The Internet of Trains

And in the UK, Siemens conducted a pilot project with a large European train operator on one of its regional routes. The project analysed a relatively small data set of one million sensor-log readings, taken in five-minute intervals over the course of a year. Analysts measured variables such as component temperature and pressure from 300 different sensors. This data was overlaid with many thousands of corresponding reports of failures and fixes. Then the team combined data sources, defined the most relevant engine problems, and divided the data into appropriate sections. They used the Teradata Aster Discovery Platform’s exceptional range of analytic tools to evaluate the combined data from different perspectives. It highlighted variables that helped to predict engine problems and identify failed elements that triggered the malfunction of other components. Siemens then applied the Aster nPath function to categorise the different sensors attributing normal, high, and low values, then tracking changes. These changes revealed which sensor pattern was likely to result in engine failure. For example, on a number of occasions Siemens found that when the engine temperature dropped from mid to low then rose to mid value again, an engine failed three days later. During the validation process, comparing results from the test data to the total data set showed a high degree of accuracy, proving that sensor-data analysis makes it possible not only to predict engine failures, but to react in time to prevent them.