System and method for corrosion and erosion monitoring of pipes and vessels

Abstract

This disclosure relates to the field of corrosion and erosion monitoring of pipes and vessels. More specifically, this disclosure relates to a system and method for corrosion and erosion monitoring of pipes and vessels, where the system/method combines ultrasonic thickness monitoring using longitudinal waves with ultrasonic area monitoring using one or more guided waves, whereby representative thickness measurements are complemented by an area monitoring feature to detect localized corrosion/erosion in between representative thickness measurement locations. In another embodiment, a system and method for optimized asset health monitoring that includes an analytics solution is disclosed.

I/We claim: 
     
         1 . A method for down-selecting from among probe assemblies installed on a piping system, wherein the probe assemblies are configured for pipe wall thickness monitoring, the method comprising:
 setting a grouping_sensitivity hyperparameter, a threshold_measurements hyperparameter, and a group_size hyperparameter for a model, before training the model;   grouping, by the model executing on a processor, a first set of the probe assemblies based at least on historical pipe wall thickness measurements collected from the probe assemblies installed on the piping system over a period of time;   assigning a unique groupID to each set of probe assemblies;   selecting, by the model after training the model, an optimization function from among a plurality of optimization functions for the model;   identifying, by the model, a single probe assembly corresponding to each groupID for pipe wall thickness monitoring of the piping system; and   sending, by a thickness monitoring controller associated with the piping system, a pipe wall thickness measurement of the single probe assembly from each groupID for inspection.   
     
     
         2 . The method of  claim 1 , wherein the probe assemblies comprise at least a resistance temperature detector, a thickness monitoring ultrasonic transducer, and an area monitoring ultrasonic transducer configured to detect localized corrosion in the piping system. 
     
     
         3 . The method of  claim 2 , further comprising:
 validating that the pipe wall thickness measurement of the single probe assembly is general corrosion and not localized corrosion by:
 generating a probability plot of all pipe wall thickness measurements associated with the piping system; 
 grouping the plotted pipe wall thickness measurements by nominal thickness; and 
 failing to identify a non-linear relationship in the probability plot of pipe wall thickness measurements grouped by nominal thickness to confirm the general corrosion. 
   
     
     
         4 . The method of  claim 1 , further comprising:
 during the inspection, down-selecting by disregarding all remaining probe assemblies in each groupID except the single probe assembly from each groupID to reduce a number of inspection samples measured without compromising a risk profile of the piping system.   
     
     
         5 . The method of  claim 1 , wherein the grouping of the first set of the probe assemblies is further based at least on inspection information provided to the system and historical pipe wall thickness measurements collected over a period of time from the probe assemblies installed on the piping system. 
     
     
         6 . The method of  claim 1 , wherein the plurality of optimization functions comprises median_TML_within_groupID, minimum_average_TML_within_groupID, and minimum_variation_from_mean. 
     
     
         7 . The method of  claim 6 , wherein the plurality of optimization functions comprises TML_position. 
     
     
         8 . The method of  claim 1 , wherein the piping system comprises a tank, and wherein a first probe assembly of the probe assemblies is configured to measure a wall thickness of the tank. 
     
     
         9 . The method of  claim 1 , wherein the pipe wall thickness monitoring comprises measuring the thickness of pipe wall at a specific probe assembly, wherein the pipe wall is located at one or more of a pipe, tank, vessel, and pipeline. 
     
     
         10 . The method of  claim 9 , wherein the pipe wall thickness monitoring comprises, by the probe assemblies, analyzing the original wall thicknesses, wall thickness loss over time, calibration error, and measurement location repeatability error. 
     
     
         11 . The method of  claim 1 , further comprising:
 storing, in computer memory communicatively coupled to the processor, historical pipe wall thickness measurements collected over an extended period of time from the probe assemblies installed on the piping system; and   training, by the processor, the model with at least the historical pipe wall thickness measurements stored in the computer memory.   
     
     
         12 . The method of  claim 11 , wherein the model comprises an artificial neural network. 
     
