In a time when there is pressure to reduce costs, doing routine or scheduled channel maintenance cleaning weeds and silt channels is a costly option. However, deliveries that fail to meet service standards, unplanned reactive maintenance and excess wear and tear on channel walls have even greater costs.

Predictive maintenance is now a cost effective alternative using your channel operational flow and level data to monitor their condition.

The aim of predictive maintenance in this context is first to predict when channels need remediation or cleaning (or TCC retuning), and secondly, to prevent service delivery failures or channel wear and tear by performing maintenance.

Ideally, predictive maintenance allows the maintenance frequency to be as low as possible to prevent unplanned reactive maintenance, without incurring costs associated with doing too much preventative or routine maintenance (such as spraying all weeds every year).

When predictive maintenance is working effectively as a maintenance strategy, maintenance is only performed on channels when it is required. That is, just before problems are likely to occur. This brings several benefits:

  • minimising the time and cost of maintenance or remediation
  • minimising the disruption to customers of unplanned outages
  • maximising the customer service in water delivery

Take the example of the Bombora Channel shown in the diagram. Channel and meter data from SCADA systems is used to calculate service levels for delivery efficiency, reliability and on-time delivery and the stability of channel levels. This could be calculated for any channel or set of channels

When we plot these service levels over time, we notice that from 2012, two of the service levels began declining and continued to decline indicating problems that are not transient and that need to be investigated. We can extrapolate these values and act before they fall below our defined targets.

Bombora Channel

Bombora Channel service levels

We can target our maintenance efforts based on real data, making maintenance expense much more efficient and ensuring high service to customers at all times.