Honeywell International Inc.

Fair Isaac Corporation

William Groves

Wendy K Foslien

Randy Marvin Miller

Anna Olecka

Hao He

Constantine A Vitt

Francois Leclerc

Shane De Zilwa

Ramya Raghunathan

Chiung-Chi Wang

A method includes processing data associated with operation of equipment in an industrial process to repeatedly (i) identify one or more models that mathematically represent the operation of the equipment using training data and (ii) generate first indicators potentially identifying at least one specified condition of the equipment using evaluation data and the one or more models. The method also includes classifying the first indicators into multiple classes. The multiple classes include true positive indicators and false positive indicators. The true positive indicators identify that the equipment is suffering from the at least one specified condition. The false positive indicators identify that the equipment is not suffering from the at least one specified condition. The method further includes generating a notification indicating that the equipment is suffering from the at least one specified condition in response to one or more first indicators being classified into the class of true positive indicators.

Apparatus and method for two-stage detection of furnace flooding or other conditions

A method includes obtaining data associated with operation of equipment in an industrial process and identifying training data and evaluation data in the obtained data. The method also includes, during each of multiple training periods, identifying one or more models and one or more first statistical values using at least some of the training data and determining a threshold value using the one or more first statistical values. The one or more models represent the operation of the equipment. The method further includes, during each of multiple evaluation periods, determining one or more second statistical values using at least some of the evaluation data and the one or more models, comparing the one or more second statistical values to the threshold value determined in a preceding one of the training periods, and determining whether the equipment is suffering from at least one specified condition based on the comparison. In addition, the method includes, in response to determining that the equipment is suffering from the at least one specified condition, generating an alert identifying the at least one specified condition.

Apparatus and method for using model training and adaptation to detect furnace flooding or other conditions

Data characterizing a plurality of sensor generated events is received. Thereafter, analysis of the plurality of events is initiated using a decision tree with splits performed on decision keys. A first portion of the decision keys comprising analyst-selected splits can be derived from user-generated domain knowledge regarding a first plurality of historical events. A second portion of the decision keys comprising software-driven splits can be derived from a predictive model trained using a second plurality of historical events. Later, a disposition is determined for each event based on a traversal of at least one of the decision keys in the decision tree and such disposition is later initiated. Related apparatus, systems, techniques and articles are also described.

Determing a disposition of sensor-based events using decision trees with splits performed on decision keys

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Bear, DE, United States of America

William Groves