Case Study

Visualising trends and patterns across millions of alarm logs every month

Control Rooms are constantly inundated with thousands of network alarms. With overwhelming amounts of alarm data, determining root cause and priority amongst the noise is a challenge. Data visualisation, machine learning and advanced analytics can help address these challenges.


The Challenge

To improve alarm management and resolution, a large electrical infrastructure company needed a quick and visual way to identify, analyse and prioritise trends and patterns in their control room alarm data. The tool needed to draw from several sources of data, beyond alarm data, to provide a more holistic view for root-cause analysis. Functionalities, such as multiple views of the data for different user needs, scenario planning, and reporting were also required.

The Solution

Harmonic built a visual tool for interactive and dynamic presentations of problematic alarm analytics that collated many different sources of data. The focus was on visually presenting high volume alarms, persistent alarm events, switching event alarms, and repeat incident alarms. It was also designed to include auxiliary data (i.e. weather, earthquake, and site information) to provide a holistic view.

An overview of alarm status and volume is provided alongside the functionality to view the data from different visual perspectives and levels. The tool's features were designed to support users in conducting root-cause analysis, decision making for resolution, scenario planning and report generation to support strategic decision making.

It was important for the tool to be insightful but also actionable. Clear management and accountability were achieved through annotation and assignment functionalities, which supports communication and management of problematic alarms across teams.

Currently under development is the machine learning component which aims to predict alarm anomalies within the network.

The Result

  • Reduction in alarm volumes and associated alarm callouts through the rationalisation of alarm management and data collection process.
  • Improved operational efficiency through the reduction in the cost of effort on “fire-fighting” or resolving incidents.
  • Improved operational efficiency via reporting and transparency across a range of users and teams.
  • Enables proactive asset management which has led to users predicting asset failure through analysis.