Asunto(s)

RESUMEN

BACKGROUND: The Food and Drug Administration (FDA) blood pump is an open-source benchmark cardiovascular device introduced for validating computational and experimental performance analysis tools. The time-resolved velocity field for the whole impeller has not been established, as is undertaken in this particle image velocimetry (PIV) study. The level of instantaneous velocity fluctuations is important, to assess the flow-induced rotor vibrations which may contribute to the total blood damage. METHODS: To document these factors, time-resolved two-dimensional PIV experiments were performed that were precisely phase-locked with the impeller rotation angle. The velocity fields in the impeller and in the volute conformed with the previous single blade passage experiments of literature. RESULTS: Depending on the impeller orientation, present experiments showed that volute outlet nozzle flow can fluctuate up to 34% during impeller rotation, with a maximum standard experimental uncertainty of 2.2%. Likewise, the flow fields in each impeller passage also altered in average 33.5%. Considerably different vortex patterns were observed for different blade passages, with the largest vortical structures reaching an average core radii of 7 mm. The constant volute area employed in the FDA pump design contributes to the observed velocity imbalance, as illustrated in our velocity measurements. CONCLUSIONS: By introducing the impeller orientation parameter for the nozzle flow, this study considers the possible uncertainties influencing pump flow. Expanding the available literature data, analysis of inter-blade relative velocity fields is provided here for the first-time to the best of our knowledge. Consequently, our research fills a critical knowledge gap in the understanding of the flow dynamics of an important benchmark cardiovascular device. This study prompts the need for improved hydrodynamic designs and optimized devices to be used as benchmark test devices, to build more confidence and safety in future ventricular assist device performance assessment studies.

RESUMEN

Centrifugal pump pressure pulsation contains various signals in different frequency domains, which interact and superimpose on each other, resulting in characteristics such as intermittency, non-stationarity, and complexity. Computational Fluid Dynamics (CFD) and traditional time series models are unable to handle nonlinear and non-smooth problems, resulting in low accuracy in the prediction of pressure fluctuations. Therefore, this study proposes a new method for predicting pressure fluctuations. The pressure pulsation signals at the inlet of the centrifugal pump are processed using Variational Mode Decomposition-Particle Swarm Optimization (VMD-PSO), and the signal is predicted by Convolutional Neural Networks-Long Short-Term Memory (CNN-LSTM) model. The results indicate that the proposed prediction model combining VMD-PSO with four neural networks outperforms the single neural network prediction model in terms of prediction accuracy. Relatively high accuracy is achieved by the VMD-PSO-CNN-LSTM model for multiple forward prediction steps, particularly for a forward prediction step of 1 (Pre = 1), with a root mean square error of 0.03145 and an average absolute percentage error of 1.007%. This study provides a scientific basis for the intelligent operation of centrifugal pumps.

RESUMEN

Centrifugal pumps are essential in many industrial processes. An accurate operation diagnosis of centrifugal pumps is crucial to ensure their reliable operation and extend their useful life. In real industry applications, many centrifugal pumps lack flowmeters and accurate pressure sensors, and therefore, it is not possible to determine whether the pump is operating near its best efficiency point (BEP). This paper investigates the detection of off-design operation and cavitation for centrifugal pumps with accelerometers and current sensors. To this end, a centrifugal pump was tested under off-design conditions and various levels of cavitation. A three-axis accelerometer and three Hall-effect current sensors were used to collect vibration and stator current signals simultaneously under each state. Both kinds of signals were evaluated for their effectiveness in operation diagnosis. Signal processing methods, including wavelet threshold function, variational mode decomposition (VMD), Park vector modulus transformation, and a marginal spectrum were introduced for feature extraction. Seven families of machine learning-based classification algorithms were evaluated for their performance when used for off-design and cavitation identification. The obtained results, using both types of signals, prove the effectiveness of both approaches and the advantages of combining them in achieving the most reliable operation diagnosis results for centrifugal pumps.

