We present an integrated system that automatically collects historical and current data from heterogeneous sources, performs analytics to identify telltale signatures of Loss of Containment (LOC) events, and makes asset behavior predictions as an asset ages. Our semantically enhanced system of record stores and manages heterogeneous data introduced by a variety of collection mechanisms and frequencies, and also captures subject matter knowledge and experience to automate LOC prevention decision making. Analytics provide a "learning by example" platform; LOC patterns can be quickly identified even in new facilities by utilizing other facilities LOC facts. Machine learning techniques coupled with rule-based systems and advanced visualization environments can not only determine an asset’s health, but also minimize labor intensive and error prone data comprehension and decision-making.

You can access this article if you purchase or spend a download.