Artificial intelligent vibration control

You may be unfamiliar with this term, but it is gaining traction in the industry. BOT4.AI defines Industrial Artificial Intelligence (IAI) as a field with significant potential for growth. IAI is poised to revolutionize the way production tasks are handled on equipment. Improving Quality (IAI) through machine learning will reduce unscheduled shutdowns, improve the quality of work, and reduce costs. The BOT4.AI team believes that the evidence of usefulness for IAI is exceptional. However, many remain skeptical about it.
Experts have identified a discrepancy between expectations and reality in the direction (IAI), particularly among heavy industry professionals. This discrepancy can be attributed to the fact that the same model cannot be utilized with disparate equipment and in disparate conditions. We have long been convinced that setting up vibration stands is an extremely important task. Even the surface on which the equipment is installed is important for (IAI). Our competitors show that the range is from 40% to 80%, depending on the machine and industry. In the majority of instances, recommendations regarding actions on the machine or in the process are of greater consequence than the diagnosis itself. It is unfortunate that the effectiveness of recommendation systems can reach only 20%. Furthermore, utilising a single calculation logic can result in false positives. However, we have devised a solution to enhance and optimise the indicators. (more details? hello@bot4.ai)

The accuracy of training and maintenance recommendations is contingent upon a number of factors. In particular, automated rule-based solutions have the capacity to address a comprehensive range of industries and potential issues within a single industry. In recent interviews with vibration analysts, it was frequently stated that, in order to comply with the recommendation, they must consider a number of additional factors, including the enterprise environment, supplementary analysis using portable devices, the emergence of interconnected systems, and others. Consider the multitude of factors that can influence the mechanism in motion. To the ships? Are we discussing tractors or trains?

Machine learning systems can only be created based on data that is measured at the moment and compared with static data, which requires careful preparation. This issue is sometimes considered from the perspective of different stakeholders. If something is not observed or measured, it becomes impossible to make an automatic recommendation. We would like to point out that information is often based on contextual information that is not related to vibration data, but was immediately assigned to the system. Currently, only human specialists are able to perform this task with satisfactory performance (frequency of detection and false alarms). However, we have learned to use a variety of systems in our solution to remove every third triggered system and improve employee performance.