Machine Learning and the AI application

- Jan 09, 2019-

Machine learning and artificial intelligence provide vast reverie space for the industrial Internet of things. At present, industrial applications include self-diagnosis and predictive maintenance, optimization of production processes, intelligent robots, combined with machine vision for product detection. 

(1) Self-diagnosis and predictive maintenance: by using predictive model technology and analyzing the data uploaded by field equipment and controller, the characteristics of equipment fault precursor can be obtained. Cooperate with alarm mechanism, staff can make maintenance and emergency plan as soon as possible. Avoid uncontrollable losses due to sudden arrest of the production line. For example, a malfunction of an industrial robot in operation can result in a large number of nonconforming products. Artificial intelligence technology can learn and build models by detecting data in different states on the robot reducer and spindle. In turn, the fault can be predicted ahead of time. 

(2) optimizing production flow: based on the collected data of temperature, rotational speed, energy consumption and production capacity, the artificial intelligence model analyzes and optimizes the energy saving of the production line, and puts forward a scheme of reducing energy consumption and increasing production capacity. 

(3) Intelligent robot: the robot can read and learn the information about manufacturing products in the associated software model. In the end, the robot does not need engineers to program the robot every time. The robot can directly accept the engineer's instruction and make its own production. In this way, the production line can be more flexible, efficient response to the needs of customized production. 

(4) Product detection with machine vision: it is widely used in semiconductor and 3C industries. Machine vision is a branch of the rapid development of artificial intelligence. The camera of the machine vision system can quickly obtain a large amount of production information. By processing the backstage software and combining the design information and the machining control information, the camera can improve the efficiency, accuracy, quality and flexibility of production instead of manual work.

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