Data analytics has a lot of useful applications and predictive maintenance is one of them. Its achievements are ensuring maximum availability of machines while guaranteeing the production quality. It is important, especially in some industries where the downtime is among the costliest risks of all potential threats. Data analytics is capable of creating machine-learning models trained by real-time data flow that predicts when, where and why a particular machine will fail.
Predictive maintenance is minimizing downtimes, increase productivity and prevents machine faults. A wave of new data generated by internet of things (IoT) can provide real-time telemetry on detailed aspects of production process. Microsoft` data scientists are able to predict the probability of flight delay or cancellation due to aircraft malfunction. They rely on data varying from maintenance history to flight route information. Machine learning solution based on a historical string of data is able to predict in real time the type of mechanical issue that will result in delay or cancellation of a flight during following 24 hours.
The modern vehicles we drive today also benefited from predictive maintenance which already resulted in extended service intervals. ATM machines are another area of application by utilizing sensor data to predict the failure of requested transaction. As we have already discussed in our Energy Data Analytics blog post, there are typical fluctuations in electric usage, voltage, weather conditions and other variables suggesting that a failure is going to occur. Such data can be modeled in order to predict potential network failures. If an electric motor, for instance, shows unusual energy consumption growth, this is a signal that a belt or bearing is worn and it generates more resistance that it should. This way an inevitable failure can be prevented by simple application of predictive analytics.
Predictive maintenance is eliminating the engineers’ guesswork and evolve into minimization or even complete avoidance of any unplanned maintenance or downtime. And sometimes this is crucial.
