Abstract:

IoT and ML have made predictive maintenance in industrial systems more efficient and better monitored.  This study proposes applying Recurrent Neural Networks (RNNs) for real-time anomaly detection in rotating equipment for predictive maintenance.  Conventional predictive maintenance methods requiring pre-defined criteria or considerable domain knowledge to detect abnormalities might be limiting and fault-prone under dynamic and intricate industrial environments. These methods also delay the important failure detection by not being able to keep pace with evolving machine conditions.  Real-time Anomaly Detection for Rotating Machinery in Predictive Maintenance using Recurrent Neural Networks (PM-RNN) identifies time-series data of IoT sensors by utilizing RNNs, particularly LSTM networks, to address these challenges.  Equipment behavior adaptation, real-time anomaly detection, and continuous learning from past data are achievable with this method. Following the analysis of temporal dependencies in machine data, the PM-RNN system provides more reliable and dynamic predictive maintenance solutions than conventional models.  Real-time anomaly detection utilizing the proposed technology enhances industrial equipment defect detection capability and responsiveness.  Ongoing monitoring offers swift response, eluding unexpected faults and downtime.  The output indicates enhanced predictive maintenance, enhancing operational efficiency and cost savings to IoT-driven business.