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.

翻译：与目前的人工智能或机器人系统相比,人类可以轻松地在环境中游览环境,使数据收集等任务变得微不足道。然而,人类发现难以模拟数据隐藏的复杂关系。人工智能系统,特别是深学习(DL)算法,能够令人印象深刻地捕捉这些复杂关系。同时将人类和计算机的优势结合在一起,可以同时尽量减少所收集的数据,并建立复杂的输入到产出的绘图模型。本文通过提供新的人体机器互动框架来进行故障诊断,同时提供最低限度的数据。收集复杂系统诊断错误的数据十分困难而且耗时。尽量减少所需数据将提高数据驱动模型在诊断错误中的实用性。框架向人类用户提供指示,以收集数据,减轻用于培训和测试故障诊断模型的数据之间的差异。框架由三个部分组成:(1) 强化数据收集学习算法,以开发培训数据集,(2) 收集复杂系统缺陷的深度学习算法是困难和耗时费的。(3) 尽可能减少数据诊断错误时所需的数据模型将增强现实应用的实用性。 用于测试数据的精确性测试的每步步步都提供了一次更新的精确度。 提议的每个用户都提供了更新的精确度的步态。