Adverse Condition and Critical Event Prediction Toolbox (ACCEPT)

Many natural or complex engineered systems rely upon critical functions or processes that can be measured with the aid of various sensors or other novel devices. As a result, sensor and measurement data can be used to learn a parametric or non-parametric model of the behavior for a given process or metric. For such processes or metrics, it may be critical to avoid or be forewarned of impending level crossings that may characterize entry into extreme or potentially catastrophic operating regimes. Under certain circumstances, the metric to be monitored may represent the residual, or difference between an actual value and a predicted value generated by an independent regression method, rather than a physical process having a physically interpretable meaning.