Banner Engineering’s new QS18 Expert models have joined the QS18 Clear Object Detection and QS18 Background Suppression sensors, which are already available with IO-link, to provide a complete solution for IO-link enabled photoelectric sensors.

 

There are many advantages of IO-Link including standardised and reduced wiring, increased data availability, remote configuration and monitoring, simple device replacement, and advanced diagnostics. Together these capabilities result in overall reduced costs, increased process efficiency, and improved machine availability. Now, these benefits are available in a compact, economical photoelectric sensor.

 

QS18 Expert sensors with IO-link also allow users to more specifically determine how the sensor will perform. For example, users can set hysteresis sensitivity, on and off delays, offset percentages from teach, and response speeds. In addition, users can set the sensor to Push Pull, PNP, or NPN so that one sensor can be used for any output type. Furthermore, users can control health thresholds to define when to trigger an alert for cleaning or maintenance or signal when a runtime threshold is reached.

 

There are many advantages of an IO-link system including standardised and reduced wiring, increased data availability, remote configuration and monitoring, simple device replacement, and advanced diagnostics.

 

IO-link sensors provide process data that typically includes sensor output, health, marginal light, marginal dark, and signal strength. The new QS18 Expert sensors with IO-Link provide even more pre-defined process data including signal (12 bit ADC value); counts (detection events since last reset) for counting product being sensed without a PLC; duration (ms) between events measures time between detection to determine production speed; and events per minute to track production rates for overall equipment effectiveness (OEE) calculations and trigger an alarm if goals are not met.

 

This data can trigger real-time alerts or can be logged for long term analysis to improve efficiency and predict maintenance requirements.