  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.
