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Compared to current AI or robotic systems, humans navigate their environmentwith ease, making tasks such as data collection trivial. However, humans findit harder to model complex relationships hidden in the data. AI systems,especially deep learning (DL) algorithms, impressively capture those complexrelationships. Symbiotically coupling humans and computational machines'strengths can simultaneously minimize the collected data required and buildcomplex input-to-output mapping models. This paper enables this coupling bypresenting a novel human-machine interaction framework to perform faultdiagnostics with minimal data. Collecting data for diagnosing faults forcomplex systems is difficult and time-consuming. Minimizing the required datawill increase the practicability of data-driven models in diagnosing faults.The framework provides instructions to a human user to collect data thatmitigates the difference between the data used to train and test the faultdiagnostics model. The framework is composed of three components: (1) areinforcement learning algorithm for data collection to develop a trainingdataset, (2) a deep learning algorithm for diagnosing faults, and (3) ahandheld augmented reality application for data collection for testing data.The proposed framework has provided above 100\% precision and recall on a noveldataset with only one instance of each fault condition. Additionally, ausability study was conducted to gauge the user experience of the handheldaugmented reality application, and all users were able to follow the providedsteps.

Deep Learning and Handheld Augmented Reality Based System for Optimal Data Collection in Fault Diagnostics Domain