###### Abstract
From the above references, efficient computational offloading decisions based on RL methods can help the system to reduce computational complexity and computational time cost. In the framework of the intelligent fault diagnosis system proposed in this paper, the user layer consists of intelligent terminals with certain computing power, and the mobile edge layer consists of MEC servers with strong computing power, as shown in Figure 3. The intelligent terminal offloads the fault diagnosis data to any MEC server proportionally through the agent server's policy. The optimization problem becomes an offloading decision problem in a dynamic MEC environment, and the current channel state information (CSI) cannot be observed while making the offloading decision. The offloading policy should follow the predicted CSI and task arrival rates under the intelligent terminal and MEC server energy constraints aiming to minimize the long-term average delay cost. We first establish a low-complexity deep Q-learning network (DQN)-based offloading framework where the action includes only discrete MEC server selection, while the local content splitting ratio and the transmission/computation power allocation are optimized by the convex optimization method. Then, we develop a deep deterministic policy gradient (DDPG)-based framework, which includes both the discrete MEC server selection variable and constant local content splitting ratio, the transmission/computation power allocation variable as actions. The numerical results demonstrate that both proposed strategies perform better than the traditional non-learning scheme. The DDPG strategy is superior to the DQN strategy as it can online learn all variables. Compared with the traditional fault diagnosis system, the intelligent fault diagnosis system migrates the original computing tasks based on the central server to the edge computing system, which reduces the computing load of the central server, slows down the network bandwidth pressure, and improves the real-time data interaction. On the other hand, the new intelligent fault diagnosis system solves the problem of the single function of traditional instrumentation systems, which increases the intelligence of instrumentation and makes it easier to access other intelligent methods. The contributions of this paper can be summarized as follows. 1. A new framework for the intelligent fault diagnosis system based on the MEC framework is proposed, in which MEC servers and intelligent terminals can process monitoring data and the ratio determined by the offload policy of the agent server. Compared with the traditional fault diagnosis system, the intelligent fault diagnosis system solves the problems of limited computing resources and network delay and increases the intelligence of the equipment. 2. Two offloading scenarios of the intelligent fault diagnosis system are modeled: one-to-one and one-to-multiple. One-to-one means that one MEC server can only be connected by one intelligent terminal simultaneously, and one-to-multiple implies that multiple intelligent terminals can be connected to the same MEC server simultaneously. The optimization goal is taking the maximum time delay for the system to complete the computation task at each time slot. Every intelligent terminal and MEC server has its energy constraints, and the agent determines the power allocation during the offloading process. 3. The offloading decision optimization algorithm based on the combination of convex optimization and deep reinforcement learning is designed. Firstly, the convex optimization methods are used to solve the connection problem of the intelligent terminal needing to choose which MEC server. Then, the resource allocation of intelligent fault diagnosis system offloading is given by the DQN and DDPG algorithm. The remainder of this paper is structured as follows. The intelligent fault diagnosis system models are provided in Section 2. The DDPG-based Offloading Design and DQN-based Offloading Design are described in Section 3 and Section 4, respectively.