Artificial intelligence and distributed algorithms have been widely used in mechanical fault diagnosis with the explosive growth of diagnostic data. A novel intelligent fault diagnosis system framework that allows intelligent terminals to offload computational tasks to Mobile edge computing (MEC) servers is provided in this paper, which can effectively address the problems of task processing delays and enhanced computational complexity. As the resources at the MEC and intelligent terminals are limited, performing reasonable resource allocation optimization can improve the performance, especially for a multi-terminals offloading system. In this study, to minimize the task computation delay, we jointly optimize the local content splitting ratio, the transmission/computation power allocation, and the MEC server selection under a dynamic environment with stochastic task arrivals. The challenging dynamic joint optimization problem is formulated as a reinforcement learning (RL) problem, which is designed as the computational offloading policies to minimize the long-term average delay cost. Two deep RL strategies, deep Q-learning network (DQN) and deep deterministic policy gradient (DDPG), are adopted to learn the computational offloading policies adaptively and efficiently. The proposed DQN strategy takes the MEC selection as a unique action while using the convex optimization approach to obtain the local content splitting ratio and the transmission/computation power allocation. Simultaneously, the actions of the DDPG strategy are selected as all dynamic variables, including the local content splitting ratio, the transmission/computation power allocation, and the MEC server selection. Numerical results demonstrate that both proposed strategies perform better than the traditional non-learning schemes.

翻译：人工智能与分布式算法已广泛应用于机械故障诊断领域，随着诊断数据的爆炸式增长，本文提出了一种新型智能故障诊断系统框架，允许智能终端将计算任务卸载至移动边缘计算（MEC）服务器，从而有效解决任务处理延迟与计算复杂度提升的问题。由于MEC与智能终端的资源有限，合理优化资源分配可提升系统性能，尤其适用于多终端卸载系统。为最小化任务计算延迟，本研究在动态环境中联合优化本地内容分割比例、传输/计算功率分配及MEC服务器选择，并考虑随机任务到达场景。该动态联合优化问题被构建为强化学习（RL）问题，通过设计计算卸载策略实现长期平均延迟成本最小化。本文采用两种深度强化学习策略——深度Q学习网络（DQN）与深度确定性策略梯度（DDPG），自适应且高效地学习计算卸载策略。所提DQN策略将MEC选择作为唯一动作，同时通过凸优化方法获取本地内容分割比例与传输/计算功率分配；而DDPG策略则将本地内容分割比例、传输/计算功率分配及MEC服务器选择等所有动态变量均作为动作。数值结果表明，两种策略均优于传统非学习方案。