The downtime of industrial machines, engines, or heavy equipment can lead to a direct loss of revenue. Accurate prediction of such failures using sensor data can prevent or reduce the downtime. With the availability of Internet of Things (IoT) technologies, it is possible to acquire the sensor data with high frequency. Machine Learning and Deep Learning (DL) algorithms can then be used to predict the part and equipment failures, given enough historical data. DL algorithms have shown significant advances in problems where progress has eluded the practitioners and researchers for several decades.
Sensors convert physical signals into electrical signals. This makes it possible to measure physical quantities in the environment. If such measurements are made repeatedly and stored, the behavior of the physical quantity can be studied to gain valuable insights.
ML offers algorithms that learn from data. The algorithms learn a representation of the training data, which is then used to make predictions on out-of-sample data.
Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before.
