Menneske, teknologi og organisasjon


The ALADDIN project aims at the study and development of flexible, accurate, and reliable event identification and fault diagnosis techniques for machinery and processes. The final goal is the development of a software module that can be easily integrated/interfaced with other existing Computerized Operator Support Systems (COSS).

Hoffmann, Mario



The ALADDIN event classification system developed at IFE, is based on advanced numerical techniques, combining Ensembles of Recurrent Neural Network Classifiers with Wavelet Preprocessing and Autonomous Recursive Task Decomposition. This combination has demonstrated superior performance on tasks such as the Classification of Anomalous Islanding Events in PWR Nuclear Power Plants, and the Early Detection & Diagnosis of High-Pressure Preheater Sub-System Faults in BWR Nuclear Power Plants. See Other information in the menu above.

Many industrial processes are characterized by long periods of steady-state operation, intercalated by occasional shorter periods of a more dynamic nature, in correspondence of either normal events, such as minor disturbances, planned interruptions or transitions to different operation states, or abnormal events, such as major disturbances, actuator failures, instrumentation failures, etc. This second class of events represents a challenge, and possibly a threat, to the smooth, safe, and economical operation of the monitored process. The prompt detection and recognition of such an event is of the essence for the performance of the most effective and informed response to the challenge.

The most common way of performing event detection and recognition in an industrial plant is to rely on experienced operators, which, by observing the current values of important process variables, as well as their recent history on trend displays, plus eventual alarms generated by the process monitoring system, can usually quickly and reliably diagnose the current event and perform the adequate correcting actions through the plant control system. However, 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 track and recognize the current situation. Also, when the changes to the process variables caused by the occurring event are subtle or very slow, and do not cause the alarm system to generate alert signals, the abnormal situation can be easily overlooked by an operator. In both cases, a COSS like ALADDIN, able to detect and classify these process changes, would be of great value.