Saima-786
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6 months ago Predictive Maintaince (PdM) with Machine Learn- ing (ML) techniques is becoming increasingly vital for industrial equipment upkeep. The collection of comprehensive data from sensors and devices installed on machinery, ensuring both quality and quantity of data. Relevant features are then extracted from this data to build predictive models. These models utilize anomaly detection algorithms to identify irregular patterns indicative of potential equipment failures. Subsequently, time series fore- casting techniques are employed to predict when failures are likely to occur, allowing for proactive maintenance interventions. The development and validation of these ML models involve rigorous training and testing procedures, ensuring their accuracy and reliability in real-world scenarios. Integration with existing maintenance systems facilitates seamless scheduling of proactive maintenance tasks based on predicted failure probabilities. Con- tinuous improvement is key, as models are regularly updated with new data to adapt to changing equipment conditions and optimize predictive accuracy over time. Application of Long Short-Term Memory (LSTM) networks in predictive maintenance strategies for identifying and forecasting gearbox and machinery faults in industrial settings. Leveraging LSTM, a type of deep learning architecture capable of capturing long-term dependencies in sequential data, the study aims to proactively mitigate downtime and associated losses. Through analysis of gearbox and rotatory machinery datasets, LSTM models are trained to predict faults, achieving high performance metrics. Specifically, in the gearbox fault dataset, LSTM models exhibit robust fault prediction capa- bilities, with F1-scores and AUC scores reaching approximately 0.98, accompanied by a minimal error rate of 7 percent
Predictive-Maintenance-using-LSTM-master.zip