     
         13 . A system for detecting localized corrosion to a plurality of components that transport materials across a distance, the system comprising:
 a plurality of probe assemblies affixed to one or more of the components, wherein each of the plurality of probe assemblies corresponds to a unique identifier;   a data store configured to store historical wall thickness measurements collected over a period of time from measurements performed by the probe assemblies;   a model trained on the historical wall thickness measurements in the data store and with hyperparameters comprising at least a grouping_sensitivity hyperparameter; and   a monitoring apparatus comprising a processor and a memory storing computer-executable instructions that, when executed by the processor, cause the system to perform steps comprising:
 grouping, based on the model, a first set of the probe assemblies; 
 assigning a unique groupID to each set of probe assemblies; 
 selecting, based on the model, an optimization function from among a plurality of optimization functions; 
 identifying, based on the model and selected optimization function, a probe assembly for each groupID for wall thickness monitoring of the components, wherein each groupID corresponds to the unique identifier corresponding to the identified probe assembly; and 
 outputting a list of the unique identifiers corresponding to any groupID. 
   
     
     
         14 . The system of  claim 13 , wherein the plurality of probe assemblies comprise at least a thickness monitoring ultrasonic transducer and an area monitoring ultrasonic transducer configured to detect localized corrosion to the components, wherein the probe assembly identified from each groupID comprises more than one probe assembly of the plurality of probe assemblies, and wherein the memory of the monitoring apparatus stores computer-executable instructions that, when executed by the processor, cause the system to perform steps comprising:
 sending, by a thickness monitoring controller associated with the components, a wall thickness measurement of the probe assembly from each groupID for inspection;   during the inspection, disregarding all remaining probe assemblies in each groupID except the more than one probe assembly from each groupID; and   validating that the wall thickness measurements of the more than one probe assembly from each groupID fails to identify general corrosion.   
     
     
         15 . The system of  claim 13 , wherein the wall thickness measurement of the probe assembly from a first groupID comprises a thickness of a wall of a pipe component at the probe assembly. 
     
     
         16 . The system of  claim 13 , wherein the wall thickness measurement of the probe assembly from a first groupID comprises a thickness of a wall of a tank component at the probe assembly. 
     
     
         17 . The system of  claim 13 , wherein the hyperparameters comprise a grouping_sensitivity hyperparameter, a threshold_measurements hyperparameter, and a group_size hyperparameter, and wherein the plurality of optimization functions comprises median_TML_within_groupID, minimum_average_TML_within_groupID, minimum_variation_from_mean, and TML_position. 
     
     
         18 . A non-transitory computer-readable medium storing computer-executable instructions that, when executed by a processor, cause a system to down-select from among probe assemblies installed on a piping system, by performing steps comprising:
 storing, in a computer memory communicatively coupled to the processor, historical pipe wall thickness measurements collected over a period of time from the probe assemblies installed on the piping system;   setting a hyperparameter for a model;   training, by the processor, the model with at least the historical pipe wall thickness measurements stored in the computer memory;   grouping, by the model executing on the processor, a first set of the probe assemblies;   assigning a unique groupID to each set of probe assemblies;   selecting, based on the model, an optimization function from among a plurality of optimization functions;   identifying, based on the model and selected optimization function, a probe assembly corresponding to each groupID for pipe wall thickness monitoring of the piping system; and   sending, by a thickness monitoring controller associated with the piping system, a pipe wall thickness measurement of the probe assembly from each groupID for inspection.   
     
     
         19 . The non-transitory computer-readable medium of  claim 18 , wherein the hyperparameters comprise at least one of a grouping_sensitivity hyperparameter, a threshold_measurements hyperparameter, and a group_size hyperparameter; and wherein the plurality of optimization functions comprises median_TML_within_groupID, minimum_average_TML_within_groupID, minimum_variation_from_mean, and TML_position. 
     
     
         20 . The non-transitory computer-readable medium of  claim 18 , further storing computer-executable instructions that, when executed by the processor, cause the system to perform steps comprising:
 during the inspection, disregarding all remaining probe assemblies in each groupID except the probe assembly identified from each groupID.