IoT for the Transportation Industry
The railroad industry continues to face financial pressure and a changing landscape affecting their core business, coal. With the downturn in coal usage and an aging rail system, maximizing existing assets and prolonging asset lifecycle is critical to their survival. Passenger rail faces the primary challenge of commuter safety in addition to the financial pressure of lowering operating costs to stay competitive. Predictive analytics and machine learning offer a new hope and huge cost saving potential providing better insight and predictability in the operations. Assets include linear assets, locomotives, railcars, signal crossing, rail yards, hump stations, building and maintenance facilities totaling billions of dollars.
The industry has evolved from run to failure to scheduled and preventative maintenance. More recent adoption of RCM (Reliability Center Maintenance) offered promises of lower costs. Even with all these tried techniques over the many decades, still unplanned downtime and catastrophic failures continue to cost billions of dollars each year. With the introduction of machine learning at the turn of the century, corporations made investments developing in-house data scientists utilizing highly educated and academically qualified subject matter experts in the field of analytics to develop custom models to predict potential problems. Implementation time took months or sometimes years and in the meantime, costly failures continued. SORBA is one of the first machine learning automation tools to allow engineers and mechanical experts to solve their own problems reducing implementation time predicting failures with very early alert notification. SORBA changed maintenance from reactive to predictive, lowering cost and increasing efficiency.
SORBA’s ruggedized IoT event recorder and edge processor are uniquely designed for transportation. Data is collected from onboard engine sensors, traction motors, sensor panels, cellular detectors, battery monitors, and cameras. Data is then stored on board and transferred through PTC, cellular and Wi-Fi to remote storage systems, data lakes or SORBA-HISTORIAN for predictive maintenance. Custom rules and machine learning agents, process video and sensor data, detecting very early stage events and predictive alert notifications, all in one open platform.
SORBA is an IoT solution that is simple to deploy, complete in its offerings and enterprise in its scale.
Use SORBA to:
A majority of all maintenance is either too late or diagnosed incorrectly and therefore you end up applying the wrong remedy. SORBA identifies the exact problem, applying the proper corrective action, at the earliest stage, before costly degradation starts and causes downtime.
In our work with CSX, we know the railroad industry relies primarily on periodic inspection and repair activities to identify equipment conditions that require corrective actions, in order to maintain desirable service levels. In spite of the massive efforts to maintain the physical plant and the myriad of assets that run on it using the periodic approach, railroad continues to experience costly service disruptions that adversely affect safety, train service, asset life, and asset availability.
How SORBA can help:
Over 10,000 rail switches installed throughout the 21,000 miles of track. A rail switch is a mechanical installation enabling locomotives to be guided from one track to another. Read more about this use case HERE.
A US-based rail transportation company manages 21,000 miles of track and operates a fleet of 4,000 locomotives & 100,000 rail cars. In addition to rolling assets, they maintain maintenance shop facilities, railyards, hump stations, ship to rail terminals and large intermodal transfers. Read more about this use case HERE.