Predictive maintenance
Predictive maintenance
Predictive maintenance represents a specific type of preventive maintenance: it consists of constant monitoring of the conditions of an asset, by means of applying particular sensors.
These sensors supply real-time data that can be used to predict when and how the asset is going to need maintenance, if adequately processed and elaborated through mathematical models and algorithms, with the objective of breakdowns and failures prevention.
Predictive maintenance focuses on real-time evaluation of service life of important components of a machine, in order to maximize their use time.
Doing so helps minimizing maintenance cost and optimizing equipment available time, which in turn increases productivity and allows maintenance team to plan more efficiently their interventions.
There are two basic requirements: presence of an indicator to evaluate the lower failure tolerance of the component and the availability of an adequate time interval between detection of the failure and the failure itself.
Predictive maintenance: advantages and characteristics
By analyzing data measured through IIoT (Industrial Internet of Things) devices installed, CAAR can support its clients in implementing predictive maintenance to analyze continuously the equipment during a normal work cycle, in order to reduce the occurrence probability of unexpected failures.
Our company has developed a work method that allows the implementation of predictive maintenance on any industrial asset, including those less prone to data digitalization.
The practice of predictive maintenance has enormous advantages, especially the reduction of machine down time between the moment of occurrence of the failure and when it is repaired, with the obvious consequence of higher productivity.
A sudden breakdown or malfunction can be dangerous for the operator, thus Safety as well is favorably affected by this maintenance philosophy.
Finally, taking advantage of IIoT data collected through the sensors installed on the equipment, system designers can use those data to extend useful life, improve duration and reliability of the equipment and build machines that are more efficient for the future.
