Robot system for asset health management

Abstract

A processing system having at least one processor operatively coupled to at least one memory. The processor receives sensor data from the at least one sensor indicating one or more characteristics of the asset. The processor generates, updates, or maintains a digital representation that models the one or more characteristics of the asset. The processor detects a defect of the asset based at least in part on the one or more characteristics. The processor generate an output signal encoding or conveying instructions to provide a recommendation to an operator, to control the at least one robot to address the defect on the asset, or both, based on the defect and the digital representation of the asset.

What is claimed is: 
     
         1 . A robotic system for monitoring health of an asset, comprising:
 at least one robot comprising at least one sensor capable of detecting one or more characteristics of an asset and at least one effector capable of performing a repair or maintenance operation on the asset, wherein the at least one robot is configured to inspect an asset with the at least one sensor; and   a processing system having at least one processor operatively coupled to at least one memory, wherein the processor is configured to:
 receive sensor data from the at least one sensor indicating one or more characteristics of the asset; 
 generate, update, or maintain a digital representation that models the one or more characteristics of the asset; 
 detect a defect of the asset based at least in part on the one or more characteristics; and 
 generate an output signal encoding or conveying instructions to provide a recommendation to an operator, to control the at least one robot to address the defect on the asset, or both, based on the defect and the digital representation of the asset. 
   
     
     
         2 . The robotic system of  claim 1 , wherein the digital representation comprises a three dimensional (3D) model of the asset, wherein the 3D model is generated based on at least image data acquired via the at least one robot using the at least one sensor. 
     
     
         3 . The robotic system of  claim 1 , wherein the processor is configured to identify the defect based on a difference between the digital representation and reference data of the asset. 
     
     
         4 . The robotic system of  claim 1 , wherein the processor is configured to generate the output signal encoding or conveying instructions to control the at least one robot to address the defect of the asset by repairing the defect of the asset, replacing part of the asset from an available inventory of parts, spraying part of the asset with a preventative or treatment composition, lubricating part of the asset, welding part of the asset, printing 3D printable parts to apply to the asset, or any combination thereof. 
     
     
         5 . The robotic system of  claim 1 , wherein the processor is configured to determine a severity of the defect based on the digital representation of the asset. 
     
     
         6 . The robotic system of  claim 1 , wherein the processor is configured to determine a risk factor associated with the asset due to the defect. 
     
     
         7 . The robotic system of  claim 6 , wherein the risk factor is based on a severity of the defect, a location of the defect, a likelihood of poor performance due to the defect, or any combination thereof. 
     
     
         8 . The robotic system of  claim 1 , wherein the defect comprises a crack, a region of corrosion, or missing part, of the asset. 
     
     
         9 . A non-transitory, computer readable medium comprising instructions configured to be executed by a processor of a robotic system comprising at least one robot, wherein the instructions comprise instructions which, when executed, cause the processor to:
 receive sensor data from at least one sensor of the at least one robot indicating one or more characteristics of the asset;   generate, update, or maintain a digital representation that models the one or more characteristics of the asset;   detect a defect of the asset based at least in part on the one or more characteristics; and   generate an output signal encoding or conveying instructions to provide a recommendation to an operator, to control the at least one robot to address the defect on the asset, or both, based on the defect and the digital representation of the asset.   
     
     
         10 . The non-transitory computer readable medium of  claim 9 , comprising instructions which, when executed, cause the processor to identify the defect based on a difference between the digital representation and reference data of the asset. 
     
     
         11 . The non-transitory computer readable medium of  claim 9 , comprising instructions which, when executed, cause the processor to determine a risk factor associated with the asset due to the defect. 
     
     
         12 . The non-transitory computer readable medium of  claim 11 , wherein the risk factor is based on a severity of the defect, a location of the defect, a likelihood of poor performance due to the defect, or any combination thereof. 
     
     
         13 . The non-transitory computer readable medium of  claim 9 , comprising instructions which, when executed, cause the processor to adjust a plan for monitoring the asset by adjusting or adding one or more tasks of the plan based on one or both of the quality of the acquired data or a potential defect of the asset, or wherein the adjusted plan causes the at least one robot to acquire additional data related to the asset when executed. 
     
     
         14 . The non-transitory computer readable medium of  claim 9 , comprising instructions which, when executed, cause the processor to detect the defect by comparing the one or more characteristics with prior knowledge of the asset, by analysis of the digital representation against known parameters or patterns, or both. 
     
     
         15 . The non-transitory computer readable medium of  claim 9 , comprising instructions which, when executed, cause the processor to address the defect of the asset by repairing the defect of the asset, replacing part of the asset from an available inventory of parts, spraying part of the asset with a preventative or treatment composition, lubricating part of the asset, welding part of the asset, printing 3D printable parts to apply to the asset, or any combination thereof. 
     
     
         16 . A method, comprising:
 receiving sensor data from at least one sensor of at least one robot indicating one or more characteristics of an asset;   generating, updating, or maintaining a digital representation that models the one or more characteristics of the asset;   detecting a defect of the asset based at least in part on the one or more characteristics; and   generating an output signal encoding or conveying instructions to provide a recommendation to an operator, to control the at least one robot to address the defect on the asset, or both, based on the defect and the digital representation of the asset.   
     
     
         17 . The method of  claim 16 , comprising determining risk associated with the defect of the asset based on severity of the defect, location of the defect, likelihood of poor performance due to the defect, or any combination thereof. 
     
     
         18 . The method of  claim 17 , comprising determining whether improved performance from repairing the defect of the asset outweighs a cost associated with repairing the defect. 
     
     
         19 . The method of  claim 16 , comprising sending a control signal to the at least one robot to inspect an area based on previously detected anomalies and a risk associated with the previously detected anomalies. 
     
     
         20 . The method of  claim 16 , comprising sending the control signal to send the at least one robot to inspect part of an asset prone to cracking based on previously detected cracking.