Echo State Network Based Soft Sensor For Monitoring And Fault Detection Of Industrial Processes

In this paper a semi-automatic computationally inexpensive system is developed and implemented for monitoring and fault detection of industrial processes. The system uses a soft sensor based on Echo State Networks (ESN) and is able to capture the non-linear dynamic relationships in the process data, making it convenient for real-time monitoring applications. The soft sensor is set to simulate normal operat-ing conditions, so that when the process is governed by other causes, possibly in failure, high residues occur and allow the failure identification. In addition, the system monitors the reliability of the model predictions by tracking the internal states of the ESN dynamic reservoir, indicating whether the model predictions can be used instead of the measured data. The system is successfully applied to the Mackey -Glass Anomaly Benchmark (MGAB) and to the monitoring of critical pieces of equipment of a real oil and gas plant. (c) 2021 Elsevier Ltd. All rights reserved.