Time series Forecasting to detect anomalous behaviours in Multiphase Flow Meters
arXiv:2301.00014v1h-index: 43
Originality Synthesis-oriented
AI Analysis
This is an incremental application of existing methods to a domain-specific problem for industrial monitoring.
The paper tackled the problem of detecting anomalies in Multiphase Flow Meters by developing an anomaly detection system using machine learning for time series forecasting, which predicts sensor behavior to identify anomalies.
An Anomaly Detection (AD) System for Self-diagnosis has been developed for Multiphase Flow Meter (MPFM). The system relies on machine learning algorithms for time series forecasting, historical data have been used to train a model and to predict the behavior of a sensor and, thus, to detect anomalies.