For most, seeing a Union Pacific diesel-electric locomotive painted in the historic Armour Yellow, Signal Red, and Harbor Mist Gray does not bring predictive analytics to mind. However, the largest railroad network in the United States is showing the effectiveness of analyzing non-conventional data forms to increase operational efficiency.
Applications in industries including financial services, insurance, retail, and healthcare are what commonly come to mind when discussing predictive analytics. Thinking outside the box, Union Pacific is currently using bearing acoustic monitors to identify faulty or failing bearings on trains. The acoustic detectors have been used for years, but enough data has now been collected to show the correlation between failing bearings and certain frequency spectrums. This correlation has been applied to real time acoustic readings to help predict bearing failures and ultimately decrease derailments and costly train delays. Download our Case Study on Mechanical Failure and Predictive Analytics.