RESUMEN

To investigate the flow characteristics in front chamber and rear chamber in pump mode and pump as turbine mode, a 3D computational model of a centrifugal pump was established, including the front and rear chamber. Based on Realizable k-ε turbulence model, numerical calculations of incompressible flow were carried out for internal viscous flow in two operating modes. Further analysis was conducted on the flow stability and hydraulic losses under two modes using energy gradient theory and entropy production theory. The numerical simulation results are within reasonable error compared to the experimental results in pump operation mode, which ensures the reliability of the numerical calculation method. The results indicate that the volumetric efficiency in both two modes is on an upward trend with increasing flow, but the volumetric efficiency of the pump mode is more significantly affected by changes in flow; the distribution patterns of dimensionless circumferential velocity and dimensionless radial velocity in the front and rear chambers under two operating modes are similar, but the distribution pattern of dimensionless radial velocity in the front chamber in turbine mode is significantly different from other operating conditions; flow instability is most likely to occur at the outlet of impeller, and the energy loss in clearance of wear-rings is greater than that in the pump chamber.

RESUMEN

The centrifugal pump is the workhorse of many industrial and domestic applications, such as water supply, wastewater treatment and heating. While modern pumps are reliable, their unexpected failures may jeopardise safety or lead to significant financial losses. Consequently, there is a strong demand for early fault diagnosis, detection and predictive monitoring systems. Most prior work on machine learning-based centrifugal pump fault detection is based on either synthetic data, simulations or data from test rigs in controlled laboratory conditions. In this research, we attempted to detect centrifugal pump faults using data collected from real operational pumps deployed in various places in collaboration with a specialist pump engineering company. The detection was done by the binary classification of visual features of DQ/Concordia patterns with residual networks. Besides using a real dataset, this study employed transfer learning from the image detection domain to systematically solve a real-life problem in the engineering domain. By feeding DQ image data into a popular and high-performance residual network (e.g., ResNet-34), the proposed approach achieved up to 85.51% classification accuracy.

RESUMEN

This study introduces an innovative approach for fault diagnosis of a multistage centrifugal pump (MCP) using explanatory ratio (ER) linear discriminant analysis (LDA). Initially, the method addresses the challenge of background noise and interference in vibration signals by identifying a fault-sensitive frequency band (FSFB). From the FSFB, raw hybrid statistical features are extracted in time, frequency, and time-frequency domains, forming a comprehensive feature pool. Recognizing that not all features adequately represent MCP conditions and can reduce classification accuracy, we propose a novel ER-LDA method. ER-LDA evaluates feature importance by calculating the explanatory ratio between interclass distance and intraclass scatteredness, facilitating the selection of discriminative features through LDA. This fusion of ER-based feature assessment and LDA yields the novel ER-LDA technique. The resulting selective feature set is then passed into a k-nearest neighbor (K-NN) algorithm for condition classification, distinguishing between normal, mechanical seal hole, mechanical seal scratch, and impeller defect states of the MCP. The proposed technique surpasses current cutting-edge techniques in fault classification.

RESUMEN

With improved treatment of coronary artery disease, more patients are surviving until heart failure occurs. This leads to an increase in patients needing devices for struggling with heart failure. Ventricular assist devices are known as the mainstay of these devices. This study aimed to design a centrifugal pump as a ventricular assist device. In order to design the pump, firstly, the geometrical parameters of the pump, including the gap distance, blade height, and position of the outlet relative to the blade, were investigated. Finally, the selected configuration, which had all the appropriate characteristics, both hydraulically and physiologically, was used for the rest of the study. The study of the blade, as the main component in energy transfer to the blood, in a centrifugal pump, has been considered in the present study. In this regard, the point-to-point design method, which is used in industrial applications, was implemented. The designer chooses the relationship between the blade angles at each radius in the point-to-point method. The present study selected logarithmic and second-order relations for designing the blade's profile. In total, 58 blades were examined in this study, which differed regarding blade inlet and outlet angles and the relationship between angle and radial position. ANSYS CFX 17.0 software was utilized to simulate blades' performances, and a benchmark pump provided by the US Food and Drug Administration (FDA) was used to validate the numerical simulations. Then, the selected impeller from the numerical investigation was manufactured, and its performance was compared experimentally with the FDA benchmark pump. A hydraulic test rig was also developed for experimental studies. The results showed that among the blades designed in this study, the blade with an input angle of 45° and an output angle of 55°, which is designed to implement a logarithmic relationship, has the best performance. The selected impeller configuration can increase the total head (at least by 20%) at different flow rates compared to the FDA pump.

