Remote Condition Monitoring: combining tech and data to enable predictive maintenance and asset reliability in Greater Manchester

Context:  Innovating with predictive maintenance to improve operational reliability  

In 2017, KeolisAmey Metrolink (KAM), the joint venture between Keolis and Amey, took over operations and maintenance of Greater Manchester’s Metrolink light rail network for a period of up to 10 years. The largest of its kind in the UK, KAM serves 99 stops across eight different lines along almost 103 km of track, with a fleet of 120 trams completing more than 34 million journeys a year. A major focus of KAM’s contract is to innovate using predictive maintenance techniques and improve operational reliability to the benefit of all users, in addition to customer service and security.

Innovation: Leveraging the latest tech to enable predictive maintenance of machines and equipment 

Advances in Internet of Things (IoT) technology, data collection and cloud computing in recent years have created many technical and engineering opportunities for maintenance efficiencies. Remote Condition Monitoring uses these technical leaps to embed predictive maintenance to monitor machine and equipment status, performance and behavior from a distance, thereby improving asset reliability, safety and operational performance. 

In the past, asset inspections were carried out in a more traditional manner with track walks, manual measurements and daily checks of trams in the depot. Today’s RCM project has two main workstreams that were introduced in early 2020. The first is the use of RCM equipment at the Trafford Park Depot to automate the inspection regimes of the tram wheel tread profiles and pantograph head condition. The second is the use of tram-mounted monitoring equipment to measure and record “ride quality” by tracking everything from automation of wheel and tire measurements, rail wear and track geometry, to video capture of strategic assets such as overhead lines (OHL). This combination of innovation in technology with data requires new processes and is driving a culture shift in engineering, showing how “new blood” can invigorate a long-standing network. 

Value: Continuous equipment tracking for greater predictive maintenance management   

This smart infrastructure uses embedded devices, linked by both fixed and wireless infrastructure, to report on the condition of assets through real-time alerts, enabling faster, more effective maintenance. Data is constantly collected through sensors, cameras and other mobile technologies that monitor criteria such as temperature, motion, vibration and pressure. Processing this data provides operators with a comprehensive view of the KAM network to identify where maintenance is needed before components show signs of fatigue, compromise or failure. This not only saves on the costs (financial and down-time) of serious repairs but also allows for efficiencies in terms of staff planning. The increased automation and accuracy of the data collected is improving decision making from a maintenance and renewals/enhancements perspective. In turn, this improved process is driving KAM to their desired predictive maintenance approach as opposed to a classic time-based and asset failure approach. 

Next Steps: A promising future for predictive maintenance across the network 

Project successes are already showcasing the potential for replicability in other networks, especially other light rail networks in Keolis' portfolio. Several benefits mentioned above have already been realized, such as the detection of asset related issues, the avoidance of further damage and disruption and resource efficiencies. There is more to identify as all systems are rolled out and calibrated.