Effective approaches to remote asset monitoring with acoustic emission


This paper presents the effectiveness of remote asset monitoring using acoustic emission. We will discuss different aspects of implementing an effective remote asset monitoring operation, such as considerations for the deployment of instrumentation in the field, instrumentation, communications, benefits of automatic data transfer from the monitoring system to a secure server, continuous automatic verification of hardware integrity, and automatic notification of potential damage or conditions affecting the asset integrity occurring. We will provide an in-depth view of the cloud monitoring implementation processes, and the use of machine learning and other data analytics techniques to identify abnormal acoustic emission data patterns. The application of these tools to specific case studies in electric power transformers, power steam generators and civil infrastructure assets will be presented. Finally, we will illustrate how these capabilities help to provide timely, actionable information to the asset owner.
