Centrifugal Pump Cavitation Detection Using Machine Learning Algorithm Technique

Dutta, Nabanita; Umashankar, S.; Shankar, V. K. Arun; Padmanaban, Sanjeevikumar; Leonowicz, Zbigniew; Wheeler, Patrick

doi:10.1109/eeeic.2018.8494594

Cited by 26 publications

(12 citation statements)

References 13 publications

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“…As shown in Fig 1, a centrifugal pump consists of two main parts: the rotating part containing a shaft and an impeller, and the stationary part, which is composed of casing, casing box, bearing and an electrical motor, typically an induction motor [8]. The fluid inside the pump flows axially from the eye of the casing, engaging with the impeller blades and rotating radially to get velocity and pressure to get out of the impeller into the casing's diffuser.…”

Section: Centrifugal Pump Operationmentioning

confidence: 99%

“…The five major causes of cavitation fault are: failure of the pump housing, destruction of the impeller, excessive vibration, higher than necessary power consumption, and decreased flow and pressure. There are five types of cavitation: vaporization, turbulence, vane syndrome, internal recirculation, air aspiration cavitation [8]. According to [35], if run for a long period of time, the cavitation also creates unsteady flow that causes following internal surfaces' failure such as volute, bearing, shaft, seal and etc.…”

Section: D: Cavitationmentioning

confidence: 99%

“…In other words, MCSA uses the motor as a transducer to monitor pump conditions [6]. For example, [7] proposes fault detection in pumps using deep learning, [1] uses neural networks for multi-objective prediction in multi-stage pumps, and [8] investigates cavitation faults using machine learning. However, despite a large amount of work on MCSA-based methods for induction motors [9], a systematic review of MCSA-based techniques for fault detection in centrifugal pumps is not documented in the literature to the best of our knowledge.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Review of Machine Learning Based Fault Detection for Centrifugal Pump Induction Motors

2022

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Centrifugal pumps are an integral part of many industrial processes and are used extensively in water supply, sewage, heating and cooling systems. While there are several review papers on machine learning-based fault diagnosis on induction motors, its application to centrifugal pumps has received relatively little attention. This work attempts to summarize and review recent research and development in machine learning-based pump condition monitoring and fault diagnosis. The paper starts with a brief explanation of pump operation including common pump faults and the main principles of the motor current signature analysis (MCSA) method. This is followed by a detailed explanation of various machine learning-based methods including the types of detected faults, experimental details and reported accuracies. The performances of different approaches are then presented systematically in a unified table. Finally, the authors discuss practical aspects and challenges related to data collection, storage and real-world implementation.

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Section: Centrifugal Pump Operationmentioning

confidence: 99%

Section: D: Cavitationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Review of Machine Learning Based Fault Detection for Centrifugal Pump Induction Motors

2022

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“…Vibration analysis has been also applied to extract cavitation-related features, which can be used in classifiers for fault diagnosis. For that purpose, different types of classifiers and optimizers have been used in order to improve the calculation speed and diagnosis accuracy [7][8][9][10][11]. As the signal-based methods [12][13][14][15] for machinery diagnosis, as well as the model-based methods [16][17][18][19], have been applied, the research on both newer vibration analysis techniques and more advanced machine learning tools for accurate cavitation analysis is still continued.…”

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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“…Indicators for seal damage and cavitation are extracted from the frequency spectra of vibration measurements in (Sun, Yuan, & Luo, 2018). In (Dutta et al, 2018), cavitation is detected using support vector machines for classifying pressure and speed measurements. In (Sanchez, Carvajal, Poalacin, & Salazar, 2018), an alteration in the vibration range at the alabe pitch frequency is reported in the case of cavitation.…”

Section: Introductionmentioning

confidence: 99%

Degradation detection for internal gear pumps using pressure reduction time maps

et al. 2021

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In this paper, a novel approach for detecting degradation in internal gear pumps is proposed. In a data-driven approach, pressure reduction time maps (PRTMs) are identified as a useful indicator for degradation detection. A PRTM measures the time for reducing the internal pump pressure from certain levels to any lower level when the pump engine is stopped and the valves are closed. The PRTM can thus be interpreted as an internal leakage indicator of the pump. For simplified evaluation, PRTMs are compressed to a single scalar indicator by computing their volume (PRTMV). When the internal leakage increases due to wear, the pressure in the pump decreases faster (implying a decreased PRTMV). The proposed approach has been developed and tested with data of real internal gear pumps with different operating times. The PRTMV shows a close relation to the operating time of the pump. Moreover, we compare PRTMV with the commonly used and well known approach of observing pressure holding speed (PHS). Especially for medium degradation, PRTMV shows better sensitivity then PHS.

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Centrifugal Pump Cavitation Detection Using Machine Learning Algorithm Technique

Cited by 26 publications

References 13 publications

Review of Machine Learning Based Fault Detection for Centrifugal Pump Induction Motors

Review of Machine Learning Based Fault Detection for Centrifugal Pump Induction Motors

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

Degradation detection for internal gear pumps using pressure reduction time maps

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