Policy approach(es) used to catalyse investment: Development of a national, regional, or sectoral InfraTech strategy
Finance approach(es) used to catalyse investment: De-risking mechanisms or blended finance
Predictive maintenance utilises monitoring and advanced machine learning methods to develop predictive models about failure of physical and mechanical assets such as pipes, pumps, and motors. These aim to prevent failure and optimise maintenance of critical infrastructure by providing early warning and predictive actions to issues before they occur. Key components include sensors that are installed in the machines, a communication system that allows data to be transmitted in real-time between sensors and a centralised data platform, and machine-learning predictive analytics to identify patterns and generate actionable insights. Predictive maintenance tools enable asset management workforces to automatically diagnose problems of industrial assets’ breakdowns and inefficiencies and optimise maintenance scheduling ahead of asset failure as well as extend the life of the asset.
Traditional asset maintenance activities are beset by limited visibility of asset condition, infrequent monitoring, labour-intensive periodic maintenance, and manual data analysis processes. This leads to a slow response to asset deterioration, driving productivity losses, and unoptimised infrastructure capital and operational expenditure. Development of more sensitive and intelligent monitoring and modelling technologies have created opportunities to minimise labour needs and plan investments better.
Mechanical asset owners face inherent challenges with aging infrastructure and assets reaching their end of life. For example, a 2018 survey showed that USD 472.6 billion will be required over the next 20 years, to maintain and improve drinking water infrastructure in the USA. The majority of this (USD $312.6 billion) is for the replacement or refurbishment of ageing or deteriorating distribution assets. The increasing need for water asset maintenance and renewal optimisation is evident in the USD $90 billion increase in 20-year investments required to repair, replace and renew existing infrastructure while there is a $30 billion decrease in investment requirements for new infrastructure .
Knowing which asset to maintain, renew or refurbish will potentially defer substantial amounts of capital expenditure. Increased usage of predictive failure models will be an essential planning tool to shift towards more proactive maintenance and optimise maintenance budgets. Proactive programs will prevent catastrophic failure of water distribution networks, pipe bursts and leaks causing damage to property and public infrastructure. This will assist water utilities to maintain critical water services to communities, eliminate unplanned downtime, reduce maintenance costs, improve asset reliability, and enhance operational efficiency.
As more water utilities start to embrace digitalisation and generate large amounts of data, the technology can access better quality and quantity of data from various inputs to train the models and improve the precision of failures detected. This technology can also be further developed and applied to other sectors and scenarios such as with critical physical infrastructure such as energy generation, transport, and manufacturing. As infrastructure continues to age and renewal investment needs continue to grow, demand for more accurate and robust failure prediction models will grow and be more widely used in all industries. Predictive models from different industries can be combined to optimise maintenance of assets in close proximity i.e. pipes, electricity, communications, gas, roads, etc.
Improving efficiency and reducing costs:
Enhancing economic, social and environmental value:
Legislation and regulation: Governments can develop strategies to drive operators to invest in more efficient and sustainable operations of critical assets. Regulatory driven asset management plans can be implemented to maintain the efficiency of water infrastructure.
Funding and financing: Greater focus on committing funding to optimise and extend the life of existing assets rather than building new infrastructure is needed.
Transition of workforce capabilities: Training and upskilling workforce to have the skills to effectively interpret and action the insights from AI technologies.
Risk: Machine learning can only operate using good quality input data. There is a risk where incorrect data or lack of data can limit functionality or lead to incorrect actions, which can increase project costs and lead to poor infrastructure planning and investment.
Mitigation: Investing in sensors and monitoring solutions before investing in machine learning software.
Risk: The shift from scheduled and reactive maintenance to predictive and proactive maintenance can create the need for re-training of workers to interpret and appropriately action results from predictive models.
Mitigation: Industry can assist through training and up-skilling programs to help mitigate these issues.
Safety and (Cyber)security risk
Risk: Control systems, especially in those located in the cloud, are at risk of cyber-attacks. Sensitive information about location and condition of critical infrastructure and potential attacks can have high risks on public health.
Mitigation: Organisations need to ensure a strong level of cyber security in their networks and data storage, for both local servers and cloud services. Focus should be on having strict data ownership models and the appropriate level of data security as needed by the application. Any implementation of data transfer and storage should be undertaken by suitable qualified and experienced professionals.
Implementation: Sydney Water and Data61 are collaboratively researching advanced analytics approaches to solving water industry challenges, including water pipe failure prediction, predicting sewer chokes and prioritising active leakage detection areas.
Cost: Sydney Water found the potential to reduce maintenance and renewal costs by several million dollars over a four-year period and minimise inconvenience to customers from pipe breaks.
Timeframe: Projects are undertaken on case-by-case basis and can be completed within a few months.
Implementation: Voda AI software have assessed more than 1200 pipes for a Florida water utility, prioritising pipe monitoring, maintenance, and replacements.
Cost: Voda predicted 18 avoidable breaks saving the water utility more than $100,000 in reactive maintenance and preventing negative coverage of bursts.
Timeframe: Projects are undertaken on case-by-case basis and can be completed within a 12 months.
Implementation: The University of Queensland have installed the FitMachine on 22 chiller units, delivering 24/7 air-conditioning, on campus since March 2016 to detect the early warnings of failures, using machine-learning algorithms4.
Cost: The University of Queensland realised 135% return on their FitMachine investment. They saved up to $100,000 in repair costs by discovering and preventing machine failure ahead of time
Timeframe: Movus solution was implemented in a short time frame (within 6 months).