Asunto(s)

RESUMEN

This paper proposes a new fault diagnosis method for centrifugal pumps by combining signal processing with deep learning techniques. Centrifugal pumps facilitate fluid transport through the energy generated by the impeller. Throughout the operation, variations in the fluid pressure at the pump's inlet may impact the generalization of traditional machine learning models trained on raw statistical features. To address this concern, first, vibration signals are collected from centrifugal pumps, followed by the application of a lowpass filter to isolate frequencies indicative of faults. These signals are then subjected to a continuous wavelet transform and Stockwell transform, generating two distinct time-frequency scalograms. The Sobel filter is employed to further highlight essential features within these scalograms. For feature extraction, this approach employs two parallel convolutional autoencoders, each tailored for a specific scalogram type. Subsequently, extracted features are merged into a unified feature pool, which forms the basis for training a two-layer artificial neural network, with the aim of achieving accurate fault classification. The proposed method is validated using three distinct datasets obtained from the centrifugal pump under varying inlet fluid pressures. The results demonstrate classification accuracies of 100%, 99.2%, and 98.8% for each dataset, surpassing the accuracies achieved by the reference comparison methods.

RESUMEN

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.

RESUMEN

Asynchronous rotational-speed modulation of a continuous-flow left ventricular assist device (LVAD) can increase pulsatility; however, the feasibility of hemodynamic modification by asynchronous modulation of an LVAD has not been sufficiently verified. We evaluated the acute effect of an asynchronous-modulation mode under LVAD support and the accumulated effect of 6 consecutive hours of driving by the asynchronous-modulation mode on hemodynamics, including both ventricles, in a coronary microembolization-induced acute-myocardial injury sheep model. We evaluated 5-min LVAD-support hemodynamics, including biventricular parameters, by switching modes from constant-speed to asynchronous-modulation in the same animals ("acute-effect evaluation under LVAD support"). To determine the accumulated effect of a certain driving period, we evaluated hemodynamics including biventricular parameters after weaning from 6-hour (6 h) LVAD support by constant-speed or asynchronous-modulation mode ("6h-effect evaluation"). The acute-effect evaluation under LVAD support revealed that, compared to the constant-speed mode, the asynchronous-modulation mode increased vascular pulsatility but did not have significantly different effects on hemodynamics, including both ventricles. The 6 h-effect evaluation revealed that the hemodynamics did not differ significantly between the two groups except for some biventricular parameters which did not indicate negative effects of the asynchronous-modulation mode on both ventricles. The asynchronous-modulation mode could be feasible to increase vascular pulsatility without causing negative effects on hemodynamics including both ventricles. Compared to the constant-speed mode, the asynchronous-modulation mode increased pulsatility during LVAD support without negative effects on hemodynamics including both ventricles in the acute phase. Six hours of LVAD support with the asynchronous-modulation mode exerted no negative effects on hemodynamics, including both ventricles, after weaning from the LVAD.

Asunto(s)

RESUMEN

BACKGROUND: The objective of this study was to design a new wireless left ventricular assist device (LVAD) that can be charged without using a conventional transcutaneous energy transfer system (TETS). METHODS: Our new wireless LVAD was a hybrid pump operating in two different modes: magnetic and electric modes. The pump was driven wirelessly by extracorporeal rotating magnets in magnetic mode, whereas it was driven by electricity provided by an intracorporeal battery in electric mode. A magnetic torque transmission system was introduced to wirelessly transmit torque to the pump impeller. The intracorporeal battery was charged in magnetic mode making use of electromagnetic coils as a generator, whereas the coils were used as a motor in electric mode. To demonstrate the feasibility of our system, we conducted a bench-top durability test for 1 week. RESULTS: Our hybrid pump had shown sufficient pump performance as a LVAD, with a head pressure of approximately 80 mm Hg and a flow volume of 5.0 L/min, for 1 week. The intracorporeal battery was wirelessly charged enough to power electric mode for 2.5 h a day throughout the 1-week durability test. CONCLUSIONS: Our hybrid wireless LVAD system demonstrated the possibility of a wireless LVAD and has the potential to reduce medical complications of LVAD therapy.

Asunto(s)

