This stream of the Awareness platform provides solutions to deliver a more efficient and cost-effective way for our customers to manage the maintenance of their assets.
Assets often fail without warning (power/light poles, bridges, etc) or deteriorate at unpredicted rate with catastrophic consequences. Hayden Data has developed solutions to better predict these incidents to better protect lives, communities, habitats, stock, and assets.
Energy organizations can leverage our solutions to streamline the maintenance of their distribution pole network. This can facilitate transitioning from traditional scheduled-based maintenance to condition-based and eventually to predictive maintenance, providing cost saving along the way.
Today we can deliver condition-based maintenance through constantly monitoring power poles, bridges, buildings, equipment, piping infrastructure, etc… and reporting in near real time when a set of measured parameters reaches an unacceptable threshold. Maintenance can then be scheduled to remediate the issue before the imminent failure of the asset. This allows maintenance crews to focus only on those assets that require maintenance or replacement, bypassing assets that are still in sound working condition. Savings of 20% to 25% have been realised across other industries that have already adopted this type of initiative.
The roadmap for our Generation Two technology will be combined with advanced artificial intelligence analytics to provide a smoother transition to predictive maintenance.
Predictive maintenance compares the data of the asset’s last reported condition to the historical data from hundreds, if not thousands, of similar poles subjected to comparable environmental conditions and stress loads to predict possible failure. The software recommended maintenance is based on known parameters of structural integrity with sufficient time to remediate the risk prior to failure.
Further savings of between 20%-30% have been shown across other industries when they have moved to a predictive maintenance regimen. This combines to deliver cost savings of close to 40% moving from the traditional scheduled based maintenance to predictive maintenance. These savings easily justify the investment in rolling out the Maintenance Stream which could be staged and modularized according to the risk profile of each area and cost benefit analysis to support each asset.
Hayden Data’s Maintenance Stream provides modules that deliver:
- Vegetation Detection
- Pole Impact
- Movement or seismic activity