Lifelong condition monitoring based on NB-IoT for anomaly detection of machinery equipment

The condition monitoring for machinery equipment is vital for safe and economical production in the industry. In modern manufacturing, data-driven methods for machine prognosis and health management (PHM) have been paid greater importance due to the development of machine learning. For another, the emerging Internet of Things (IoT) technique makes it possible for large scale data collection using distributed IoT terminal devices. In this paper, a condition monitoring system for machinery equipment is designed based on Narrow Band Internet of Things (NB-IoT) technique. Combined with the wavelet packet decomposition (WPD) and one-class support vector machine (OCSVM) algorithm, the abnormal data can be effectively identified. The system is verified by a small fan working at two conditions: normal and blade imbalance. The experiment results prove that the system can achieve reliable and stable online monitoring. What's more, the low power design of the IoT terminal ensures the system's longtime operation.