RESUMEN

This paper proposes an intelligent framework for the fault diagnosis of centrifugal pumps (CPs) based on wavelet coherence analysis (WCA) and deep learning (DL). The fault-related impulses in the CP vibration signal are often attenuated due to the background interference noises, thus affecting the sensitivity of the traditional statistical features towards faults. Furthermore, extracting health-sensitive information from the vibration signal needs human expertise and background knowledge. To extract CP health-sensitive features autonomously from the vibration signals, the proposed approach initially selects a healthy baseline signal. The wavelet coherence analysis is then computed between the healthy baseline signal and the signal obtained from a CP under different operating conditions, yielding coherograms. WCA is a signal processing technique that is used to measure the degree of linear correlation between two signals as a function of frequency. The coherograms carry information about the CP vulnerability towards the faults as the color intensity in the coherograms changes according to the change in CP health conditions. To utilize the changes in the coherograms due to the health conditions of the CP, they are provided to a Convolution Neural Network (CNN) and a Convolution Autoencoder (CAE) for the extraction of discriminant CP health-sensitive information autonomously. The CAE extracts global variations from the coherograms, and the CNN extracts local variations related to CP health. This information is combined into a single latent space vector. To identify the health conditions of the CP, the latent space vector is classified using an Artificial Neural Network (ANN). The proposed method identifies faults in the CP with higher accuracy as compared to already existing methods when it is tested on the vibration signals acquired from real-world industrial CPs.

RESUMEN

The real-time diagnostic monitoring of self-priming centrifugal pumps is essential to ensure their safe operation. Nevertheless, owing to the intricate structure and complex operational conditions inherent in such pumps, existing fault diagnosis methods encounter challenges in effectively extracting crucial fault feature information and accurately identifying fault types. Consequently, this paper introduces an intelligent fault diagnosis method tailored for self-priming centrifugal pumps. The approach amalgamates refined time-shift multiscale fluctuation dispersion entropy, cosine pairwise-constrained supervised manifold mapping, and adaptive chaotic Aquila optimization support vector machine techniques. To begin with, refined time-shift multiscale fluctuation dispersion entropy is employed to extract fault-related features, adeptly mitigating concerns related to entropy domain deviations and instability. Subsequently, the application of cosine pairwise-constrained supervised manifold mapping serves to reduce the dimensionality of the extracted fault features, thereby bolstering the efficiency and precision of the ensuing identification process. Ultimately, the utilization of an adaptive chaotic Aquila optimization support vector machine facilitates intelligent fault classification, leading to enhanced accuracy in fault identification. The experimental findings unequivocally affirm the efficacy of the proposed method in accurately discerning among various fault types in self-priming centrifugal pumps, achieving an exceptional recognition rate of 100%. Moreover, it is noteworthy that the average correct recognition rate achieved by the proposed method surpasses that of five existing intelligent fault diagnosis techniques by a significant margin, registering a notable increase of 15.97%.

RESUMEN

The estimation of fluid flows inside a centrifugal pump in realtime is a challenging task that cannot be achieved with long-established methods like CFD due to their computational demands. We use a projection-based reduced order model (ROM) instead. Based on this ROM, a realtime observer can be devised that estimates the temporally and spatially resolved velocity and pressure fields inside the pump. The entire fluid-solid domain is treated as a fluid in order to be able to consider moving rigid bodies in the reduction method. A greedy algorithm is introduced for finding suitable and as few measurement locations as possible. Robust observability is ensured with an extended Kalman filter, which is based on a time-variant observability matrix obtained from the nonlinear velocity ROM. We present the results of the velocity and pressure ROMs based on a unsteady Reynolds-averaged Navier-Stokes CFD simulation of a 2D centrifugal pump, as well as the results for the extended Kalman filter.

RESUMEN

This work presents a technique for fault detection and identification in centrifugal pumps (CPs) using a novel fault-specific Mann-Whitney test (FSU Test) and K-nearest neighbor (KNN) classification algorithm. Traditional fault indicators, such as the mean, peak, root mean square, and impulse factor, lack sensitivity in detecting incipient faults. Furthermore, for defect identification, supervised models rely on pre-existing knowledge about pump defects for training purposes. To address these concerns, a new centrifugal pump fault indicator (CPFI) that does not rely on previous knowledge is developed based on a novel fault-specific Mann-Whitney test. The new fault indicator is obtained by decomposing the vibration signature (VS) of the centrifugal pump hierarchically into its respective time-frequency representation using the wavelet packet transform (WPT) in the first step. The node containing the fault-specific frequency band is selected, and the Mann-Whitney test statistic is calculated from it. The combination of hierarchical decomposition of the vibration signal for fault-specific frequency band selection and the Mann-Whitney test form the new fault-specific Mann-Whitney test. The test output statistic yields the centrifugal pump fault indicator, which shows sensitivity toward the health condition of the centrifugal pump. This indicator changes according to the working conditions of the centrifugal pump. To further enhance fault detection, a new effect ratio (ER) is introduced. The KNN algorithm is employed to classify the fault type, resulting in promising improvements in fault classification accuracy, particularly under variable operating conditions.

