Sequential alarms, which are sets of alarms occurring sequentially within a short period of time after an initial alarm warning of an abnormality, reduce the ability of plant operators to cope with operation abnormalities because critical alarms are often buried under numerous correlated alarms. We have improved a previously proposed dot matrix method for identifying sequential alarms hidden in noisy plant-operation data by adding the use of a sliding window. The alarm sequence in the plant-operation data is converted into a set of windows containing adjacent alarms. All combinations of windows are compared, and repeated windows are identified on the basis of a minimal number of alarm matches. The alarms in each repeated window comprise a sequential alarm. Application of this method to simulated operation data for an azeotropic distillation column demonstrated that it can identify sequential alarms in noisy plant-operation data. Classifying such alarms into small numbers of subsequences effectively reduces the number of sequential alarms, enabling engineers to reduce unnecessary alarms related to plant operations.