Broken bar fault detection using fused DWT-FFT in FPGA platform

Panigrahy, Parth Sarathi; Konar, Pratyay; Chattopadhyay, Paramita

doi:10.1109/icpces.2014.7062819

Cited by 13 publications

(10 citation statements)

References 12 publications

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“…The experimental investigations have been carried out on machinery fault simulator (MFS), a laboratory prototype from Spectra Quest (Panigrahy et al, 2014;Konar and Chattopadhyay, 2015) as shown in Figure 2. Two induction motors (three-phase-squirrel cage), one faulty motor with three broken bars out of 34 and one healthy motor of rating 1/ 3H.P, 190 V, 50 Hz and 2980 rpm were used.…”

Section: Methodsmentioning

confidence: 99%

Cascaded signal processing approach for motor fault diagnosis

Panigrahy

Chattopadhyay

2018

COMPEL

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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.

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Section: Methodsmentioning

confidence: 99%

Cascaded signal processing approach for motor fault diagnosis

Panigrahy

Chattopadhyay

2018

COMPEL

Self Cite

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“…To overcome this constraint, a few researchers have utilized high-resolution spectral algorithms, with the impediment of not knowing a priori the number of subspaces designated to the noise [7,8]. Noise-based diagnosis algorithms based on fast Fourier transform (FFT) and other spectral methods that rely on subspace vectors were considered in [5][6][7][8][9][10][11][12]. However, the FFT has a drawback: it is not immune to noise since the spectrum of a noisy signal also includes the noise spectrum.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, a filter based on the windowed Fourier transforms (WFT) [13] has also been considered, a strategy that lies in a choice of amplitudes: multiexpanded in the spectrum [14], or an algorithm based on calculations on spectral vector subspaces such as MUSIC and ESPRIT [15]. Furthermore, wavelet-based strategies [11,[16][17][18][19][20][21][22] or empirical mode decomposition (EMD) [23] have also been considered.…”

Section: Introductionmentioning

confidence: 99%

Detection of Adjacent and Non-Adjacent Bar Breakages in Induction Motors Based on Power Spectral Subtraction and Second Order Statistics of Sound Signals

et al. 2020

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We apply power spectral analysis based on covariance function and spectral subtraction to detect adjacent and non-adjacent bar breakages. We obtain a spectral pattern when the signal presents one or various broken bars, independent of the relative position of the bar breakages. The proposed algorithm gives satisfactory results for detectability compared to some previous research. Additionally, we also present illustrations of faults and signal to noise in the noise-reduction stage.

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“…To locate the signatures associated with motor faults, different tools have been used on MCSA for time-frequency decomposition, allowing tracing of the evolution of such frequencies in time. Examples of these decompositions are the short-time Fourier transform [9][10][11][12], discrete wavelet transform [12][13][14][15], continuous wavelet transform [16][17][18][19], the Hilbert transform [20,21], the HilbertHuang Transform [20,21], the Wigner-Ville distribution [22][23][24][25][26][27], the Choi-Williams distribution [26][27][28], and multiple signal classification (MUSIC) [5]. Some of these tools work together with artificial intelligence classifiers for decisionmaking about the components or signatures that are present in the signals for identifying faults and their severity, such as artificial neural networks (ANN), fuzzy logic, fuzzy neural networks, and genetic algorithms [6,10,14,16,17,24,29].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Time-Frequency Decomposition Techniques for Fault Detection in Induction Motors Using Vibration Analysis during Startup Transient

Delgado-Arredondo¹,

García-Pérez²,

Moriñigo-Sotelo

et al. 2015

Shock and Vibration

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Induction motors are critical components for most industries and the condition monitoring has become necessary to detect faults. There are several techniques for fault diagnosis of induction motors and analyzing the startup transient vibration signals is not as widely used as other techniques like motor current signature analysis. Vibration analysis gives a fault diagnosis focused on the location of spectral components associated with faults. Therefore, this paper presents a comparative study of different timefrequency analysis methodologies that can be used for detecting faults in induction motors analyzing vibration signals during the startup transient. The studied methodologies are the time-frequency distribution of Gabor (TFDG), the time-frequency Morlet scalogram (TFMS), multiple signal classification (MUSIC), and fast Fourier transform (FFT). The analyzed vibration signals are one broken rotor bar, two broken bars, unbalance, and bearing defects. The obtained results have shown the feasibility of detecting faults in induction motors using the time-frequency spectral analysis applied to vibration signals, and the proposed methodology is applicable when it does not have current signals and only has vibration signals. Also, the methodology has applications in motors that are not fed directly to the supply line, in such cases the analysis of current signals is not recommended due to poor current signal quality.

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Broken bar fault detection using fused DWT-FFT in FPGA platform

Cited by 13 publications

References 12 publications

Cascaded signal processing approach for motor fault diagnosis

Cascaded signal processing approach for motor fault diagnosis

Detection of Adjacent and Non-Adjacent Bar Breakages in Induction Motors Based on Power Spectral Subtraction and Second Order Statistics of Sound Signals

Comparative Study of Time-Frequency Decomposition Techniques for Fault Detection in Induction Motors Using Vibration Analysis during Startup Transient

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