Thus, spindle current will be affected by these factors and hence is too complex to be interpreted directly. Hence HIs for tool wear monitoring have been extracted in the literature by analysing sensor signal in the time domain (TD), frequency domain (FD), and timefrequency domain (TFD) [31]. Some of the most widely used HIs in literature are shown in Table 1.…”

Intelligent Ball Bearing Fault Diagnosis Using Fractional Lorenz Chaos Extension Detection

“…A Grünwald-Letnikov fractional order approximation [ 20 , 24 , 35 , 36 , 37 ] gives Equation (7) is the dynamic error, a real (arbitrary) number, and is the desired phenomenon, see Figure 4 . From , the following rules apply: : For arithmetic quantification as well as proportional application : For classification and control of non-arithmetical values …”

“…The methods mainly used involve stator current [ 9 , 10 , 11 , 12 ], audio [ 13 , 14 ] and vibration signals [ 15 , 16 ]. For signal analysis, both discrete Fourier [ 17 ] transform and wavelet [ 18 , 19 , 20 , 21 , 22 , 23 , 24 ] were the most frequent analyses used. Over the last few years, chaos systems have been extensively used for diagnosis [ 25 , 26 ] with the fractional-order chaos method giving better diagnostic results, than a simpler chaotic system [ 27 , 28 ].…”