FMS Data to Support Service and Maintenance – Part 2

Part 2: Telltales as a Core Element for Off-board Diagnostics

In any vehicle the driver gets instant information about the condition of his vehicle by a set of various warning lights. The information by these lights varies from “yellow” which indicates the necessity of service – but no restriction of the current operation – to “red” which means the need to stop instantly. In a modern electronic architecture these lights are governed by the so-called telltales – messages on the CAN bus for on-board diagnostics. The icons corresponding to the telltales are standardized to allow an easy-to understand information flow, see picture 1.

Whilst a red alert signaling a technical problem of high impact normally triggers an instant reaction by the driver – he will stop his vehicle immediately – a “yellow” alert might be overlooked at least by not planning any inspection work being recommended or necessary. And so, a missed “yellow” indication might lead finally to a “red” alert with significant impacts and much higher corrective maintenance effort. This applies even more if the driver is able to report to the workshop only with a huge time delay – which is the case e.g. in public transport if one driver hands over his bus to another driver or he returns his bus in the late evening to the depot, hesitating to create bigger manual reports about the condition of the bus.

To bypass this communication gap and to also shorten the time delay between occurrence of an error indication and its notification in the workshop, the warning signals on the driver’s dashboard must be forwarded with a minimum time delay – the move from on-board to off-board diagnosis. This can be done by a gateway in the vehicle, reading the FMS messages including the telltales from the CAN Bus and forwarding it to the landside, see picture 2. Having this information available in the workshop would, quite instantly, make maintenance in a workshop much more efficient up to the implementation of processes for predictive maintenance – so a higher vehicle availability and reduced service cost can be achieved.  

 

To benefit from the FMS data forwarded to the landside, the workshop manager needs easy-to-use tools to visualize and analyze. First of all, the backend itself can monitor thresholds and trigger alarms, especially in case of red alarms, by dedicated telltales which require instant actions. Secondly, with all data arranged in time series, dashboards can give an easy and quick overview about availability as well as support planning work for upcoming service activities, especially if the system integrates data from operational management like timetables or planning for scheduled services. 

The basic architecture of this combination of instant alerts and long-term analytics is shown in picture 3. The data read from FMS are forwarded in a continuous data flow to the backend, from which the data are sent in packages to the cloud. The use of such packages to transmit to the cloud helps to reduce data traffic.

 

 

 

A well-designed dashboard using the telltales can give good support to make planning in the workshop more efficient. A quite simple example are the levels of operating supplies like the windshield wiper fluid. A low level is indicated by a dedicated telltale with a yellow alarm and knowing in advance, refueling can be scheduled for the night shift in the depot. 

A second example of how to make use of the telltales are the door signals. Today most doors are still pneumatic, so there is no ECU to generate early warnings (like with the transmission), but telltales show if the driver must make several attempts to close a door to be able to start from a bus stop. The dashboards can show whether there are any cumulations of these telltales to trigger corresponding maintenance.

Finally, an increased failure rate of the external lighting help to detect quality problems and to identify bad lots of lighting equipment, also detected by cumulation of the related telltales. Like with the doors, it is the cumulation which the driver would not realize but can easily be found out by simple rules for the dashboard.

 

 

Of course, the so far described examples are still in the field of reactive maintenance – reacting on deviations from the standard and triggered by the telltales showing that such a deviation is present. But it opens the door to a much more advanced approach: Just as an example, if the time series in the cloud of the telltale “door status” plus other relevant input signals are combined with a feedback from the workshop due to maintenance activities, AI or machine learning algorithms can help to generate rules how to come to a predictive maintenance strategy. And this example applies to many service relevant components, as mostly there are no dedicated sensors so one must go the way via virtual sensors or at least AI based rules to forecast the necessity of maintenance.

Literature:

[1] Pilotfish BusForce: FMS Data to Support Service and Maintenance FMS Data to Support Service and Maintenance – Busforce (pilotfish.se) 

[2] Voith Media Database

Disclaimer: The content of this blog post is the author’s opinion and doesn’t reflect the opinion of any other person or organization.

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