The rollout of 5G combined with edge computing in remote locations will change the way maintenance is carried out in the field. Historically, service teams performed maintenance either in a reactive fashion – fixing equipment when it broke – or using a preventative calendar-based approach. Neither of these methods is satisfactory, with the former being too late and resulting in failure while the latter is necessarily too early, resulting in excessive expenditure and downtime. The availability of connected sensors has allowed service teams to shift to condition monitoring without the need for taking equipment offline for inspections. The advent of analytics takes this approach further and has given us optimised scheduling in the form of predictive maintenance.
The next step is prescriptive maintenance in which AI can recommend action based on current and predicted condition according to expected usage or environmental circumstances. This could be as simple as alerting an operator to automatically ordering parts and scheduling multiple servicing tasks depending on forecasted production needs in the short term.
Prescriptive maintenance has only become possible with the advancement of AI and digital twin technology, but imminent improvements in connectivity and computing will take servicing to a new level. The rollout of 5G will give a boost to bandwidth, reduce latency, and increase the number of connections possible. Equipment in remote locations – such as transmission lines or machinery in resource industries – will benefit from the higher throughput of 5G connectivity, either as part of an operator’s network rollout or a private on-site deployment. Mobile machinery, particularly vehicles, which can include hundreds of sensors will no longer be required to wait until arrival before the condition can be assessed. Furthermore, vehicles equipped with external sensors can inspect stationary infrastructure as it passes by.
Edge computing – either carried out by miniature onboard devices or at smaller scale data centres embedded in 5G networks – ensure that intensive processing can be carried out closer to equipment than with a typical cloud environment. Bandwidth hungry applications, such as video and time series analysis, can be conducted with only meta data transmitted immediately and full archives uploaded with less urgency.
Prescriptive Maintenance with 5G and the Edge – Use Cases
- Transportation. Bridges built over railway lines equipped with high-speed cameras can monitor passing trains to inspect for damage. Data-intensive video analysis can be conducted on local devices for a rapid response while selected raw data can be uploaded to the cloud over 5G to improve inference models.
- Mining. Private 5G networks built-in remote sites can provide connectivity between fixed equipment, vehicles, drones, robotic dogs, workers, and remote operations centres. Autonomous haulage trucks can be monitored remotely and in the event of a breakdown, other vehicles can be automatically redirected to prevent dumping queues.
- Utilities. Emergency maintenance needs can be prioritised before extreme weather events based on meteorological forecasts and their impact on ageing parts. Machine learning can be used to understand location-specific effects of, for example, salt content in off-shore wind turbine cables. Early detection of turbine rotor cracks can recommend shutdown during high-load periods.
Data as an Asset
Effective prescriptive maintenance only becomes possible after the accumulation and integration of multiple data sources over an extended period. Inference models should understand both normal and abnormal equipment performance in various conditions, such as extreme weather, during incorrect operation, or when adjacent parts are degraded. For many smaller organisations or those deploying new equipment, the necessary volume of data will not be available without the assistance of equipment manufacturers. Moreover, even manufacturers will not have sufficient data on interaction with complementary equipment. This provides an opportunity for large operators to sell their own inference models as a new revenue stream. For example, an electrical grid operator in North America can partner with a similar, but smaller organisation in Europe to provide operational data and maintenance recommendations. Similarly, telecom providers, regional transportation providers, logistics companies, and smart cities will find industry players in other geographies that they do not naturally compete with.
Recommendations
- Employing multiple sensors. Baseline conditions and failure signatures are improved using machine learning based on feeds from multiple sensors, such as those that monitor vibration, sound, temperature, pressure, and humidity. The use of multiple sensors makes it possible to not only identify potential failure but also the reason for it and can therefore more accurately prescribe a solution to prevent an outage.
- Data assessment and integration. Prescriptive maintenance is most effective when multiple data sources are unified as inputs. Identify the location of these sources, such as ERP systems, time series on site, environmental data provided externally, or even in emails or on paper. A data fabric should be considered to ensure insights can be extracted from data no matter the environment it resides in.
- Automated action. Reduce the potential for human error or delay by automatically generating alerts and work orders for resource managers and service staff in the event of anomaly detection. Criticality measures should be adopted to help prioritise maintenance tasks and reduce alert noise.
In the midst of the current global crisis, the Utilities industry has had to continue to provide essential public services – through supply chain disruption, reduction of demand in the commercial sector, demand spikes in the consumer sector, change in peak profiles, remote staff management, cyber-attacks and so on. Robust business continuity planning and technology adoption are key to the continued success of Utilities companies. The Ecosystm Business Pulse Study which aims to find how organisations are adapting to the New Normal finds that 6 out of 10 Utilities companies are accelerating or refocusing the Digital Transformation initiatives after the COVID-19 outbreak, underpinning the industry’s need for technology adoption to remain competitive.
Drivers of Transformation in the Utilities Industry
The Evolving Energy Industry. As consumers become more energy-conscious, many are making changes in their usage pattern to stay off the grid as much as possible, potentially reducing the customer base of Utilities companies. This increases their reliance on renewable energy sources (such as solar panels and wind turbines) and batteries, forcing Power companies to diversify and leverage other energy sources such as biomass, hydropower, solar, wind, and geothermal. The challenge is further heightened by the fast depletion of fossil fuels – it is estimated that the world will have run out of fossil fuels in 60 years. The industry is also mandated by government regulations and cleaner energy pacts that focus on climate change and carbon emission – there are strict mandates around how Utilities companies produce, deliver and consume energy.
