基于遗传算法的机械设备故障检测机器人设计

当前故障检测机器人受到超声波影响故障检测存在精准度低的问题，据此提出了基于遗传算法的机械设备故障检测机器人设计。采用AD500-1A型号传感器采集机械设备内外部数据信息，使用等效转换电路使机器人实时感知周围环境变化信息，并利用灵敏度高电子仪器实现机器人传感工作；使用2路200万数字网络高清摄像头，监视整个机械设备，获取机器人结构通信、管理和运动信息；将proGee0813型号芯片作为导航设备定位芯片，根据实际需求获取信号指令，并选定机器人行驶路径；通过Unity与UE4引擎虚拟现实硬件交互设备进行故障定位追踪；利用关节装置连接车轮前臂和上臂，实现不同磁铁吸附与脱离，依据机器人结构，完成机器人硬件结构设计。采用遗传算法确定导航适应度函数，通过机器人视频采集信息，设计预警功能，并利用机器人即时生成设备故障图像，依据实现流程，在超声避障功能支持下，完成机械设备故障检测。由实验结果可知，该机器人检测精准度最高可达到0.96，提高了机器人检测鲁棒性。

Design of fault detection robot for mechanical equipment based on genetic algorithm

The current fault detection robot is subject to the problem of low precision in the detection of the fault of the ultrasonic wave, and the design of the robot fault detection robot based on the genetic algorithm is proposed.Ad500-1a sensor is used to collect the internal and external data information of mechanical equipment, the equivalent conversion circuit is used to make the robot sense the change information of surrounding environment in real time, and the high sensitivity electronic instrument is used to realize the sensing work of the robot; two 2-way high-definition digital network cameras are used to monitor the whole mechanical equipment to obtain the communication, management and motion information of the robot structure; progee0813 As the positioning chip of navigation equipment, the model chip obtains the signal command according to the actual demand, and selects the driving path of the robot; carries out the fault location and tracking through the virtual reality hardware interactive equipment of unity and UE4 engine; uses the joint device to connect the wheel forearm and upper arm to realize the different magnet adsorption and separation, and completes the hardware structure design of the robot according to the robot structure. Using genetic algorithm to determine the navigation fitness function, through the robot video collection information, design the early warning function, and use the robot to generate the equipment fault image in real time, according to the implementation process, under the support of the ultrasonic obstacle avoidance function, complete the mechanical equipment fault detection. The experimental results show that the detection accuracy of the robot can reach 0.96, which improves the detection robustness of the robot.