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

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.

On the acoustic response of ultrasound and induced cell death

Ultrasonically-stimulated microbubbles can enhance cell membrane permeability and decrease cell viability where the underlying acoustic mechanism has been associated with both non-inertial and inertial cavitation. In this study, breast cancer cells (MDA-MB-231) were exposed to 0.5MHz ultrasound pulses of 16μs duration at varying peak negative pressures (PNP: 218kPa, 335kPa and 908kPa) and pulse repetition period (PRP 10ms and 100ms) in the presence of Definity microbubbles (3.3% v/v). The acoustic response of microbubbles was measured using passive cavitation detection with 2.25MHz transducer, and characterized by their frequency a cavitation dose (CD). Results show that the number of non-viable cells and integrated cavitation dose (ICD) significantly increases with PNP, whereas no significant differences were found between 10ms and 100ms PRPs. In this study, no correlation was found between (ICD) and cell non-viability.

On the acoustic response of ultrasound and induced cell death

Ultrasonically-stimulated microbubbles can enhance cell membrane permeability and decrease cell viability where the underlying acoustic mechanism has been associated with both non-inertial and inertial cavitation. In this study, breast cancer cells (MDA-MB-231) were exposed to 0.5MHz ultrasound pulses of 16μs duration at varying peak negative pressures (PNP: 218kPa, 335kPa and 908kPa) and pulse repetition period (PRP 10ms and 100ms) in the presence of Definity microbubbles (3.3% v/v). The acoustic response of microbubbles was measured using passive cavitation detection with 2.25MHz transducer, and characterized by their frequency a cavitation dose (CD). Results show that the number of non-viable cells and integrated cavitation dose (ICD) significantly increases with PNP, whereas no significant differences were found between 10ms and 100ms PRPs. In this study, no correlation was found between (ICD) and cell non-viability.

Synergistic Cisplatin-induced cell death by ultrasound-microbubble mediated intracellular delivery in breast cancer cells.

Ultrasound-microbubble (USMB) potentiated cisplatin (CDDP) therapy was assessed in human breast cancer cells. Cells, MDA-MB-231, in suspension were exposed to USMB and CDDP at varying conditions, during which microbubble cavitation activity was measured using passive cavitation detection and 48 hours post-treatment cell viability and intracellular platinum concentration were measured using MTT assay and mass cytometry, respectively. USMB synergistically enhanced cell death (~20 fold) when combined with CDDP and significantly increased intracellular CDDP concentration (~8 fold) compared to CDDP treatment alone. Cell death and intracellular CDDP concentration were correlated to microbubble cavitation activity, which increased with peak negative pressure and microbubble concentration. Combined treatment of USMB and CDDP at relatively lower integrated cavitation dose (ICD) induced a synergistic effect on cell death whereas ICD greater than 10 induced an additive effect. USMB mediated CDDP intracellular accumulation synergistically enhances cell death in CDDPresistant breast cancer cells.

Synergistic Cisplatin-induced cell death by ultrasound-microbubble mediated intracellular delivery in breast cancer cells.

Ultrasound-microbubble (USMB) potentiated cisplatin (CDDP) therapy was assessed in human breast cancer cells. Cells, MDA-MB-231, in suspension were exposed to USMB and CDDP at varying conditions, during which microbubble cavitation activity was measured using passive cavitation detection and 48 hours post-treatment cell viability and intracellular platinum concentration were measured using MTT assay and mass cytometry, respectively. USMB synergistically enhanced cell death (~20 fold) when combined with CDDP and significantly increased intracellular CDDP concentration (~8 fold) compared to CDDP treatment alone. Cell death and intracellular CDDP concentration were correlated to microbubble cavitation activity, which increased with peak negative pressure and microbubble concentration. Combined treatment of USMB and CDDP at relatively lower integrated cavitation dose (ICD) induced a synergistic effect on cell death whereas ICD greater than 10 induced an additive effect. USMB mediated CDDP intracellular accumulation synergistically enhances cell death in CDDPresistant breast cancer cells.

A Method for Automated Cavitation Detection with Adaptive Thresholds

Hydroturbine operators who wish to collect cavitation intensity data to estimate cavitation erosion rates and calculate remaining useful life (RUL) of the turbine runner face several practical challenges related to long term cavitation detection. This paper presents a novel method that addresses these challenges including: a method to create an adaptive cavitation threshold, and automation of the cavitation detection process. These two strategies result in collecting consistent cavitation intensity data. While domain knowledge and manual interpretation are used to choose an appropriate cavitation sensitivity parameter (CSP), the remainder of the process is automated using both supervised and unsupervised learning methods. A case study based on ramp-down data, taken from a production hydroturbine, is presented and validated using independently gathered survey data from the same hydroturbine. Results indicate that this fully automated process for selecting cavitation thresholds and classifying cavitation performs well when compared to manually selected thresholds. This approach provides hydroturbine operators and researchers with a clear and effective way to perform automated, long term, cavitation detection, and assessment.

Feature Selection for Monitoring Erosive Cavitation on a Hydroturbine

This paper presents a method for comparing and evaluating cavitation detection features - the first step towards estimating remaining useful life (RUL) of hydroturbine runners that areimpacted by erosive cavitation. The method can be used to quickly compare features created from cavitation survey data collected on any type of hydroturbine, sensor type, sensor location, and cavitation sensitivity parameter (CSP). Although manual evaluation and knowledge of hydroturbine cavitation is still required for our feature selection method, the use of principal component analysis greatly reduces the number of plots that require evaluation. We present a case study based on a cavitation survey data collected on a Francis hydroturbine located at a hydroelectric plant and demonstrate the selection of the most advantageous sensor type, sensor location, and CSP to use on this hydroturbine for long-term monitoring of erosive cavitation. Our method provides hydroturbine operators and researchers with a clear and effective means to determine preferred sensors, sensor placements, and CSPs while also laying the groundwork for determining RUL in the future.