Sensors integrated with predictive maintenance software help determine the wear rate of equipment. But how do we detect the wear rate of sensors?
Sensor health: an essential data
In constant pursuit of optimizing production methods, the most competitive companies are equipping themselves with cutting-edge industry 4.0 software. Among these, predictive maintenance software plays a significant role. Asset durability, performance improvement, and cost savings in maintenance have convinced industries from diverse sectors of the numerous advantages offered by these software solutions. However, a particular focus is required on the functioning of the sensors that supply data to these software systems.
Sensors: indispensable tools of industry 4.0
For maintenance software to function properly and transmit a maximum amount of data to maintenance experts, they must be paired with sensors placed on machines. Sensors represent the physical element of the maintenance process. When networked with the software, sensors are capable of collecting and transmitting crucial information for maintenance purposes.
For the past decade, Amiral Technologies has been developing DiagFit, a blind-mode predictive maintenance software, to meet the growing demand for industrial maintenance.
Why are sensors advantageous?
Simply because this generation of intelligent sensors adapts to any type of equipment, regardless of its age. A turning factory, for example, can place a sensor connected to DiagFit on a traditional plastic press and another sensor on a state-of-the-art multifunctional lathe. DiagFit translates the data generated by the sensors to facilitate the maintenance operations of the industry experts.
Predictive maintenance: a valuable ally
The experts are precisely assisted by the indispensable ally of predictive maintenance. It eases their work by translating raw data into accessible information. Experts do not need to be trained in computer programming to understand the results of the analyses.
There exists a true relationship of interdependence and collaboration between humans and machines. To increase production efficiency and improve its quality, humans can rely on tools at their disposal such as sensors and predictive maintenance. The vigilance and expertise of humans are essential for operating these instruments.
Sensors display the wear rate of the equipment they analyze, but they also indicate their own health condition. Aging is a very common cause of failure, and sensors are capable of detecting these signals and assessing their severity.
All of this aims to protect production in the first place. Healthy sensors that detect deviations from normal operating conditions help maintain production. This results in fewer unjustified machine shutdowns and more reliable equipment in the long run.
Ensuring the integrity of equipment with the help of predictive maintenance enhances both site and employee safety. Reliable sensors with a wear rate below a threshold determined by experts provide accurate information on which maintenance operators can rely.
By transitioning to this new era that demands the use of intelligent and increasingly autonomous technologies, companies establish their authority in their respective sectors. Predictive maintenance requires the use of physical sensors connected in a network. Once the configuration is complete, the work of maintenance experts is facilitated, but human presence on-site is required to perform the operations indicated by the analysis results and to ensure the longevity of the sensors, without which the entire process is inconceivable.
