  In this paper, a new model-free anomaly detection framework is proposed for
time-series induced by industrial dynamical systems. The framework lies in the
category of conventional approaches which enable appealing features such as, a
fast learning with reduced amount of learning data, a reduced memory, a high
potential for explainability as well as easiness of incremental learning
mechanism to incorporate operator feedback after an alarm is raised an
analyzed. All these are crucial features towards acceptance of data-driven
solution by industry but they are rarely considered in the comparisons between
competing methods which generally exclusively focus on performance metrics.
Moreover, the features engineering step involved in the proposed framework is
inspired by the time-series being implicitly governed by physical laws as it is
generally the case in industrial time-series. Two examples are given to assess
the efficiency of the proposed approach.
