الصيانة التنبؤية لمحطة الصبية للطاقة البخارية باستخدام خوارزميات التعلم الآلي
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Al Sabiya steam power plant in Kuwait. 3.3.1 Selection and Rationale of Input Parameters In this sub-section, the rationale and meticulous selection process behind each of the 5 chosen input parameters are discussed in detail. The significance of temperature, pressure, running hours, flow, and alerts from the boiler feed pump in predicting maintenance needs is outlined, taking into account the dynamics of a steam power plant. The selection of input parameters in any predictive maintenance model is a critical decision that significantly influences the model's accuracy and effectiveness in assessing machinery health [79]. In the context of predictive maintenance for a steam power plant utilizing TinyML, the careful consideration of input parameters is of paramount importance to ensure the model's ability to predict maintenance needs accurately and in a timely manner [26]. I. Temperature Temperature is a fundamental parameter in a steam power plant as it directly affects the efficiency and safety of the system. Variations in temperature can indicate potential issues such as equipment overheating or inadequate cooling. Sudden spikes or drops in temperature can be early indicators of malfunctioning components or insufficient coolant flow, signalling the need for maintenance [77]. II. Pressure Pressure within a steam power plant is a critical parameter to monitor as it reflects the operational state of various components. Deviations in pressure levels can point towards leakages, blockages, or inefficiencies in the system. Sudden pressure drops or sustained high pressures can trigger alerts for maintenance actions, allowing for timely interventions [77,79]. III. Running Hours Running hours represent the cumulative operational time of the machinery or specific components. Monitoring running hours enables the prediction of when certain parts might reach their maintenance thresholds. Components that require maintenance after a specified number of operating hours can be scheduled for servicing, optimizing their performance and extending their lifespan [77]. IV. Flow Flow, particularly the flow of fluids or steam, is a crucial parameter in steam power plants. Anomalies in flow rates can indicate issues like blockages or leakages in pipes, valves, or other components. Monitoring flow helps in ensuring the smooth operation of the plant and provides insights into the health of the system [77, 80]. V. Alert of Boiler Feed Pump Boiler feed pump alerts are crucial as they often serve as early indicators of potential problems in the boiler or associated systems. These alerts can include a variety of conditions such as pump malfunctions, low water levels, or abnormal pressure conditions. Incorporating these alerts as an input parameter enables the predictive maintenance model to proactively address boiler-related issues [78]. The rationale behind selecting these input parameters is deeply rooted in their direct influence on the operational state of a steam power plant [69]. These parameters are highly sensitive to changes and anomalies, making them reliable indicators of potential maintenance needs. By monitoring these parameters closely, deviations from normal operating conditions can be detected, allowing for proactive maintenance measures [79]. In summary, the chosen input parameters—temperature, pressure, running hours, flow, and alerts of the boiler feed pump form a comprehensive set that represents critical aspects of the steam power plant's operation. Their selection is justified by their direct relevance to the health and efficiency of the plant, providing a solid foundation for the predictive maintenance model using TinyML. 3.3.2 Definition and Categorization of Output Parameters This sub-section expounds on the precise definitions and categorizations assigned to the 4 output parameters: 'normal', 'abnormal', 'early maintenance', and 'annual maintenance'. The criteria used to categorize maintenance needs and the importance of each category in the context of predictive maintenance for a steam power plant are outlined. The definition and categorization of output parameters in a predictive maintenance model are essential components that guide the system in making informed decisions regarding maintenance needs. In the context of predictive maintenance for a steam power plant using TinyML, the precise definition and categorization of output parameters are crucial to effectively classify the health status of the machinery and determine the appropriate maintenance actions [87]. I. 'Normal' Condition The 'normal' output parameter indicates that, based on the input parameters and model analysis, the steam power plant is in an expected and stable operational state. This condition implies that no immediate maintenance actions are required, and the system is functioning within acceptable operational parameters. II. 'Abnormal' Condition The 'abnormal' output parameter signifies that the predictive maintenance model has identified deviations or anomalies in the input parameters, indicating a potential issue within the steam power plant. The abnormalities could range from minor fluctuations to more significant deviations, suggesting a need for further investigation and potential maintenance actions. III. 'Early Maintenance' Requirement The 'early maintenance' output parameter signals that the predictive maintenance model has detected early signs of machinery degradation or performance deterioration. Acting upon this output parameter allows for proactive maintenance measures to address the identified issues, preventing potential breakdowns or further deterioration. IV. 'Annual Maintenance' Requirement The 'annual maintenance' output parameter is indicative of routine or scheduled maintenance that should be performed on an annual basis. This parameter doesn't imply an immediate critical issue but rather a regular upkeep requirement to ensure the long-term health and efficiency of the steam power plant. The categorization of these output parameters is based on a graduated scale of urgency and severity. 'Normal' represents the optimal operating condition, 'abnormal' indicates deviations from the expected state, 'early maintenance' signifies early signs of potential issues, and 'annual maintenance' implies routine upkeep. The rationale behind this categorization lies in establishing a clear and actionable framework for maintenance decisions. It allows for a systematic approach where maintenance actions can be prioritized based on the severity of the identified condition. For instance, 'abnormal' conditions might trigger a more immediate response compared to 'early maintenance,' which is more preemptive in nature. By categorizing the maintenance needs in this manner, the predictive maintenance model can effectively guide maintenance personnel in prioritizing their actions, optimizing resource allocation, and ensuring the overall health and reliability of the steam power plant. In summary, the defined output parameters—'normal', 'abnormal', 'early maintenance', and 'annual maintenance'—provide a structured and intuitive system to classify the operational state of the steam power plant. Their categorization is strategically designed to guide maintenance actions and ensure the continuous and efficient functioning of the power plant. i. Label Encoding Process The label encoding process involves assigning a unique numerical identifier to each category. For instance, in the case of maintenance labels: A. 'Normal' may be encoded as 0, B. 'Abnormal' as 1, C. 'Early Maintenance' as 2, D. 'Annual Maintenance' as 3. This mapping enables the model to understand and process the categorical data as numerical representations, allowing for effective training.
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