System and method for automated acquisition and analysis of electromagnetic testing data

A system for identifying indications in an object via non-destructive testing, the system comprising:

• a probe;
• a recording device in operative communication with the probe, the recording device comprising a storage module configured to record and store test data from the probe while the probe is operated to scan the object, the test data comprising a plurality of data points, each data point corresponding to a signal measurement acquired by the probe;
• and
• an analysis device configured to access the test data stored by the recording device, the analysis device comprising a test data analysis module configured to:
• process the test data using a first analysis machine learning algorithm trained to identify landmarks in the test data and output grouped test data identifying ranges in the test data corresponding to the identified landmarks;
• model the grouped test data as a chain of identified landmarks, and identify patterns within the chain of identified landmarks corresponding to an expected sequence of landmarks;
• extract an isolated landmark sequence comprising landmarks that match the patterns;
• process the isolated landmark sequence to identify regions in the object based on the landmarks;
• process the test data and the identified regions using a second analysis machine learning algorithm trained to detect indications in the test data and output a list of detected indications;
• process the list of detected indications to automatically classify each of the indications according to one of a plurality of predefined indication types;
• and
• output each of the classified indications in a report specifying positions of each of the classified indications in the object.

A non-transitory computer-readable medium having instructions stored thereon to identify indications in an object via non-destructive testing using inspection equipment comprising a probe, the instructions, when executed by one or more processors, cause the one or more processors to:

• record test data from the probe while the probe is operated to scan the object, the test data comprising a plurality of data points, each data point corresponding to a signal measurement acquired by the probe;
• process the test data using a first analysis machine learning algorithm trained to identify landmarks in the test data and output grouped test data identifying ranges in the test data corresponding to the identified landmarks;
• model the grouped test data as a chain of identified landmarks, and identifying patterns within the chain of identified landmarks corresponding to an expected sequence of landmarks;
• extract an isolated landmark sequence comprising landmarks that match the patterns;
• process the isolated landmark sequence to identify regions in the object based on the landmarks;
• process the test data and the identified regions using a second analysis machine learning algorithm trained to detect indications in the test data and output a list of detected indications;
• process the list of detected indications to automatically classify each of the indications according to one of a plurality of predefined indication types;
• and
• output each of the classified indications in a report specifying positions of each of the classified indications in the object.