Incipient fault diagnosis of chemical processes via artificial neural networks

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Abstract

Artificial neural networks have capacity to learn and store information about process faults via associative memory, and thus have an associative diagnostic ability with respect to faults that occur in a process. Knowledge of the faults to be learned by the network evolves from sets of data, namely values of steady-state process variables collected under normal operating condition and those collected under faulty conditions, together with information about the degree of the faults and their causes.

Here, we describe how to apply artificial neural networks to fault diagnosis. A suitable two-stage multilayer neural network is proposed as the network to be used for diagnosis. The first stage of the network discriminates between the causes of faults when fed the noisy process measurements. Once the fault is identified, the second stage of the network estimates the degree of the fault. Thus, the diagnosis of incipient faults becomes possible.

Ancillary