Determining hardware issues and recommending corresponding actions using artificial intelligence techniques

A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device:

• to obtain input image data pertaining to at least one hardware item;
• to identify one or more portions of the at least one hardware item by processing at least a portion of the input image data using at least one convolutional autoencoder model, wherein processing the at least a portion of the input image data using the at least one convolutional autoencoder model comprises:
• generating at least one compressed data representation of the at least a portion of the input image data;
• and
• identifying the one or more portions of the at least one hardware item by processing the at least one compressed data representation of the at least a portion of the input image data using one or more of at least one clustering algorithm and at least one supervised learning algorithm;
• to detect, in the one or more identified portions, at least one defect of the at least one hardware item by processing the at least a portion of the input image data using one or more computer vision techniques;
• to generate at least one recommendation associated with the at least one hardware item, the at least one recommendation pertaining to resolving the at least one detected defect using at least one system external to the at least one hardware item;
• and
• to perform, in connection with using the at least one system external to the at least one hardware item, one or more automated actions based at least in part on the at least one recommendation.

An apparatus comprising:

• at least one processing device comprising a processor coupled to a memory;
• the at least one processing device being configured:
• to obtain input image data pertaining to at least one hardware item;
• to identify one or more portions of the at least one hardware item by processing at least a portion of the input image data using at least one convolutional autoencoder model, wherein processing the at least a portion of the input image data using the at least one convolutional autoencoder model comprises:
• generating at least one compressed data representation of the at least a portion of the input image data;
• and
• identifying the one or more portions of the at least one hardware item by processing the at least one compressed data representation of the at least a portion of the input image data using one or more of at least one clustering algorithm and at least one supervised learning algorithm;
• to detect, in the one or more identified portions, at least one defect of the at least one hardware item by processing the at least a portion of the input image data using one or more computer vision techniques;
• to generate at least one recommendation associated with the at least one hardware item, the at least one recommendation pertaining to resolving the at least one detected defect using at least one system external to the at least one hardware item;
• and
• to perform, in connection with using the at least one system external to the at least one hardware item, one or more automated actions based at least in part on the at least one recommendation.