Business Continuity & Disaster Management. Perhaps no other industry is as vulnerable to natural disasters as Utilities. One of the reasons why the industry has been better prepared to handle the current crisis is because their usual business requires them to have a strong focus on business continuity through natural disasters. This includes having real-time resource management systems and processes to evaluate the requirement of resources, as well as a plan for resource-sharing. There is also the danger of cyber-attacks which has been compounded recently by employees who have access to critical systems such as production and grid networks, working from home. The industry needs to focus on a multi-layered security approach, securing connections, proactively detecting threats and anomalies, and having a clearly-defined incident response process.
The Need to Upgrade Infrastructure. This has been an ongoing challenge for the industry – deciding when to upgrade ageing infrastructure to make production more efficient and to reduce the burden of ongoing maintenance costs. The industry has been one of the early adopters of IoT in its Smart Grid and Smart Meter adoption. With the availability of technology and advanced engineering products, the industry also views upgrading the infrastructure as a means to mitigate some of its other challenges such as the need to provide better customer service and business continuity planning. For example, distributed energy generation systems using ‘micro grids’ have the potential to reduce the impact of storms and other natural disasters – they can also improve efficiency and quality of service because the distance electricity travels is reduced, reducing the loss of resources.
The Evolving Consumer Profile. As the market evolves and the number of Energy retailers increases, the industry has had to focus more on their consumers. Consumers have become more demanding in the service that they expect from their Utilities provider. They are increasingly focused on energy efficiency and reduction of energy consumption. They also expect more transparency in the service they get – be it in the bills they receive or the information they need on outages and disruptions. The industry has traditionally been focused on maintaining supply, but now there is a need to evaluate their consumer base, to evolve their offerings and even personalise them to suit consumer needs.
The global Ecosystm AI study reveals the top priorities for Utilities companies, that are focused on adopting emerging technologies (Figure 1). It is noticeably clear that the key areas of focus are cost optimisation (including automating production processes), infrastructure management and disaster management (including prevention). 
Technology as an Enabler of Utilities Sector transformation
Utilities companies have been leveraging technology and adopting new business models for cost optimisation, employee management and improved customer experience. Here are some instances of how technology is transforming the industry:
Interconnected Systems and Operations using IoT
Utilities providers have realised that an intelligent, interconnected system can deliver both efficiency and customer-centricity. As mentioned earlier, the industry has been one of the early adopters of IoT both for better distribution management (Smart Grids) and for consumer services (Smart Meters). This has also given the organisations access to enormous data on consumer and usage patterns that can be used to make resource allocation more efficient.
For instance, the US Government’s Smart Grid Investment Grant (SGIG) program aims to modernise legacy systems through the installation of advanced meters supporting two-way communication, identification of demand through smart appliances and equipment in homes and factories, and exchange of energy usage information through smart communication systems.
IoT is also being used for predictive maintenance and in enhancing employee safety. Smart sensors can monitor parameters such as vibrations, temperature and moisture, and detect abnormal behaviours in equipment – helping field workers to make maintenance decisions in real-time, enhancing their safety.
GIS is being used to get spatial data and map project distribution plans for water, sewage, and electricity. For instance, India’s Restructured Accelerated Power Development & Reforms Program (R-APDRP) government project involves mapping of project areas through GIS for identification of energy distribution assets including transformers and feeders with actual locations of high tension and low tension wires to provide data and maintain energy distribution over a geographical region. R-APDRP is also focused on reducing power loss.
Transparency and Efficiency using Blockchain
Blockchain-based systems are helping the Utilities industry in centralising consumer data, enabling information sharing across key departments and offering more transparent services to consumers.
Energy and Utilities companies are also using the technology to redistribute power from a central location and form smart contracts on Blockchain for decisions and data storage. This is opening opportunities for the industry to trade on energy, and create contracts based on their demand and supply. US-based Brooklyn Microgrid, for example, is a local energy marketplace in New York City based on Blockchain for solar panel owners to trade excess energy generated to commercial and domestic consumers. In an initiative launched by Singapore’s leading Power company, SP Group, companies can purchase Renewable Energy Certificates (RECs) through a Blockchain-powered trading platform, from renewable producers in a transparent, centralised and inexpensive way.
Blockchain is also being used to give consumers the transparency they demand. Spanish renewable energy firm Acciona Energía allows its consumers to track the origin of electricity from its wind and solar farms in real-time providing full transparency to certify renewable energy origin.
Intelligence in Products and Services using AI
Utilities companies are using AI & Automation to both transform customer experience and automate backend processes. Smart Meters, in itself, generate a lot of data which can be used for intelligence based on demographics, usage patterns, demand and supply. This is used for load forecasting and balancing supply and demand for yield optimisation. It is also being leveraged for targeted marketing including personalised messages on Smart Energy usage.
Researchers in Germany have developed a machine learning program called EWeLiNE which is helping grid operators with a program that can calculate renewable energy generation over 48 hours from the data taken from solar panels and wind turbines, through an early warning system.
Niche providers of Smart Energy products have been working with providing energy intelligence to consumers. UK start-up Verv, as an example, uses an AI-based assistant to guide consumers on energy management by tracing the energy usage data from appliances through meters and assisting in reducing costs. Increasingly, Utilities companies will partner with such niche providers to offer similar services to their customers.
Utilities companies have started using chatbots and conversational AI to improve customer experience. For instance, Exelon in the US is using a chatbot to answer common customer queries on power outages and billing.
While the predominant technology focus of Utilities companies is still on cost optimisation, infrastructure management and disaster management, the industry is fast realising the power of having an interconnected system that can transform the entire value chain.