RESUMEN

BACKGROUND: The objective of this animal study was to evaluate the hemodynamic performance of a new centrifugal pump for extra-corporeal membrane oxygenation (ECMO) support in neonates. METHODS: Six healthy swines were supported with veno-venous ECMO with the New Born ECMOLife centrifugal pump (Eurosets, Medolla, Italy) at different flow rates: 0.25, 0.5, 0.6, and 0.8 L/min; three animals were evaluated at low-flows (0.25 and 0.5 L/min) and three at high-flows (0.6 and 0.8 L/min). Each flow was maintained for 4 hours. Blood samples were collected at different time-points. Hematological and biochemical parameters and ECMO parameters [flow, revolutions per minute (RPM), drainage pressure, and the oxygenator pressure drop] were evaluated. RESULTS: The increase of the pump flow from 0.25 to 0.5 L/min or from 0.6 to 0.8 L/min required significantly higher RPM and produced significantly higher pump pressures [from 0.25 to 0.5 L/min: 1470 (1253-1569) versus 2652 (2589-2750) RPM and 40 (26-57) versus 125 (113-139) mmHg, respectively; p < .0001 for both - from 0.60 to 0.8 L/min: 1950 (1901-2271) versus 2428 (2400-2518) RPM and 66 (62-86) versus 106 (101-113) mmHg, respectively; p < .0001 for both]. Median drainage pressure significantly decreased from -18 (-22; -16) mmHg to -55 (-63; -48) mmHg when the pump flow was increased from 0.25 to 0.5 L/min (p < .0001). When pump flow increased from 0.6 to 0.8 L/min, drainage pressure decreased from -32 (-39; -24) mmHg to -50 (-52; -43) mmHg, (p < .0001). Compared to pre-ECMO values, the median levels of lactate dehydrogenase, d-dimer, hematocrit, and platelet count decreased after ECMO start at all flow rates, probably due to hemodilution. Plasma-free hemoglobin, instead, showed a modest increase compared to pre-ECMO values during all experiments at different pump flow rates. However, these changes were not clinically relevant. CONCLUSIONS: In this animal study, the "New Born ECMOLife" centrifugal pump showed good hemodynamic performance. Long-term studies are needed to evaluate biocompatibility of this new ECMO pump.

RESUMEN

The ability to provide antegrade cerebral and systemic perfusion simultaneously may negate the requirement for any prolonged period of circulatory arrest during complex aortic arch reconstruction procedures, depending on the cannulation strategy. We describe the development and successful implementation of a custom 'split arterial line' extracorporeal circuit configuration to facilitate complex aortic surgery. This circuit design offers a wide range of cannulation and perfusion strategies, is safe, adaptable, simple to manage, and avoids the use of roller pumps for blood delivery, which are associated with deleterious haematological complications during prolonged cardiopulmonary bypass cases. The split arterial line approach has now become the standardised methodology for facilitating complex aortic surgery at our institution.

RESUMEN

INTRODUCTION: Well-timed explant of veno-arterial extracorporeal life support (V-A ECLS) depends on adequate assessment of cardiac recovery. Often, evaluation of cardiac recovery consists of reducing support flow while visualizing cardiac response using transoesophageal echocardiography (TEE). This method, however, is time consuming and based on subjective findings. The dynamic filling index (DFI) may aid in the quantitative assessment of cardiac load-responsiveness. The dynamic filling index is based on the relationship of support flow and pump speed, which varies with varying hemodynamic conditions. This case series intends to investigate whether the DFI may support TEE in facilitating the assessment of cardiac load-responsiveness. METHODS: Measurements for DFI-determination were performed in seven patients while simultaneously assessing ventricular function by measuring the aortic velocity time integral (VTI) using TEE. Measurements consisted of multiple consecutive transient speed manipulations (∼100 r/min) during weaning trials, both at full support and during cardiac reloading at reduced support. RESULTS: The VTI increased between full and reduced support in six weaning trials. In five of these trials DFI decreased or remained equal, and in one case DFI increased. Of the three trials in which VTI decreased between full and reduced support, DFI increased in two cases and decreased in one case. Changes in DFI, however, are mostly smaller than the detection threshold of 0.4 mL/rotation. CONCLUSION: Even though current level of accuracy of the parameter requires further investigation to increase reliability and possibly predictability, DFI seems likely to be a potential parameter in supporting TEE for the assessment of cardiac load-responsiveness.