Abstract: A method for detecting and classifying an event includes the procedure of acquiring a plurality of data-instances, each corresponding to a respective attributes measurement of selected attributes, each including at least one attribute, each being further associated with a respective time-stamp and defining a data point in an attributes space. For each selected data-instance, the distance in the attributes space is determined between a point ‘TN’ corresponding to the selected data-instance and the Kth preceding data-point ‘Tn?k’. A distance versus time function is determined from the determined distances and time-stamps associated with each selected data-instance and the occurrence of an event is detected according to a distance threshold of the distances in the distance versus time function. The morphology parameters of the distance versus time function are determined when an event is detected; and the event is classified according to the determined morphology parameters of the distance versus time function.

Abstract: An abnormal-occurrence-detection-system comprising an abnormal-occurrence-detector inspecting a plurality of inspected-data-instances, each data-instance including values associated with at least one physical-attribute, the values defining the location of each data-instance in an attribute-space, the abnormal-occurrence-detector detecting when at least one data-instance corresponds to an abnormal-occurrence according to one of the following: when a density-point associated with one of the inspected-data-instances is not associated with at least one hilltop-point, and when the distance in the attribute-space, between a selected one of the inspected-data-instances associated with a first respective unique-identifier in a sorted list of unique-identifiers and a respective Kth adjacent inspected-data-instance associated with a second respective unique-identifier in the sorted list of unique-identifiers, exceeds a distance-threshold-for-Kth-adjacency, the sorted list of unique-identifiers defining a sorted sequenc

Abstract: An apparatus for the analysis of a process having parameter-based faults includes: a parameter value inputter configured for inputting values of at least one process parameter, a fault detector, configured for detecting the occurrence of a fault, a learning file creator associated with the parameter value inputter and the fault detector, configured for separating the input values into a first learning file and a second learning file, the first learning file comprising input values from a collection period preceding each of the detected faults, and the second learning file comprising input values input outside the collection periods, and a learning file analyzer associated with the learning file creator, configured for performing a separate statistical analysis of the first and second learning files, thereby to assess a process status.

Abstract: An apparatus for the analysis of a process having parameter-based faults includes: a parameter value inputter configured for inputting values of at least one process parameter, a fault detector, configured for detecting the occurrence of a fault, a learning file creator associated with the parameter value inputter and the fault detector, configured for separating the input values into a first learning file and a second learning file, the first learning file comprising input values from a collection period preceding each of the detected faults, and the second learning file comprising input values input outside the collection periods, and a learning file analyzer associated with the learning file creator, configured for performing a separate statistical analysis of the first and second learning files, thereby to assess a process status.

Abstract: An apparatus for the analysis of a process having parameter-based faults includes: a parameter value inputer configured for inputting values of at least one process parameter, a fault detector, configured for detecting the occurrence of a fault, a learning file creator associated with the parameter value inputer and the fault detector, configured for separating the input values into a first learning file and a second learning file, the first learning file comprising input values from a collection period preceding each of the detected faults, and the second learning file comprising input values input outside the collection periods, and a learning file analyzer associated with the learning file creator, configured for performing a separate statistical analysis of the first and second learning files, thereby to asses a process status.

Abstract: An apparatus for the analysis of a process having parameter-based faults includes: a parameter value inputter configured for inputting values of at least one process parameter, a fault detector, configured for detecting the occurrence of a fault, a learning file creator associated with the parameter value inputter and the fault detector, configured for separating the input values into a first learning file and a second learning file, the first learning file comprising input values from a collection period preceding each of the detected faults, and the second learning file comprising input values input outside the collection periods, and a learning file analyzer associated with the learning file creator, configured for performing a separate statistical analysis of the first and second learning files, thereby to asses a process status.

Abstract: A method for controlling at least one characteristic of a product of an industrial batch process. The method comprising the steps of creating a hierarchical knowledge tree describing the process. Following the creation of a knowledge tree a learning process occurs. This leads to the creation of a global model. During the execution of a batch process, the global model is applied to dynamically target subsequent phase parameters based on already executed phases.

Abstract: A method for controlling at least one characteristic of a product of an industrial batch process. The method comprising the steps of creating a hierarchical knowledge tree describing the process. Following the creation of a knowledge tree a learning process occurs. This leads to the creation of a global model. During the execution of a batch process, the global model is applied to dynamically target subsequent phase parameters based on already executed phases.