A SYNTACTIC PATTERN-RECOGNITION APPROACH FOR PROCESS MONITORING AND FAULT-DIAGNOSIS

Process operators often deal with vast amounts of sensor data that are typically updated every few minutes. From such real-time data, operators extract interesting and important qualitative trends and features that describe the essential aspects of the process behavior. This level of understanding is essential for performing causal reasoning about process behavior. To aid this decision-making process of operators, a syntactic pattern-recognition approach for process monitoring has been developed, The syntactic pattern-recognition approach has two main parts: (i) a set of primitives that form the trend description language to represent basic changes in trends and (ii) a grammar to perform error correction and explanation generation. The syntactic approach to process monitoring provides a capability to describe complex patterns using a small set of simple primitive patterns. In this work, a backpropagation-based neural network was trained to identify the presence of the appropriate primitives in a trend of noisy process data, A process grammar which can utilize both contextual and non-contextual information to perform error correction and explanation generation has also been developed. These are discussed with the aid of a FCCU case study.