Predictive Maintenance of Ventilator using Pressure and Flow Parameters

Predictive maintenance is an approach that utilizes machine learning algorithms to predict when equipment maintenance is required based on the analysis of real-time data. In this context, this paper proposes a predictive maintenance model to monitor pressure parameters of ventilators using machine learning techniques. The aim is to predict possible malfunctions or breakdowns before they occur, ensuring that the ventilators are always in good working condition, and reducing downtime and maintenance costs. The proposed model is based on the analysis of pressure parameters of ventilators, such as peak pressure, plateau pressure, and positive end-expiratory pressure (PEEP). The model utilizes supervised learning algorithms, such as regression and classification models, to analyse historical data collected from the ventilators. This historical data is used to train the model, and the resulting model is then used to predict future maintenance needs. The results of the study show that the proposed model has high accuracy and can predict maintenance needs with high precision. The model can also identify potential problems in advance, enabling timely intervention and preventing major equipment breakdowns. The implementation of the proposed predictive maintenance model can significantly reduce maintenance costs, improve equipment reliability, and increase patient safety in critical care environments.