When you mention the Internet of Things (IoT) to a seasoned railroad veteran, you expect to get an eye roll and a lot of suspicion in return. After all, we work in an industry that is traditionally slow to adopt any new form of technology, and why should we rush? After all, the basic function of railroads remains largely unchanged over the decades. As the adage goes, “if it isn’t broken, don’t fix it”.
The Massachusetts Bay Transportation Authority’s (MBTA) Railroad Operations Directorate is charged with the management of the 5th largest commuter rail operation in the United States. Operating 500 trains per day on 14 different lines with a mixed fleet of over 100 diesel electric locomotives, the MBTA was aggressively searching for new and innovative ways to forecast our fleet availability and lifecycle, while also trying to understand how to best identify gaps that could introduce a risk to service delivery. With some of the fleet showing signs of aging (locomotives are between five and 30 years old), we were building a plan that would refresh the fleet over time. But, with the increasing demand for service not going away, it is imperative to use every available tool to help drive expectations and deliver the best possible service for our passengers.
Fast forward to the spring of 2018, when the MBTA embarked on a pilot programme that utilised predictive analytics and coupled it to our locomotive fleet, harnessing one of the most basic elements of an internal combustion diesel locomotive – lubricating oil.
When you examine the oil’s function, you can see that it acts as the ‘blood’ of the engine, as, without oil, the engine will not run. Oil sampling has been around for many years. We run oil-sampling metrics, and treat the data as a lagging indicator for engine health. Oil reports historically look at individual elements like copper, iron, lead, tin, aluminium, calcium and sodium. There are high and low critical warning thresholds, but we look at them in their own ‘silos’. When we had a failure in the past, we would go back to the maintenance history and also pull the last oil sample, looking for out-of-range values. What we had to do was understand that the humble oil sample was actually a molecular ‘blackbox’ recorder that had a powerful story to tell, measured in parts per million (ppm).