Predictive Pipe Crossing Maintenance

Failures of above-ground pipe crossings that traverse railways, roads and waterways have the potential to cause substantial financial, societal and environmental losses.

Most companies continue to rely on traditional approaches such as Condition-Based Maintenance (CBM) or Preventive Maintenance (PM). While the former could result in catastrophic failures, the later leads to either over-maintenance or under-maintenance as the operational and environmental factors in which the assets operate do not influence this maintenance approach.

Decreasing cost of sensors and increasing technological advancements, promise to make the transition from traditional approaches towards Predictive Maintenance (PdM) more viable. However, practitioners of PdM are faced with the challenges of selecting the appropriate monitoring technology, feature engineering and machine learning algorithms as there isn’t enough empirical data that informs on the failure modes, and the frequency and accuracy of measurements that need to be monitored.

Reliability-centred Maintenance (RCM) aims to optimise the maintenance strategy by recognising the types of failures that affect the function of pipe crossings and implement specific maintenance on the most critical assets.

The pipe crossing cloud application developed for our customers helps to ease this transition from traditional maintenance strategies such as Condition-Based Maintenance (CBM) and Preventive Maintenance (PM) to an optimised maintenance strategy such as Reliability-Centred Maintenance (RCM) and Predictive Maintenance (PdM).

Depending on the type of crossing, structure, material and size, only the relevant sections of the data capture template from over 400 fields are presented to the surveyor. This allows to systematically capture all the features and relevant information in a consistent format that will not only support RCM but will also alleviate the challenges faced by PdM practitioners.