Predictive Maintenance for Remote Field IoT Devices—A Deep Learning and Cloud-Based Approach

Abstract

Predictive maintenance is the process of monitoring equipment continuously during its operation to monitor its performance to report its faults beforehand. Using machine learning and analytics, predicting the machine’s failure before it occurs is possible. Various anomaly detection algorithms and predictive learning algorithms can be used to check whether the machine performs normally during its operation. Using IoT, predictive maintenance can be performed remotely which saves costs and time for the company. This predictive maintenance project is aimed at oil rod pumps which are used to extract oil from the ground. The rod pump is machinery used to suck up the oil from the ground level. These machines are monitored by the sensors which are used to keep them in check. The data coming from the machines are called telemetry data. The telemetry data from these machines are collected. The collected data can be processed and used for prediction. The prediction can be used to prevent the failure of the machine beforehand. This can be used to reduce the sudden downtime caused by the machine. The data is collected from the IoT sensors and stored in the cloud storage for processing. Using deep learning, the data can be used to detect anomalies which cause the machine to fail in its operations. The components of the pump will be monitored and data coming out of them can be used to check their health. This keeps a continuous tab on the health of the machines. These companies will be able to remotely monitor and control the oil rod pumps and alert their repair teams only when needed. This work produces predictive systems that can detect anomalies in IoT machinery and alert the repair team automatically once set up.