Know when rail sanding is working, and when it is not.
Rail sanding systems are designed to improve wheel and rail adhesion when conditions require it. Every sanding event generates data, but that data is only useful when it can be understood in the context of the train, its location, speed, operating conditions, and what happened before and after the event.
D4 brings these signals together into a single workflow. It can process sanding events as they occur, combine them with other operational and external data, and expose the resulting insight to the people and systems responsible for keeping trains running safely and reliably.
A sanding event on its own tells you very little.
A rail operator may know that a sanding event occurred, but that does not necessarily tell them whether the system behaved as expected. To understand an event properly, it may be necessary to know where the train was, how fast it was travelling, what the weather conditions were, whether similar events occurred nearby, and whether the behaviour differs from what is normally expected.
That information is often available, but across different systems. Train telemetry, event data, location information, weather services, and historical records may all have different formats, identifiers, timestamps, and interfaces.
This makes it difficult to move from an individual event to a useful operational picture. Teams can end up investigating problems manually, looking at several systems to establish what happened and whether it is something they need to act on.
Turn individual events into contextualised rail intelligence.
D4 receives sanding events and other train signals as they are generated. Incoming data is validated and structured so that events from different sources can be handled consistently.
The workflow can then combine the sanding event with relevant operational context. Train position and speed can establish where and under what conditions the event occurred. Weather and other external data can add environmental context. Historical events can provide a baseline for identifying behaviour that differs from what is normally expected.
From there, D4 can apply defined rules, pattern analysis, or governed AI to the resulting data. The AI does not need access to the raw sensor streams. It receives the relevant, curated context and produces a structured result that can be validated and handled by the surrounding workflow.
The final result can then be exposed through an API, dashboard, alert, report, or another system depending on what the use case requires.
One event. Multiple signals. One contextual view.
- Sanding events
When sanding occurred, including the relevant event metadata and timestamp. - Train position
GPS coordinates and location information associated with the train at the time of the event. - Train speed
The train's speed when the event occurred, providing important operational context. - Weather conditions
External weather information associated with the train's location and time. - Historical events
Previous sanding events and related operational data used to establish patterns and identify changes. - Operational data
Additional information from connected rail systems that can help explain the circumstances around an event.
From a recorded event to an actionable signal.
Instead of simply recording that a sanding event happened, the operator can see the event in context.
D4 can help identify patterns such as unusual changes in sanding activity, events occurring under unexpected conditions, or behaviour that differs from the historical pattern. These signals can be surfaced to the relevant team without requiring them to manually correlate information from multiple systems.
The same workflow can also support different consumers. An operations team might need a live view of unusual events. A maintenance team might need a list of trains showing potential issues. An application might need a structured API response. A reporting system might need historical data for further analysis.
The underlying data and processing can remain the same while the output is shaped for each specific use case.
Have a data challenge hiding in your operations?
D4 can connect the signals you already have, add the context you need, and turn them into something your teams and systems can use.
Tell us what you are trying to understand, improve, or automate.
