DECEMBER 2020CIOAPPLICATIONSEUROPE.COM 19eliably predicting the place, time and strength of an earthquake - this wish is probably as old as mankind itself. Every machine manufacturer and operator of a plant also wants a reliable prognosis of the end of its service life. The aim is to ensure long-term operation, detect errors at an early stage and prevent failures.In predictive maintenance, the focus is on identifying signs of random failures at an early stage and predicting ageing processes. Therefore maintenance can be planned in good time and the risk of failures and downtime can be minimized.Use of existing dataModern predictive maintenance concepts rely on smart processing of all signals already available, without the use of additional sensors. Countless signals and calculations converge in a modern control system. If all these values are combined with less obvious parameters such as latency times, cycle times, room temperature and even time of day and time of year, a very precise status cloud of a machine is created. CSEM has developed predictive maintenance software that links this data to an intelligent system via neural networks. The intelligent system works in three steps:1. detecting a deterioration of a machine, 2. predicting how the condition of the machine will develop, and 3. identifying the components responsible for the malfunction.Detect anomaliesThe neural network learns how a machine behaves in normal operation. Depending on settings, processed parts or ambient conditions, the measured values of a machine can change considerably, although technically everything is still in the green range. Such patterns must be recognized and stored in the network. The more complex a machine is, the more dependencies there are between different sensors and actuators. With large machines it is impossible even for experienced experts to understand all the relationships. This is where artificial intelligence beats humans, on the basis of the training data it uncovers even hidden dependencies independently and without expert knowledge. To do this, the neural network must sort the valuable from the worthless: Which signals are relevant? Which patterns are normal? Which variables are linked and how? Once the software has learned the normal behaviour of a machine, it can reliably detect when a machine deviates from its normal operating range and evaluate this anomaly.Create predictionOnce the system has detected that the machine is drifting out of the normal operating range, the next question is in what time horizon the fault is occurring (Time to Failure). This is the core of a predictive maintenance solution. A simple linear regression can only achieve very poor results. An accurate prognosis requires a deep understanding of the machine. Not only short-term changes are important, but also states of the machine that date back longer. In order to integrate such past events, so-called recurrent neural networks RMachines with Brain Predictive Maintenance with Deep Neural NetworksPHILIPP SCHMID, HEAD ROBOTICS & MACHINE LEARNING, CSEMPhilipp SchmidcXoinsights
< Page 9 | Page 11 >