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

本文介绍一种新型的人机交互框架，利用强化学习算法、深度学习算法和手持增强现实应用收集最小化的数据来实现故障诊断。针对复杂系统的故障诊断，该框架在新颖数据集上提供了高于100%的精度和召回率，并进行了可用性研究以初步验证用户体验。

基于深度学习和手机增强现实的故障诊断领域最优数据收集系统