This success story was first published in NCUB’s in showcasing booklet, Artificial Intelligence: the present and future of technology. Read the booklet in full here.

Condition monitoring of industrial machinery and power networks has become increasingly important for companies and utilities.

It helps them to reduce maintenance costs, minimise unplanned downtime and optimise asset performance. Advanced technologies such as interconnected devices, big data analytics and machine learning algorithms are being employed to collect and analyse real-time data from various sensors and measurement tools.

This means collecting data on factors such as temperature, vibration and power, which can then be analysed to identify trends and abnormalities that may indicate potential issues. Current approaches to condition monitoring of industrial machinery typically demand a significant amount of infrastructure and some older systems may not have the capacity to accommodate advanced monitoring technologies. Additionally, manual analysis of the gathered data can be time-consuming and prone to errors, further adding to the drawbacks of traditional methods.

VoltVision is one company that has been at the forefront of this trend. The company developed a modular “plug and play” solution that can be retrofitted on high voltage powered machinery, such as motors in conveyor systems, pumps, compressors and other production equipment. This adds smart and digitally integrated features to existing equipment and infrastructure. The added connectivity of machines is known as the Internet of Things (IoT), which is one of the main pillars of Industry 4.0 – the fourth industrial revolution. Raw power data from hard-to-access high voltage and medium voltage networks extracted in this manner contain critical indicators of an asset’s condition. Applying advanced analytics and machine learning algorithms to these data, VoltVision’s system can then accurately predict equipment failures and prescribe appropriate maintenance actions. This minimises the risk of unplanned downtime and reduces overall maintenance costs.

To further enhance the capabilities of their system, VoltVision partnered with the Department for Business, Energy and Industrial Strategy (BEIS), Science and Technology Facilities Council  (STFC) and a multidisciplinary team at Brunel University London and the Brunel Centre for Artificial Intelligence: Social and Digital Innovation, led by Professor Tatiana Kalganova, to conduct a project aimed at understanding the unique patterns exhibited by faulty industrial motors. Data was collected from a test rig, designed and built at Brunel University London using VoltVision’s innovative V-CUBE technology.

The Brunel AI team then analysed these data in conjunction with available open-source data to identify specific signatures associated with various motor faults. These insights were then used to develop an advanced analytical approach that automates the condition monitoring process. The end-to-end system can detect anomalous behaviour in assets, identifying potential faults and their severity. It can provide continuous feedback to the team to schedule maintenance and prevent faults from worsening and affecting other parts of the machine. Most importantly, the recent findings demonstrate the current approach does not require study of individual motors on their behaviour; instead, the model is generalisable and works well on different motors.

Professor Kalganova said: “Extensive trials of this approach have been conducted at various companies worldwide, gathering positive feedback regarding the significant improvements in operational efficiency and reduction in maintenance costs achieved through its implementation, and highlighting if there is any anomalous behaviour in their systems.

“Leveraging advanced technologies, such as IoT, big data analytics and machine learning algorithms for condition monitoring, has become a crucial aspect for companies and utilities seeking to maximize the performance and longevity of their critical assets”

Strategic partnerships with organisations such as VoltVision can help these businesses enhance their operational efficiency and achieve significant cost savings by streamlining their maintenance schedules. Moreover, the implementation of such solutions can also contribute towards meeting environmental, social and governance (ESG) goals by mitigating unnecessary inefficiencies in power systems.