2836-7863ISSN (Online)
Volume 2 , Issue 2 , PP: 15-23, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Samandarboy Sulaymanov 1 *
Doi: https://doi.org/10.54216/NIF.020202
This study focuses on the task of maintenance, in pump systems by utilizing a combination of multi dimensional sensor fusion and advanced machine learning techniques. Pump systems play a role in settings but unexpected failures can lead to significant disruptions and operational inefficiencies. The goal of this research is to predict and prevent these failures effectively. To achieve this we analyzed a dataset consisting of 52 sensor units and over 220,000 readings. By applying Principal Component Analysis (PCA) we were able to extract information and reduce complexity gaining an understanding of how the pump system behaves. We then utilized Long Short Term Memory (LSTM) networks to learn from the combined sensor data enabling predictions and early detection of faults that're vital for proactive maintenance strategies. Our findings demonstrate the potential of these methodologies. The integration of sensor data sources and the use of PCA for dimensionality reduction allowed us to obtain a view while LSTM networks effectively captured the temporal dynamics present, in the sensor data leading to precise predictions regarding system behavior.
Sensor Integration , Predictive Analytics , Machine Learning , Data Fusion , Condition Monitoring , Fault Detection , Pump Performance , Prognostics , Sensor Networks , Maintenance Optimization
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