Description:The present invention pertains to the technical field of industrial maintenance systems, with a specific focus on predictive maintenance in manufacturing environments. The invention integrates machine learning algorithms and big data analytics to enhance the reliability and efficiency of machinery in manufacturing plants. By analyzing vast amounts of operational data, this invention aims to predict equipment failures before they occur, thereby reducing downtime, optimizing maintenance schedules, and improving overall production efficiency. , Claims:Claims:
Claim 1: A method for predictive maintenance in manufacturing using a machine learning system, comprising:
• A receiving step for collecting operational data, including sensor readings, machine logs, and historical maintenance records.
• A preprocessing step to prepare the collected data by cleaning, normalizing, and transforming it for input into the machine learning model.
• A predictive analysis step using a machine learning model to predict equipment failures based on the preprocessed data.
• A decision support step to provide maintenance recommendations based on the predicted outcomes.
Claim 2: The method according to claim 1, further comprising:
• A real-time monitoring step to continuously analyze incoming data streams and update predictions dynamically.
Claim 3: The method according to claim 1, wherein the predictive analysis step employs an ensemble of machine learning models to enhance predictive accuracy.
Claim 4: The method according to claim 1, wherein the decision support step includes generating maintenance schedules optimized based on predicted failure times.
Claim 5: A computer-readable storage medium comprising computer-executable instructions for performing the method of predictive maintenance as claimed in claim 1, when executed on a computer system.
Claim 6: A computer-implemented predictive maintenance system, comprising:
• A data input module configured to receive operational data from manufacturing equipment.
• A machine learning module comprising a machine learning model configured to preprocess and analyze the received data to predict equipment failures.
• A decision support module configured to provide maintenance recommendations based on the predictive analysis.
Claim 7: The system according to claim 6, further comprising:
• A real-time monitoring module configured to provide continuous updates and alerts based on dynamic data analysis.
Claim 8: The system according to claim 6, wherein the machine learning module is trained on a labeled dataset of operational data, optimizing model parameters through gradient descent to improve predictive accuracy.
Claim 9: The system according to claim 6, wherein the data input module further includes a component for integrating metadata associated with the operational data into the analysis process.