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 operating 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.

在本文中，开发并实现了一种半自动计算成本低廉的系统，用于工业过程的监控和故障检测。该系统采用基于回声状态网络（ESN）的软传感器，能够捕捉过程数据中的非线性动态关系，便于实时监控应用。软传感器设置为模拟正常操作条件，以便当过程受其他原因控制时，可能发生故障时，会出现高残留并允许识别故障。此外，系统通过跟踪ESN动态油藏的内部状态来监控模型预测的可靠性，指示是否可以使用模型预测代替实测数据。