Fault Diagnosis of Rotor Broken Bar in Induction Motor Based on Successive Variational Mode Decomposition

Abstract

When an induction motor is running at stable speed and low slip, the fault signal of the induction motor’s broken bar faults are easily submerged by the power frequency (50 Hz) signal. Thus, it is difficult to extract fault characteristics. The left-side harmonic component representing the fault characteristics can be distinguished from power frequency owing to V-shaped trajectory of the fault component in time-frequency (t-f) domain during motor startup. This article proposed a scheme to detect broken bar faults and discriminate the severity of faults under starting conditions. In this scheme, successive variable mode decomposition (SVMD) is applied to analyze the stator starting current to extract the fault component, and the signal reconstruction is proposed to maximize the energy of the fault component. Then, the quadratic regression curve method of instantaneous frequency square value of the fault component is utilized to discriminate whether the fault occurs. In addition, according to the feature that the energy of the fault component increases with the fault severity, the energy of the right part of the fault component is proposed to detect the severity of the fault. In this paper, experiments are carried out based on a 5.5 kW three-pole induction motor. The results show that the scheme proposed in this paper can diagnose the broken bar faults and determine the severity of the fault.

Solving the inverse problem in electrocardiography imaging for atrial fibrillation using various time-frequency decomposition techniques based on empirical mode decomposition: A comparative study

“…In these two decomposition methods, the screened IMFs cannot distinguish the valuable components for the ECGI inverse problem well. Although the SVMD-based solution, as an improved version of the VMD-based solution, adds constraints when solving variational problems ( Liu et al, 2022 ), it is not better than the VMD-based solution, in fact, it is marginally worse. Dependent on the different ECGs, the execution efficiency of VMD-based and SVMD-based solutions differs.…”

“…Among them, in recent years, multivariate empirical mode decomposition (MEMD) (Zheng and Xu, 2019), noise-assisted MEMD (NAMEMD) (Ahrabian et al, 2012), variational mode decomposition (VMD) (Dragomiretskiy and Zosso, 2014), successive variational mode decomposition (SVMD) (Nazari and Sakhaei, 2020), empirical wavelet transform (EWT) (Gilles, 2013;Hurat, 2020), UPEMD (Wang et al, 2018), improved UPEMD (IUPEMD) (Hurat, 2020;Ying et al, 2021;Zheng et al, 2021) have been proposed successively. The effectiveness of the above methods has been verified in mechanical fault detection, voice signal, ECG signal, and seismic signal processing (Lal et al, 2018;Zeng and Yuan, 2021;Liu et al, 2022). In order to study the influence of different EMD-based solutions on the accuracy and reliability of ECGI inverse operation, firstly, the ECGs were decomposed using the various EMD-based solutions mentioned above in this paper.…”

A feature extraction method for rotating machinery fault diagnosis based on a target detection index and successive variational mode decomposition

“…Guo et al [18] proposed a line search algorithm with an energy concentration and position accuracy (EP) index to determine the penalty factor of SVMD, and diagnosed faults through analyzing the IMF components with a higher EP index. Liu et al [19] used SVMD to extract fault components from stator starting current, reconstructed them based on their central frequencies, and ultimately detected faults through a quadratic regression curve of instantaneous frequency squared values.…”

Development of Broken Rotor Bar Fault Diagnosis Method with Sum of Weighted Fourier Series Coefficients Square

“…However, the diagnosis of the BRB fault is challenging because there are very slight fault signals at the current, voltage, rotor speed, and vibration. To overcome this problem, several studies have investigated BRB fault diagnosis methods for induction motors [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. The studied BRB fault diagnosis methods can be categorized into digital signal analysis, other information from special sensors, fault start cases, and neural networks or machine-learning-based algorithms [3].…”

“…Even in a noisy inverter-driven system, the fault signal can be separated and used for fault diagnosis [14]. Further, successive variable-mode decompositions have been used for a better diagnosis [15]. In contrast, using the induced voltage after removing the input grid power supply, Milimonfared et al presented a BRB fault diagnosis method with an induction motor model [2].…”