This is the second report on the use of on-line monitoring (OLM) in nuclear power plants (NPPs). The first report, focused on the application of OLM to verify the static (calibration) and dynamic (response time) performance of process instruments in NPPs. This second report extends the application of OLM to equipment and process condition monitoring, encompassing an array of technologies including vibration monitoring, acoustic monitoring, loose parts monitoring, motor current signature analysis and noise diagnostics, as well as vibration analysis of the reactor core and the primary circuit. Furthermore, this report includes the application of modelling technologies for equipment and process condition monitoring. A majority of these technologies depend on existing data from existing sensors and first principles models to estimate equipment and process behaviour using empirical and physical modelling techniques. In doing so, pattern recognition tools such as neural networks, fuzzy classification of data, multivariate state estimation and other means are used. These means are described in the report, and examples of their application and implementation are provided.
