The Internet of Things (IoT) and machine learning are both getting a great deal of attention. Many pundits say there is more substance than reality to this coverage. But one area where the ROI of IoT is clear, is in the area of predictive and prescriptive maintenance.
At the 2016 ARC Industry Forum in Orlando, CSX, a US-based rail transportation company that manages 21,000 miles of track and operates a fleet of 4,000 locomotives, presented the company’s long-term strategy for leveraging predictive maintenance to gain competitive advantage.
In 2004, CSX changed its business strategy to focus on profit drivers and the competitive advantage the company could achieve by leveraging newly available technology.
With the knowledge that the value of the company’s operating assets – mainly comprised of locomotives, cars, and track – exceeded $60 billion, cor-porate eyes quickly turned to asset utilization. With replacement costs in the neighborhood of $1 million each, locomotives represented the largest cost pool to address. In addition, the company’s rail infrastructure – with some rails dating back to 1902 – was starting to show its age.
By tracking company financial results between 2013 and 2014, the company was able to identify strong evidence of an increase in revenue and profit, which Mr. Moty attributed to asset utilization and technology enablement.
Prior to this, CSX had embarked on internal and external benchmarking ac-tivities that began when Mr. Moty attended a Berlin InnoTrans Conference where he came to the realization that Europe was much further ahead of North America in terms of enterprise asset management (EAM). CSX teamed up with ARGO, a consulting firm, to look at internal processes and uncovered surprising results.
CSX Improvement Opportunities (Based on CSX-Arco Studies)
This study revealed that CSX’s expensive locomotives typically only spent 40 percent of the time actually pulling freight; the only time the company makes money. In addition, the company learned that its employees were only productive 23 percent of the time, a staggering statistic. This internal analysis revealed over $100 million in potential savings; driving CSX to shift its focus to improving asset utilization to improve operating ratio and return on assets. This involved several parallel initiatives.
CSX successfully tied a long-term incentive program for executives to operating ratio and return on assets. This new focus on return on assets (ROA) drove CSX to develop new measures for asset performance. The company’s Asset Performance Index (API) describes how much of the time a particular asset is making money and how much time it is unproductively sitting around at a maintenance facility or terminal. The company also created a train delay index to better understand the severity of a trip failure and the cost attributable to delayed trains.
CSX’s core business is to safely and reliably transport customer goods on time. However, the company was previously plagued by operational and catastrophic failures. CSX reliability professionals knew the performance-to-failure curve all too well and since 2003 the company had experienced over $1 billion in total losses in 2,500 train derailments due to human factors, tracks, locomotives, and other causes.
CSX started its journey by initiating a series of projects designed to enable the company to effectively predict and more rapidly resolve (or avoid) issues with its extensive fixed assets (tracks) and costly rolling assets (locomotives and rail cars.) To help on this journey, CSX selected Mtell, a leading supplier of machine learning technologies for industrial assets.
Critical IoT Sensor Feeds
To be able to identify and remediate track-related issues that could cause derailments and wheel or bearing damage on the company’s fleet of locomotives and freight cars, CSX deployed a series of interconnected sensors and wayside systems that continuously study the car movements throughout the US East Coast. CSX calls these “super-sites.” As a locomotive and the entire train pass through these stationary monitoring sites, the system reads the acoustic signatures of the bearings, peak impacts/pounding on the rail (indicating out-of-round wheels), and looks at lateral forces (geometry) such as wild or hunting trucks. Automatic equipment identification (AEI) sensors count the ax-les to let rail maintenance crews know exactly where repairs are needed.
This IIoT-enabled monitoring and predictive analytics system, developed in conjunction with Mtell, helped CSX drastically reduce the incidence of derailments and wheel- and bearing-related failures. The key was to be able to catch the failure before it happens and increase the notice time by which maintenance personnel could act on the alert.
CSX also worked to improve their locomotive reliability. Two percent of the company’s 80, $100,000-plus locomotives were failing each year. CSX decided to embark on a three-phase pilot project with Mtell to determine whether oil analysis could be used to predict costly locomotive failure in time to prevent these failures.
Previously, the company had been performing simple threshold analysis on engine oil samples to alert personnel of issues and initiate workflows with some moderate success. But it suspected it could do more. The Mtell software revealed increases in iron, viscosity soot level, and anti-wear and how the percentage of each impacted the overall signature. The Mtell software further identified 18 low-water-pressure problems that were worth paying attention to. The process involved finding and “training” the Mtell software agents against failures. A new Prescriptive Performance Improvement Request (PIR) workflow was initiated, and CSX was able to successfully move from a “run to failure” operating model to a proactive preventive/predictive maintenance operating mode.
In the future, CSX will integrate all locomotive communication systems into the Mtell software. This will involve over 300 sensors for each locomotive and 1,200,000 potential data points for the enterprise.
Machine learning is not new; its roots can be traced back to before the first computer. What has changed is the use of automated machine learning, the scale of computing power, intensely powerful algorithms that process massive amounts of data, and intuitive uses for time-series sensor data from machines and manufacturing processes. Machine learning enables a new paradigm. Now, rather than having a problem and formulating an algorithm to solve that problem, we can give the machine the problem and let the computer solve it.
Shippers of course, always welcome programs like this that improve the ability of carrier partners to deliver goods on time.
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