Case-Based Experience Reuse and Agents for Efficient Health Monitoring, Preventive and Corrective Actions

Experienced staff acquires their experience during many years of practice, and sometimes also trough expensive mistakes. This knowledge is often lost when technicians retire, or if companies need to downsize during periods of reduced sale. When scaling up production, new staff requires training and may repeat similar mistakes. Another issue that may be costly is when monitoring systems repeatedly give false alarms, causing expensive loss off production capacity and resulting in technicians losing trust in the systems and in worst case, switch them off. If monitoring systems would learn from previous experience, both correct alarms and false alarms, the reliability and trust in the monitoring system would increase. Also connecting alarms to either equipment taking automatic actions or recommend actions based on the current situations and previous experience would be valuable. An engineer repeating the same task a second time is often able to perform the task in 1/3 of the time it took the first time. Most corrective or preventive actions have been carried out before, and if not by the same engineer, by some other engineer in the same company, or working somewhere else in the world at the same or similar production equipment. The potential in time saving and reduction of mistakes is very large if efficient experience transfer can be accomplished. But building large complex support systems is not always the ideal way. We propose instead localized intelligent agents, able to either autonomously perform the necessary actions or aid a human in the decision making process by providing the necessary information needed to make an informed and validated decision.