Predictive maintenance techniques are designed to help determine the condition of in-service equipment in order to predict when maintenance should be performed. This approach gives cost savings over standard or time-based preventive maintenance, because tasks are performed only when needed.
The main promise of predictive maintenance is to allow convenient planning of corrective maintenance, and to prevent unexpected equipment failures. The key is "the right information in the right time". By knowing which equipment needs maintenance, maintenance work can be better planned (people, spare parts) and what would have been "unplanned stops" are transformed to shorter and fewer "planned stops", thus increasing plant availability.
Other potential advantages include increased equipment lifetime, increased plant safety, fewer accidents with negative impact on environment, and optimized spare parts handling.
Smarter manufacturing by capturing and analysing sensor data
An important area in predictive maintenance is the use of various types of sensors, and the need to be able to understand what the sensor data means.
By applying SensorBeat AI software on real time sensor data, such as motion, temperature, vibration or rotation speed, it's possible to quickly and securely predict and prevent failures.
SensorBeat AI software uses very little power and is hardware independent so it runs on basically aný hardware.