Asset management systems that don’t store spatial data, in my view, can cause significant hindrance to your organisation.
Whatever your assets, there are many spatial-based techniques to capture data about assets:
- Drones or planes capturing LIDAR data (a laser scanning technique that captures point cloud data);
- Aerial imagery; and
- Various other remote condition monitoring techniques which have a spatial representation.
But if your asset register can’t store spatial data, it is still very hard to “join” or “align” this captured data to the asset record. So why does this matter?
A lack of spatial awareness significantly reduces the value you get from the captured data because it makes it difficult to monitor and maintain your assets.
For example, look at the above LIDAR image which may have identified a suspected defect on part of the overhead line structure.
- LIDAR tells us precisely where the structure is, but it can’t know information such as an asset ID;
- To do that, we need to join it to the asset. How?
- A “spatial join” would identify assets in the same location between two systems – If LIDAR has found an asset here, and the asset register has an asset here, we can join the information;
- Now we can investigate the history of the asset further, and potentially send someone to inspect/maintain the asset.
However, if the asset register has no spatial representation of the asset;
- It is not possible to perform the above join;
- You can’t know where all your assets are – you couldn’t simply view them all on, say, Google Maps;
- You can’t simply say “there is an asset here, a problem here with that asset, Person A has responsibility for assets in that location and needs to go and inspect this asset;
- All that quality asset-condition data becomes much harder to derive value from.
For another example of this problem, let’s look at another data capture technique that uses linear referencing; Train-borne remote condition monitoring. To capture data, the recording system needs to know where on the network it is (its’ linear reference), so it uses a network model to derive a linear reference from, and to record data against. For this data capture technique, it is critical to have a track model to derive linear references from.
- The asset register could not perform linear referencing;
- The asset register is not capable of storing the network model in order to perform a linear reference function;
- This means you can capture the condition data from a train on the track, to a high degree of accuracy, but it is very difficult to align this data to other track data in the asset register;
- As with the above example, it’s difficult to say “where have a suspected problem at, say, 5525m along this track, therefor we can inspect historic data for this location and send Person B to go and inspect more closely.
The underlying problem in both examples is that the core system (the asset register) did not store spatial geometry, and this made it impossible (in these examples) for the organisations involved to align highly (spatially) accurate condition data to their asset register and realise the full benefits of these condition monitoring techniques.
Perhaps a pertinent question is “are clients informed enough about the need for spatial data as a core function of a modern asset management system?” Are you as a client asking the right questions of your system suppliers, and identifying the right functional requirements of an asset management system?