 |  |
How good would it be if you could tell which machine in your factory is almost worn out and needs repair before it breaks down and causes loss of work for a day?
Industrial and technical machines are normally serviced using a specified schedule. This always leads to the risk of unexpected and sudden machine failures and wasted labor most of the time. I believe we can have more effective servicing schedules and achieve better performance when sensors used with these machines and information gathered is fed to Machine Learning models.
An example of the implementation of the following is GE’s manufacturing of Digital Twins, where a virtual model is created for big equipment and machines. Many sensors either in hundreds or thousands are used to ensure that the twin is made aware of the condition of the real equipment or machine. This ensures that the twins currently deployed (approximately 650,000 twins) are serviced and managed. It is expected that there will be further implementation of digital twins in intelligent factories across the world.
