A DSP-based FFT-analyzer for the fault diagnosis of rotating machine based on vibration analysis

Betta, G.; Liguori, Consolatina; Paolillo, Alfredo; Pietrosanto, Antonio

doi:10.1109/tim.2002.807987

Cited by 164 publications

(42 citation statements)

References 17 publications

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“…Similar results were obtained for other Debauchies family, like DB6. The PSD values did not showing any significant changes in the lower frequency bands, where changes do occur [6], [7] due to electrical faults. Also, for the mechanical fault considered here, it was found to be not a suitable choice.…”

Section: Resultsmentioning

confidence: 80%

“…The conventional Fourier and STFT have been well discussed and tried out in many of the previous works like in [1], [6]. The drawbacks of Fourier and STFT approach has lead to the widespread use of wavelets for processing the data.…”

Section: Proposed Approachmentioning

confidence: 99%

“…Most methods are to analyze the vibration signal in either timedomain or frequency-domain. Frequency-domain analysis is more attractive because it can provide more detailed information about the status of the machine [6]. Due to the averaging problems in classic FFT (Fast Fourier Transform) analysis, there is a good chance that the significant lines which correspond to certain faults may be hidden.…”

Section: Introductionmentioning

confidence: 99%

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Condition monitoring of induction motors using combined PSD based wavelet decomposition and selective weighting using spider web plots

Jeevanand¹,

Mathew²

2008

2008 IEEE Canada Electric Power Conference

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Condition monitoring of machines has its roots in the human ECG analysis for detecting cardiac arrhythmias. Condition monitoring in industry is desirable for increasing machinery availability, reducing consequential damage, and improving the operational efficiency. This is very significant in industries that use heavy duty machines for various processes. E.g.: A monstrous three-phase AC induction motor to drive a city water supply pump or very big, high power motors used in mills, huge generators for generating power in hydel plants etc depict a few of them. Also, for safety and economic considerations, there is a need to monitor the behavior of motors working in critical production processes as well. This paper demonstrates how the condition of an induction motor can be monitored by the analysis of the acoustic signal that represents the non-stationary vibration data. The analysis have been done using various signal processing algorithms and a new and better classification technique has been developed using the combined wavelet based PSD analysis and Spider Web Plots. The proposed method appears to give better performance.

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Section: Resultsmentioning

confidence: 80%

Section: Proposed Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Condition monitoring of induction motors using combined PSD based wavelet decomposition and selective weighting using spider web plots

Jeevanand¹,

Mathew²

2008

2008 IEEE Canada Electric Power Conference

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“…Among the noninvasive ones are those that perform a current analysis [2]- [9], vibration analysis [10]- [18], voltage analysis [19], [20], and magnetic flux analysis [20]. A review of some these techniques is presented in [22].…”

Section: Introductionmentioning

confidence: 99%

Empirical Mode Decomposition Analysis for Broken-Bar Detection on Squirrel Cage Induction Motors

Valles-Novo

Rangel-Magdaleno

Ramírez-Cortés

et al. 2015

IEEE Trans. Instrum. Meas.

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Induction motor is a ubiquitous machine. In industrial settings, online monitoring of motors' health status in order to schedule maintenance operations with the goal of damage prevention has become an essential necessity. Broken rotor bar is one of the most common failures in the rotor of a squirrel cage motor. Motor current signature analysis (MCSA) has become a popular method for the detection of this failure because of its high reliability. Recent works have performed the MCSA with a combination of different signal processing techniques to identify the presence of broken bars. In this paper, the MCSA is done with empirical mode decomposition from which a set of intrinsic mode functions (IMFs) is obtained. The extracted features from two of the obtained IMFs form the basis of the proposed classification criterion; these are the samples between zero crossings (SBZCs) and the time between successive zero crossings (TSZCs). The standard deviation from the SBZCs and the TSZCs is used as a classification feature. Experimental results using our method show high accuracy in the detection of a broken and a half-broken rotor bar.Index Terms-Broken bar, empirical mode decomposition (EMD), motor current signature analysis (MCSA), squirrel cage motor.

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“…Hence, reliable fault diagnosis is one of the most significant concerns in industry. For the purpose of early fault detection in induction motors, the authors in [3] developed a digital signal processor (DSP)-based FFT analyzer for machinery condition monitoring in real-time. Likewise, Contreras-Medina et al proposed a lowcost field programmable gate array (FPGA)-based multichannel vibration analyzer that can carry out online fault diagnosis of an induction motor [4].…”

Section: Introductionmentioning

confidence: 99%

Real-time and energy-efficient bearing fault diagnosis using discriminative wavelet-based fault features on a multi-core system

Kang

Jeong

Jun

et al. 2015

2015 IEEE Conference on Prognostics and Health Management (PHM)

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This paper presents a comprehensive bearing fault diagnosis methodology to prevent unscheduled interruptions in machinery. This method consists of fault signature extraction, discriminative fault feature selection (not only to improve diagnosis but also to reduce computational overhead), and decision making using a k-nearest neighbor classifier. Although the presented method yields very accurate classification, its computational complexity limits its use in real-time applications. To address that issue, this paper introduces a single instruction, multiple data-based multi-core system including 64 low-cost processing elements to support the high computational fault diagnosis method, where the multi-core system is implemented in a Xilinx Virtex-7 field programmable gate array device, operating at 200 MHz clock frequency. In addition, this study compares the performance of the multi-core system with that of high-performance digital signal processors (DSPs) to demonstrate improvement in execution time and energy efficiency. Experimental results using one-second acoustic emission signals sampled at 1 MHz indicate that the multi-core approach achieves 82.5x and 7.9x speedups over Texas Instruments (TI) TMS320C6748 and TMS320C6671, respectively, when executing the same bearing fault diagnosis algorithm. In addition, the presented multi-core approach reduces 21.5x and 4.2x less energy than TMS320C6748 and TMS320C6671, respectively. Keywords-Acoustic emission; discrete wavelet packet transform; discriminative fault feature selection; energy efficiency; multi-core system; online bearing fault diagnosis I. * Corresponding author 978-1-4799-1894-2/15/$31.00 ©2015 IEEE

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A DSP-based FFT-analyzer for the fault diagnosis of rotating machine based on vibration analysis

Cited by 164 publications

References 17 publications

Condition monitoring of induction motors using combined PSD based wavelet decomposition and selective weighting using spider web plots

Condition monitoring of induction motors using combined PSD based wavelet decomposition and selective weighting using spider web plots

Empirical Mode Decomposition Analysis for Broken-Bar Detection on Squirrel Cage Induction Motors

Real-time and energy-efficient bearing fault diagnosis using discriminative wavelet-based fault features on a multi-core system

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