Vibration analysis offers a lot of information to help understand the functionality and the overall health of rotating machines. As we explained in a previous article, if vibration analysis is widely used, especially in the monitoring of complex and/or critical machines, it is because it is the only technique to combine early detection of a wide range of defects with reliable explanations for the problem encountered.
Like all other monitoring solutions, vibration analysis evolves with the input of new technologies (artificial intelligence, lloT, Cloud). Thanks to these new technologies, experts of vibration analysis are gaining not only efficient and reliable diagnostics, but they also now have the means to move from a conditional preventive maintenance strategy (which is still very often the level to which planned maintenance is limited) to a strategy of maintaining equipment at good working conditions. This is where the success of vibration analysis 4.0 plays out.
Remotely supervise an entire group of machines
Thanks to new supervision tools, it is now possible for a vibration analysis expert to monitor machines from anywhere in the world. The dashboard will provide him/her with an overview of the machines and will report which ones could be problematic. The supervision tool allows the expert to see, in the real sense of the word, the state of an entire group of machines. It allows the expert to avoid the tedious work of screening, compiling and prioritizing information. All the more important is that it allows the compilation of data from all the machines, regardless of how they are monitored (by wired or wireless monitoring, continuous or periodic monitoring).
Thanks to new intelligent diagnostic indicators, it is possible for the expert to target more quickly the defects on which their analysis is focused. The signal passes through the filter of an automated process which then goes back to the visual indicators. The expert can then access the full signal to carry out a thorough diagnosis.
With the development of artificial intelligence, these indicators are increasing in number, accuracy and reliability, allowing experts to be more efficient and potentially monitor more machines. (See our article « How does artificial intelligence improve vibration analysis? »).
Moving from anticipating breakdown to improving machine health
By frequently acquiring rich signals via online solutions and searching through these signals, thanks to machine learning, indicators are more and more precise, making it possible to detect abnormal behavior in a machine which conventional indicators would have classified as healthy.
To be able to intervene as quickly as possible in the monitoring of machine conditions makes it possible to reduce maintenance costs further and increase availability.
It can equally allow maintenance services to go even further and significantly improve the health of their machines in order to reduce, for example, energy consumption and increase production quality. With vibration analysis 4.0, predictive maintenance is at its full potential.
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