Innovative data techniques for centrifugal pump optimization with machine learning and AI model
-д хадгалсан:
| -д хэвлэсэн: | PLoS One vol. 20, no. 6 (Jun 2025), p. e0325952 |
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| Үндсэн зохиолч: | |
| Бусад зохиолчид: | , , |
| Хэвлэсэн: |
Public Library of Science
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| Нөхцлүүд: | |
| Онлайн хандалт: | Citation/Abstract Full Text Full Text - PDF |
| Шошгууд: |
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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MARC
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| 001 | 3217665605 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1932-6203 | ||
| 024 | 7 | |a 10.1371/journal.pone.0325952 |2 doi | |
| 035 | |a 3217665605 | ||
| 045 | 2 | |b d20250601 |b d20250630 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Dave, Gaurav Sandeep | |
| 245 | 1 | |a Innovative data techniques for centrifugal pump optimization with machine learning and AI model | |
| 260 | |b Public Library of Science |c Jun 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a In modern centrifugal pump machines (CPM), a data acquisition system encompassing software- hardware interfacing is essential for parameter recording. The quality of recorded data plays a crucial role and directly influences the data transformation phase in machine learning (ML) and deep learning (DL) models. The Dewesoft FFT DAQ system is designed to extract the high-quality data from the CPM based on sensor fusion technology. The data recorded from DAQ system undergoes thorough in-depth analysis, processing & transformation before being incorporated into machine learning (ML) or artificial intelligence models. This paper emphasizes the importance of data cleaning, pre-processing, and applying appropriate methodologies to transform raw data into a valuable resource that can be utilized by ML and AI models. Key techniques include Exploratory Data Analysis (EDA), Data Visualization, and Feature Engineering (FE), which collectively enhance data interpretability. Following these transformations, hypothesis testing validates the data’s integrity, ensuring reliability for subsequent modeling. The validated data is employed to train machine learning classifiers and deep learning algorithms, targeting a 27.25% enhancement in operational efficiency based on F1 score. Additionally, it decreases model training time by 180 seconds, facilitating predictive maintenance of critical performance metrics and minimizing downtime. The assessment of model performance relies on Precision, Recall, and F1 score. This approach leverages recent advancements in data science to derive actionable insights from CPM data, facilitating more informed decision-making and optimization of pump operations. | |
| 653 | |a Data acquisition | ||
| 653 | |a Scientific visualization | ||
| 653 | |a Data analysis | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Datasets | ||
| 653 | |a Signal processing | ||
| 653 | |a Machine learning | ||
| 653 | |a Automation | ||
| 653 | |a Deep learning | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Data processing | ||
| 653 | |a Performance measurement | ||
| 653 | |a Data science | ||
| 653 | |a Sensors | ||
| 653 | |a Optimization | ||
| 653 | |a Algorithms | ||
| 653 | |a Downtime | ||
| 653 | |a Decision making | ||
| 653 | |a Predictive maintenance | ||
| 653 | |a Data visualization | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Amar, Pradeep Pandhare | |
| 700 | 1 | |a Atul Prabhakar Kulkarni | |
| 700 | 1 | |a Khankal, Dhananjay Vasant | |
| 773 | 0 | |t PLoS One |g vol. 20, no. 6 (Jun 2025), p. e0325952 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3217665605/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3217665605/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3217665605/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |