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Moving towards maintenance 4.0

Water utilities need to embrace smart asset management technologies but that is only part of the solution, writes Chris Steele, Black & Veatch's head of information management and analytics - Europe

Water utilities the world over need to weather a perfect storm of increasing demand, falling revenues and climate change. While building new assets remains part of the solution, enhancing the performance of existing assets is more important than ever before.

The growing focus on asset management as a route to providing a high-quality service for utilities’ customers – and meeting quality and environmental regulatory targets – has been driven to a large extent by the falling cost and increased access to smart sensors and data analytics tools. The rate of change is accelerating as artificial intelligence (AI) and machine-learning software similarly becomes less expensive and more widely available.

Technology alone, however, is insufficient. The most successful smart asset management and maintenance programmes blend human and technological excellence. Dynamic maintenance needs to be grounded in the deep institutional knowledge of an asset base that can only come from the people who design, build and operate it.

The rise of the machines

AI and machine-learning technologies allow water utilities to move beyond the descriptive analytics, which many currently use to understand past incidents and trends, and shift to predictive analytics, which establish what is likely to happen, and prescriptive analytics, which suggest actions on the basis of the predictions.

The internet of things (IoT) is an important enabler for these approaches. The IoT is made up of connected devices – from simple sensors to smartphones. The internet's ubiquity and the availability of cheap sensors make possible ever-increasing cost-efficient gathering of condition and performance data.

Sensors connected via the internet for the purposes of analytics make the visibility of performance cheaper. This gives the flexibility to extend lower-cost performance monitoring into areas where the level of criticality traditionally would not justify more expensive control and protection systems, but where the asset failure would not be without cost implications.

These technological advances are helping facilitate new approaches to the ways assets are managed and operated:

  • Dynamic Preventative Maintenance (DPM) – preventing failures before they occur by using intelligent predictions and dynamic maintenance planning
  • Prognostic Maintenance Interventions (PMI) – using machine learning, pattern recognition and advanced analytics to optimise, manage and deliver interventions

Data comes at a price

The volume of data these technologies make accessible to a water utility is potentially overwhelming. In addition, there is a cost to capturing, storing and accessing each data point. So, when developing DPM and PMI strategies, it is vital utilities define the assets and related data that best supports their goals – and focus on them only.

Failure to achieve this has resulted in data gathering initiatives that cost more than the savings they were expected to yield. This is because of the costs associated with capturing and storing data and – most importantly – maintaining accurate, up-to-date information.

Like physical assets, asset data has a lifecycle. Around 20 per cent of the cost of gathering asset data comes in the capital phase of the asset's lifecycle. The remaining 80 per cent of data costs are generated during the operation and maintenance (O&M) phase of the asset's lifecycle. This is due in part to the length of the capital phase compared to the O&M phase, but mostly because the O&M data is live, evolving, and in need of ongoing monitoring, storage and updating.

Understanding the costs associated with the different phases of the asset data lifecycle – and planning data acquisition accordingly – is the cornerstone of dynamic preventative maintenance. Harvesting data you do not need combined with the risk of using bad data comes, literally, at a price.

After the most significant assets and associated data have been identified, their criticality can be understood. This means focusing on what an asset or process is intended to do and identifying factors that stop it from performing as required. This information is used to inform measures to mitigate the factors degrading asset performance, creating a condition- or output-based maintenance regime at the optimum balance between cost, risk and performance.

This root-cause analysis and failure mitigation will allow water utilities to better understand planned and unplanned costs across comparable processes and, if they differ, understand why. This will give vital insights into the true cost-to-serve.

In reality: Yorkshire Water’s Dynamic Maintenance Planning Programme

Yorkshire Water’s Dynamic Maintenance Planning Programme (DMPP) is one of the first, as well as the largest, programmes of its kind undertaken by a UK water utility. Yorkshire Water serves five million customers in northern England. The DMPP created an effective predictive maintenance regime covering the utility’s entire asset base, encompassing 695 water and wastewater treatment works and 83,000 kilometres of water and sewerage pipes.

Central to the programme’s success was the blending of human and technological capabilities. The Asset Information Standards, which dictate how the assets are recorded and the asset information held, were created with full participation of Yorkshire Water’s O&M teams.

This enabled a collaborative DPM study, producing a condition-based maintenance programme based on failure modes, with O&M buy-in. This approach meant time and money are focused on ensuring process and asset outputs are maintained.

Innovative use of mobile technology also yielded benefits: iPads with Bluebeam enabled live asset survey findings, and piping and instrumentation diagram updates, to be uploaded to a dynamic asset database. O&M teams in the field are using mobile devices to access the condition-based maintenance programme that guides their activities, and to record and upload condition reports, in real-time. Initial indications are a circa 30 per cent decrease in reactive O&M work.

Towards maintenance 4.0

DPM and PMI programmes mark a significant step towards maintenance 4.0, the fourth industrial revolution, the shift towards cyber-physical systems. As they seek to weather the perfect storm, water utilities need to embrace this change. In doing so, it is important that technology is seen only as part of the solution. 

To deliver the smartest possible maintenance solutions, O&M teams will need to trust in AI driven programmes. For this to work, the AI platform needs to be founded on the deep institutional knowledge of water utility design, construction and O&M experts.

Topic: Asset Management , Data, IT & Communications , Innovation
Tags: Black & Veatch , asset management , maintenance , technology , artificial intelligence , machine learning

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