Off-Road and Special Purpose vehicles are used all over the world in various environmental conditions. They exist in all different kinds and formats and, within companies, a broad range of types of such vehicles will be in use.
Maintenance on these vehicles, be it preventive or corrective, can cause unavailability, having a negative impact on both productivity and efficiency. An overall objective regarding maintenance is to maximize the availability of the vehicles at the lowest maintenance cost. Therefore, a pro-active and preventive maintenance approach should lead to important savings, with higher availability.
The ILIAS approach
Most of these vehicles are already equipped with on-board HUMS systems/black boxes. The data generated by these on-board systems contain a broad range of information that can be used as input in a MANTIS-based platform in order to optimize the full maintenance strategy.
The diversity of HUMS systems, however, is very broad, even on the same type of vehicles. Each vehicle has its own configurations, interfaces, data formats, etc. Hence, there is a need to convert the collected data from various systems into a uniform and structured format in order to make them further exploitable.
This observation has led us to the conclusion that there are two viable approaches to building MANTIS-based platforms:
- A per-vehicle type / HUMS system platform approach, aiming at an optimum maintenance strategy for a small number of equipment types.
- An open platform approach that can be easily customized by the user to the type of vehicle/HUMS system(s) being used.
We opted for the second approach but have not limited it to the collection of data only but broadened it to a complete set of functionalities within the MANTIS-based platform.
Based on the experience and know-how gained from the collaboration within the MANTIS project and the architectural guidelines derived from it, ILIAS Solutions aims at building a platform that provides a complete solution from the readout of the black box until the optimization of the maintenance plans in an environment with high numbers of highly complex and mobile assets.
The ILIAS platform, therefore, provides users with an integrated set of user-friendly tools, permitting them to:
- Import data from external sources like ERP systems, leading to a centralized data set. (Step 1)
- Import raw data coming from any kind of HUMS system and cleanse them, based on automatic data wrangling, leading to state detection and health assessment. (Steps 2, 3, 4)
- Make analysis of the data via different algorithms and translate them into rules/conditions to apply in the system. (Steps 5)
- Define rules/conditions, including use and abuse rules, for triggering maintenance or other linked actions, based on the combined dataset. (Step 6)
- Approve/disapprove the system-proposed maintenance actions and register them to make the system self-learning. (Steps 7, 8, 9)
This figure illustrates the approach.
The figure below illustrates how we go through different steps in implementing the platform, following more iterations to improve the system.
For Off-Road and Special Purpose vehicles, the overall objective regarding maintenance is to maximize the availability of the vehicles at the lowest maintenance cost. Thus, a proactive and preventive maintenance approach leads to important savings, with higher availability.
ILIAS Solutions aims at building a platform that provides a complete solution from the readout of the black box until the optimization of the maintenance plans in an environment with high numbers of highly complex and mobile assets.
This platform should be an open platform that can be easily customized by the user to the type of vehicle/HUMS system(s) in use and where a number of rule sets/conditions are defined in a user-friendly way. This allows the system to trigger predictive maintenance actions. Analysis of broad data sets will lead to additional rules and conditions, optimizing the platform it selves.