Points condition monitoring

Points condition monitoring

Our Points Monitoring System (PMS) combines sensor technology, machine learning, and effective visual displays to provide a complete picture of critical assets.

We have developed the PMS through close collaboration with our customers. The system is designed around our ‘predict and prevent’ principles and provides early detection of problems before they cause disruption.

How it works


The basis of the PMS is the PCMAlert module which is deployed to monitor; three phase AC, single phase AC, and DC point machines. The module is connected to current sensors, a supply voltage reference, and spare (i.e. voltage-free) contacts on the points detection relays. The combination of these inputs provides a comprehensive set of:
• Direct measurements (current, voltage)
• Derived measurements (instantaneous power consumption by phase and in total)
• Statistics covering each movement (e.g. total energy use, time to move, peak current)
• Force calculations (assuming information about machine efficiency is available)

We use machine learning to accurately identify the key stages of the points operation. This enables the system to automatically identify the performance of each phase of the points move, allowing the AssetVIEW system to guide the maintainer to the cause of the pending fault. This improves overall reliability, saves on time to repair and builds a more complete picture of the common failure modes that could be eliminated by either design or process improvement in the future.

Key features


  • Intelligent analysis, alerting and diagnostics with machine learning techniques
  • Secure cloud implementation or local installation
  • Effective graphical displays and data commentary / input methods



  • Provides early detection of problems before they cause service disruption
  • Informs maintainers when preventative maintenance is required
  • Enables the effectiveness of the maintenance carried out to be assessed immediately and in the longer term
  • Allows any failures to be repaired more quickly utilising enhanced diagnostics
  • Enables better understanding of failure modes and system weaknesses to be addressed
  • Allows renewals to be planned to prevent failure due to age/wear based on actual usage statistics rather than standard intervals