The operation of any industrial plant is based on the readings of a set of sensors. The ability to identify the state of operation, or the events that are occurring, from the time evolution of these readings is essential for the satisfactory execution of the appropriate control actions. In supervisory control, detection and diagnosis of faults, adaptive control, process quality control, and recovery from operational deviations, determining the correct mapping from process trends to operational conditions is the pivotal task. Reasoning in time, however, is very demanding, because time introduces a new dimension with significant levels of additional freedom and complexity. The real-time history of scores of variables can be displayed and monitored in most computerized process monitoring and control systems. However, whereas a simple visual inspection of displayed trends is sufficient to allow the operator confirm the process status during normal, steady-state operations, when the process is in significant transience or crises have occurred, the displayed trends of interacting variables and alarms can easily overwhelm an operator. When process variables change with different rates, or are affected by varying lags, it is very difficult for a human operator to carry out routine tasks, such as distinguishing normal from abnormal conditions, identify the causes of process trends, evaluate current process trends and anticipate future states, etc. In this paper we describe how a combined use of wavelets and recurrent neural networks improves on our previously proposed solutions to the transient classification problem. In particular, the newly developed system overcomes two basic limitations of the earlier systems, namely the requirement for fixed length transients, and the requirement for a trigger signal indicating the start of a transient, i.e. the need for a separate transient detection component. The paper also includes an experimental analysis of the discrimination power of the proposed system, which provides a strong case for its application potential to a great variety of industrial processes.
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