Vibration based centrifugal pump fault diagnosis based on modulation signal bispectrum analysis

Hamomd, Osama; Alabied, Samir; Xu, Yuandong; Daraz, Alsadak; Gu, Fengshou; Ball, Andrew

doi:10.23919/iconac.2017.8082029

Cited by 12 publications

(9 citation statements)

References 4 publications

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“…The high performance of the MSB has been demonstrated extensively in fault detection and diagnosis of bearings, 31 gearboxes, 33 motors 34 and centrifugal pumps. 36 However, the implementation of the MSB is based on the conventional Fourier transform, which is not an efficient way to tackle cyclostationary signals. The random phase noise induced by the speed fluctuation and random slippage leads to the nonstationary signals, which goes beyond the capability of the Fourier transform.…”

Section: Methodology Of the Pl-msbmentioning

confidence: 99%

A phase linearisation–based modulation signal bispectrum for analysing cyclostationary bearing signals

et al. 2020

Structural Health Monitoring

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Bearings are used as the most important load-carrying transmission components in various machines, thus subjecting to a number of faults including wear, fatigue pitting, cracks and so on. Fault detection and diagnosis of bearings can effectively prevent the machine from such typical failures and subsequent consequences. The faults in bearings can lead to the vibration signals that exhibit cyclostationary characteristics due to the inevitably random phase noise (or slippage between bearing components). In this article, a phase linearisation–based modulation signal bispectrum is proposed to tune up the cyclostationary bearing signal into a periodic waveform by linearizing the instantaneous phase of the narrow frequency band signals. In this way, the signal becomes more deterministic and modulation signal bispectrum can be effectively applied to suppression noise and obtain accurate and robust diagnosis results. As a result, this fault detector can achieve high performance in characterising the nonstationary bearing vibration signals and hence diagnose the bearing faults even in the case of extremely low signal-to-noise ratio (<−20 dB), which is benchmarked by the method of conventional modulation signal bispectrum in both simulation and experiment studies.

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Section: Methodology Of the Pl-msbmentioning

confidence: 99%

A phase linearisation–based modulation signal bispectrum for analysing cyclostationary bearing signals

et al. 2020

Structural Health Monitoring

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“…A method for fault detection in impellers and bearings of a centrifugal pump was proposed by Hamomd et al [ 34 ]. The modulation signal bispectrum (MSB) method is the methodological tool used for analyzing recorded vibration signals to perform fault detection.…”

Section: Introductionmentioning

confidence: 99%

Scale-Fractal Detrended Fluctuation Analysis for Fault Diagnosis of a Centrifugal Pump and a Reciprocating Compressor

Medina,

Sánchez,

Cabrera

et al. 2024

Sensors

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Reciprocating compressors and centrifugal pumps are rotating machines used in industry, where fault detection is crucial for avoiding unnecessary and costly downtime. A novel method for fault classification in reciprocating compressors and multi-stage centrifugal pumps is proposed. In the feature extraction stage, raw vibration signals are processed using multi-fractal detrended fluctuation analysis (MFDFA) to extract features indicative of different types of faults. Such MFDFA features enable the training of machine learning models for classifying faults. Several classical machine learning models and a deep learning model corresponding to the convolutional neural network (CNN) are compared with respect to their classification accuracy. The cross-validation results show that all models are highly accurate for classifying the 13 types of faults in the centrifugal pump, the 17 valve faults, and the 13 multi-faults in the reciprocating compressor. The random forest subspace discriminant (RFSD) and the CNN model achieved the best results using MFDFA features calculated with quadratic approximations. The proposed method is a promising approach for fault classification in reciprocating compressors and multi-stage centrifugal pumps.

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“…However, it has been applied in fault diagnosis of pumps only in limited number of studies despite its promising results. Hamomd et al [24] used the modulation signal bispectrum in order to diagnose a number of faults including bearing and vane blockage in centrifugal pumps.…”

Section: Introductionmentioning

confidence: 99%

Cavitation Analysis in Centrifugal Pumps Based on Vibration Bispectrum and Transfer Learning

Hajnayeb

2021

Shock and Vibration

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Detection of cavitation in centrifugal pumps is critical in their condition monitoring. In order to detect cavitation more accurately and confidently, more advanced signal processing techniques are needed. For the classification of a pump conditions based on the outputs of these techniques, advanced machine learning techniques are needed. In this research, an automatic system for cavitation detection is proposed based on machine learning. Bispectral analysis is used for analyzing the vibration signals. The resulting bispectrum images are given to convolutional neural networks (CNNs) as inputs. The CNNs are a pretrained AlexNet and a pretrained GoogleNet, which are used in this application through transfer learning. On the contrary, a laboratory test setup is used for generating controlled cavitation in a centrifugal pump. The suggested algorithm is implemented on the vibration dataset acquired from the laboratory pump test setup. The results show that the cavitation state of the pump can be detected accurately using this system without any need to image processing or feature extraction.

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Vibration based centrifugal pump fault diagnosis based on modulation signal bispectrum analysis

Cited by 12 publications

References 4 publications

A phase linearisation–based modulation signal bispectrum for analysing cyclostationary bearing signals

A phase linearisation–based modulation signal bispectrum for analysing cyclostationary bearing signals

Scale-Fractal Detrended Fluctuation Analysis for Fault Diagnosis of a Centrifugal Pump and a Reciprocating Compressor

Cavitation Analysis in Centrifugal Pumps Based on Vibration Bispectrum and Transfer Learning

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