New smart gearbox enables Industry 4.0 predictive maintenance

Available from Mclennan, Wittenstein’s new cynapse® smart gearbox combines built-in sensors with analysis and visualisation software to monitor significant gearbox threshold data that is communicated via IO-Link to PLCs as well as higher level IPCs/Gateways and Cloud systems for comprehensive IIoT integration. Data including acceleration, vibration, operating time, and temperature etc. can be evaluated as part of Industry 4.0 predictive maintenance.

Mclennan, the leading motion control equipment supplier and systems integrator, has added the new cynapse® smart gearbox line from Wittenstein to its wide range of technologies. With its ability to collect, analyse and visualise built-in gearbox information and sensor data, cynapse adds a layer of predictive maintenance that is stored locally in the gearbox, or communicated via IO-Link to PLCs as well as higher level IPCs/Gateways and Cloud systems - enabling comprehensive IIoT integration. By directly monitoring and logging gearbox threshold data such as acceleration, vibration, operating time, and temperature etc. - where the gearbox output is often directly connected to the critical load-point of typical position-controlled motion systems – applications across automated machinery and processing can achieve real benefits in higher transparency, reduced downtimes, and improved system reliability. To support cynapse Wittenstein offers its Smart Services software comprising various functions that include a Monitor control terminal that allows graphical visualisation of all gearbox data, a Data Gateway facility to easily configure integration with PLCs and higher-level systems, a Teach-In facility for straightforward set-up of gearbox threshold values, and an  Anomaly Check facility that detects nonconformity in the process or the component behaviour.

A single IO-Link connector interfaces the gearbox allowing retrieval of sensor information which includes acceleration in X, Y and Z spatial axes as RMS and Peak-to-Peak values as well as temperature and runtime. Derived information and events such as vibration levels, gearbox orientation, ambient temperature, operational duty time, and more are thus made available via commands through IO-Link. This data can be compared alongside the expected lifetime predictions and maintenance periods for the specific application to realise comprehensive predictive maintenance for individual motion system axes.

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