Research on the Single-Value Indicators for Centrifugal Pump Based on Vibration Signals

Luo, Yin; Han, Yuejiang; Yuan, Shouqi; Yuan, Jianping

doi:10.3390/s20113283

Cited by 3 publications

(4 citation statements)

References 25 publications

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“…The main challenge in this application is extracting features from the vibration signals that can accurately represent the faults for effective fault detection. One limitation is that fault signatures are often feeble signals, masked by heavy background noise [ 9 ]. Most of the time, feature extraction methods require complex preprocessing to improve the signal-to-noise ratio.…”

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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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

View full text Add to dashboard Cite

show abstract

“…Using online monitoring of the centrifugal pump flow, pressure, head, and other performance parameters, Liu et al identified the four states of cavitation, loosening, rotor misalignment, and rotor unbalance according to threshold comparison method. Luo et al [5] established the relationship between characteristics and state by analyzing the time domain and frequency domain characteristics of the vibration signal of the centrifugal pump. Cao et al [6] extracted 15 characteristic indexes of the vibration acceleration signal of the centrifugal pump, established the relationship between cavitation and characteristic indexes, and realized cavitation state monitoring of the centrifugal pump according to the characteristics of the vibration signal.…”

Section: Introductionmentioning

confidence: 99%

Status Recognition of Marine Centrifugal Pumps Based on a Stacked Sparse Auto-Encoder

He,

Yao,

2024

Applied Sciences

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Marine centrifugal pumps (MCPs) are widely used in ships, so it is important to identify their status accurately for their maintenance. Due to the influence of load, friction, and other non-linear factors, the vibration signal of an MCP shows non-linear and non-stationary characteristics, and it is difficult to extract the state characteristics contained in the vibration signal. To solve the difficulty of feature extraction of non-linear non-stationary vibration signals generated by MCPs, a novel MCP frequency domain signal feature extraction method based on a stacked sparse auto-encoder (SSAE) is proposed. The characteristic parameters of MCP frequency domain signals are extracted via the SSAE model for classification training, and different statuses of MCPs are identified. The vibration signals in different MCP statuses were collected for feature extraction and classification training, and the MCP status recognition accuracy based on the time domain feature and fuzzy entropy feature was compared. According to the test data, the accuracy of MCP status recognition based on the time domain feature is 71.2%, the accuracy of MCP status recognition based on the fuzzy entropy feature is 87.7%, and the accuracy of MCP status recognition based on the proposed method is 100%. These results show that the proposed method can accurately identify each status of an MCP under test conditions.

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“…Centrifugal pumps are widely used in construction, municipal water supply and drainage, petroleum and chemical industries, thermal power and other industries [1,2]. Most pumps are driven by motors and their electricity consumption is huge [3,4]. Due to the high energy consumption of centrifugal pumps, the frequency conversion technology has been widely adopted to adjust the speed of centrifugal pump by means of a frequency converter, thus plays an important role in energy saving in the pump industry [5].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, several neural networks and data-driven empirical models (also named as soft sensors/analyzers) have been developed alternatively in the fluid machinery field [1,2,4,[19][20][21][22][23][24][25][26][27]. Compared with mechanism models, the process data can reflect the characteristics without a sufficient understanding of the mechanism.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Hybrid Model for Efficiency Prediction of Centrifugal Pump

Liu

Xia

Deng

et al. 2022

Sensors

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Accurately predict the efficiency of centrifugal pumps at different rotational speeds is important but still intractable in practice. To enhance the prediction performance, this work proposes a hybrid modeling method by combining both the process data and knowledge of centrifugal pumps. First, according to the process knowledge of centrifugal pumps, the efficiency curve is divided into two stages. Then, the affinity law of pumps and a Gaussian process regression (GPR) model are explored and utilized to predict the efficiency at their suitable flow stages, respectively. Furthermore, a probability index is established through the prediction variance of a GPR model and Bayesian inference to select a suitable training set to improve the prediction accuracy. Experimental results show the superiority of the hybrid modeling method, compared with only using mechanism or data-driven models.

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Research on the Single-Value Indicators for Centrifugal Pump Based on Vibration Signals

Cited by 3 publications

References 25 publications

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

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

Status Recognition of Marine Centrifugal Pumps Based on a Stacked Sparse Auto-Encoder

Two-Stage Hybrid Model for Efficiency Prediction of Centrifugal Pump

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