Adaptive networks for fault diagnosis and process control

The use of adaptive (artificial neural) networks for fault diagnosis and process control is explored. Adaptive networks can be used as fault recognition systems, as adaptive nonlinear process models, and as controllers. Connection strengths representing correlations between inputs (alarms and sensor measurements) and outputs (faults, future sensor measurements or control actions) are learned using the LMS (Widrow-Hoff) rule and the backpropagation algorithm. The resulting system is a pattern recognizer which is able to learn nonlinear and logical relationships as well as linear correlations. Results are presented for two problems: diagnosing failures in a small model chemical plant, and controlling a highly nonlinear bioreactor. For the diagnosis problem, learning in adaptive networks given qualitative (alarm) and quantitative (sensor) data are compared, and the effect of noise is studied. The importance of nonlinear networks is demonstrated using simple problems which require context sensitivity and problems where optimal alarm thresholds are learned. Results using an adaptive model-based controller using two neural networks (one for the model and one for the controller) are presented and extensions to the standard layered feedforward network are suggested which greatly increase the utility of neural networks for process control. Adaptive networks provide pattern recognition facilities which can be used and interpreted in several ways: they perform multiple nonlinear regression on input/output pairs (current sensor readings and alarms are associated with faults, future readings or control actions) that may be both quantitative and qualitative. Although adaptive networks have many similarities with well-established statistical techniques for system identification, they still offer promise of major benefit, primarily in suggesting new equations, architectures and algorithms. Although the adaptive networks can be viewed as a special form of nonlinear regression, the network formalism suggests several powerful nonlinear functional forms to use. Use of highly interconnected nonlinear systems allows unexpected interactions to be captured. Recurrent networks can learn to recognize arbitrary delays. Temporal difference methods can speed learning when feedback is not immediate. Artificial neural networks will not in any way replace control algorithms, but rather are good for learning that which we are ignorant of: alarm thresholds, patterns of disturbances and model-mismatch (including process delays). Adaptive networks can be thought of as a very data-intensive approach to system identification: many parameters are used in a format that allows interactions between all of the variables. Thus more specific patterns can be learned than when the system is described using a relatively simple equation with a small number of parameters. When the exact form of the equation is known or little data is available, an equation with fewer parameters is of course preferable and neural networks should be avoided. We looked at two example problems: fault diagnosis and adaptive model-based control. Both quantitative (sensor) and qualitative (alarm) information can be used in fault diagnosis. Optimal thresholds for triggering alarms are learned; These can be dependent on the context provided by the states of other variables. Nonlinear networks are required for all but the simplest problems. In adaptive model-based control, a highly nonlinear model was learned without using a priori knowledge of the equational forms. This approach is expected to yield the most benefit in MIMO systems which contain complex nonlinear interactions and in systems in which recurring disturbances must be recognized and forecast.