It is a known fact that when there are minor performance changes in machines, if we do not have data collection kits that facilitate situation monitoring, we have difficulty in implementing predictive maintenance processes based on data science for existing machines. Condition monitoring plays an important role in predictive maintenance and helps prevent costly downtime by allowing issues to be detected before they become serious. Often misaligned, loose or worn parts cause vibration in the machine. As the vibration and temperature increase, the damage to the machine increases. By monitoring motors, pumps, compressors, fans and gearboxes to detect vibration surges, problems can be resolved before they become severe and cause unplanned system downtime.
With the multi-axis vibration and temperature measurement sensor (MECHASense Sensor) we have developed, the data we collect from the machines are analyzed in our decision support system (MECHA Box), which is also developed by our engineers and has unique machine learning algorithms, and we can observe anomalies in critical equipment on the machine as well as life-life estimation in the same equipment. can also give a chance to realize.