Cascaded signal processing approach for motor fault diagnosis

Abstract

Purpose The purpose of this paper is to inspect strategic placing of different signal processing techniques like wavelet transform (WT), discrete Hilbert transform (DHT) and fast Fourier transform (FFT) to acquire the qualitative detection of rotor fault in a variable frequency drive-fed induction motor under challenging low slip conditions. Design/methodology/approach The algorithm is developed using Q2.14 bit format of Xilinx System Generator (XSG)-DSP design tool in MATLAB. The developed algorithm in XSG-MATLAB can be implemented easily in field programmable gate array, as a provision to generate the necessary VHDL code is available by its graphical user interface. Findings The applicability of WT is ensured by the effective procedure of base wavelet selection, which is the novelty of the work. It is found that low-order Daubechies (db) wavelets show decent shape matching with current envelope rather than raw current signal. This fact allows to use db1-based discrete wavelet transform-inverse discrete wavelet transform, where economic and multiplier-less design is possible. Prominent identity of 2sfs component is found even at low FFT points due to the application of suitable base wavelet. Originality/value The proposed method is found to be effective and hardware-friendly, which can be used to design a low-cost diagnostic instrument for industrial applications.

Decent fault classification of VFD fed induction motor using random forest algorithm

“…Various features can be extracted from frequency domain information. However, the main drawback in frequency domain analysis is that the fault signature confirms their existence only in the intense resolution domain (Panigrahy and Chattopadhyay, 2018). In turn, a huge dataset accounting for large acquisition time is required.…”

“…Finally, these DWT-based frequency spectrums are correlated with that of original data, keeping both in the same and high-resolution domain. The correlation coefficient (CC) (Mallat, 2009; Panigrahy and Chattopadhyay, 2018) decides the similarity index between these two sets of frequency information. The whole procedure is depicted in Figure 6.…”

Experimental investigation and comparative study of signal processing techniques dedicated for the diagnosis of rotor failures in the induction motors

“…(Rajalakshmi and Vittal, 2012; Kedadouche et al , 2016) or electrical and magnetic [broken rotor bars (BRBs), inter-turns short circuit, etc.] (Dehina et al , 2020; Panigrahy and Chattopadhyay, 2018; Lal et al , 2016). The literature is rich in diagnostic techniques from which we can identify two important approaches for diagnosis: without model approach (Soualhi et al , 2013; Kia et al , 2007) and with model approach (Foo et al , 2013; Harrouz et al , 2019; Saïd et al , 2000).…”