Abstract
In view of the increasing amount of information in the form of alarms, messages or also acoustic signals, the operators of systems are exposed to more workload and stress than ever before.We develop a concept for the reduction of alarm floods in industrial plants, in order to prevent the operators from being overwhelmed by this flood of information. The concept is based on two phases. On the one hand, a learning phase in which a causal model is learned and on the other hand an operating phase in which, with the help of the causal model, the root cause of the alarm sequence is diagnosed. For the causal model, a Bayesian network is used which maps the interrelations between the alarms. Based on this causal model the root cause of an alarm flood can be determined using inference. This not only helps the operator at work, but also increases the safety and speed of the repair. Additionally it saves money and reduces outage time. We implement, describe and evaluate the approach using a demonstrator of a manufacturing plant in the SmartFactoryOWL.
Highlights
The industrial revolution affects society and the working world
We develop a concept for the reduction of alarm floods in industrial plants, in order to prevent the operators from being overwhelmed by this flood of information
We presented the increasing problem of overwhelming alarm floods in industrial plants
Summary
The industrial revolution (digital transformation) affects society and the working world. In a perfect scenario, where they would have found the crucial alarm immediately, the operators would have had 25 minutes to shut down the plant or at least minimize the possible damage caused by an explosion This was impossible because of the flood of alarms so the operators could not handle the situation in an appropriate way. The increasing focus of organizations and industry on the topic of alarm management shows the importance of reducing the amount of alarms in the future In this challenge, the alarm flood reduction is one of the main tasks. The company can save a lot of money due to increased production and improved quality because the operator is able to focus better on the failures This will reduce the time to correct the failures and prevent unnecessary shut downs of parts of the plant. In the conclusion we give an outlook for further research which needs to be done to utilize the concept in real industrial plants
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