Patent ID: 11933695
Assignee: AKTIEBOLAGET SKF
Field: Measurement (Instruments)
Classification: CPC G | IPC G

Claim 8:
9. A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to perform a process, the process comprising:
monitoring sensory input data of a plurality of industrial machines of a same type that are located within a predetermined proximity of each other, wherein monitoring the sensory input data further comprises tracking and aggregating a plurality of parameters associated with the sensory input data with respect to respective components of a plurality of components of the plurality of industrial machines, wherein the sensory input data is collected via a plurality of sensors each located in a predetermined proximity to one of the plurality of industrial machines, wherein the sensory input data is indicative of an operation of the plurality of components of the plurality of industrial machines;
analyzing the plurality of parameters associated with the sensory input data using a plurality of machine learning techniques, wherein different parameters of the plurality of parameters are analyzed using different machine learning techniques of the plurality of machine learning techniques, wherein the plurality of machine learning techniques includes applying at least one of: a neural network, a recurrent neural network, decision tree learning, a Bayesian network, and clustering;
computing, based on a suspected anomalous level value of each of a plurality of suspected anomalies of the sensory input data of the plurality of industrial machines, an average anomalous amount that is associated with a time interval for each of the plurality of suspected anomalies, wherein each suspected anomaly is a portion of the sensory input data;
generating a plurality of meta-models, each meta-model being generated for one of the plurality of components, wherein the meta-model generated for each component optimally indicates anomalies in the sensory input data for the component;
determining that at least one of the plurality of suspected anomalies is an anomaly, wherein a suspected anomaly is determined to be an anomaly when a result of a subtraction of the computed average anomalous amount from the suspected anomalous level value for the at least one of the plurality of suspected anomalies exceeds a predetermined threshold, wherein each of the plurality of meta-models is utilized to detect at least a portion of the determined at least one anomaly in behavior of a respective portion of one of the plurality of industrial machines; and
determining at least one predicted machine failure based on the at least one of the plurality of suspected anomalies determined to be an anomaly and the sensory input data of the plurality of industrial machines of the same type that are located within a predetermined proximity.