Better Asset Knowledge, Better Decisions

The energy transition does not rely solely on building new assets. It also depends on organizations’ ability to better understand, maintain, optimize and extend the service life of infrastructure already in operation. 

This is particularly evident in the hydropower sector. Many generating stations, dams, civil structures, hydro-mechanical equipment and electrical assets were designed several decades ago, under operating conditions very different from those of today. As this infrastructure ages, expectations continue to rise: greater availability, operational flexibility, sustained performance and less tolerance for outages.

In other words, infrastructure is older, yet it is being called upon to play an even more strategic role.

In this context, the issue goes beyond simply deciding whether to replace or rehabilitate an asset. Organizations must now know how to make the right decisions at the right time to maximize asset value while balancing costs, risks, performance and service levels.

This is precisely where asset management maturity becomes essential.

Asset Knowledge: the Foundation of Maturity

In maturity models inspired by the Institute of Asset Management, a mature organization does more than inventory its assets or document their condition. It uses asset knowledge to support decisions aligned with its business objectives and create lasting value throughout the asset life cycle.

Asset knowledge is therefore not an end in itself. It is the foundation for evaluating the various management options: maintain, repair, rehabilitate, replace, extend useful life or accept a controlled risk.

Yet in many organizations, this knowledge exists but is fragmented.

It is found in inspection reports, Excel files, annotated drawings, maintenance histories, partial databases or, too often, in people’s memory. When they leave the organization, some of the asset knowledge leaves with them. This is not merely a loss of know-how. It is a loss of decision-making capacity.

An organization can have a great deal of data without having genuine knowledge.

The difference lies in the structure, quality, consistency and interpretation of the information.

Understanding Asset Behaviour and Aging

Before attempting to predict an asset’s future, organizations must first understand how it behaves and ages.

Two assets of the same age do not necessarily deteriorate at the same rate. Their evolution depends on many factors: operating environment, mechanical loads, thermal cycles, material quality, maintenance history, deterioration mechanisms, previous repairs, changes in use and operating conditions.

In the hydropower sector, this reality is particularly apparent. The behaviour of a turbine-generator unit is not measured in the same way as that of a dam, penstock, low-level outlet gate or spillway. The parameters differ. The deterioration mechanisms differ. The consequences of failure differ.

For certain dynamic assets, vibrations, temperatures, start-up cycles, cavitation or fatigue may be decisive. For civil structures, the focus will instead be on understanding displacement, uplift pressure, piezometry, chemical reactions in concrete, erosion, cracking or the effects of material aging.

This understanding of behaviour makes it possible to move from observing conditions to managing the asset life cycle.

It is not enough to determine whether an asset is in good or poor condition. It is also necessary to understand how its condition affects its function, performance, remaining service life, associated risks and the value it generates.

The Essential Role of an Inspection and Condition Assessment Framework

Organizations often seek to develop advanced dashboards, digital twins, health indices or artificial intelligence models before standardizing their inspections and structuring their data.

Yet the quality of decisions can never exceed the quality of the information on which they are based.

An inspection and condition assessment framework is therefore essential. It transforms field observations into actionable information. It provides a common structure for observing, describing, measuring, rating and monitoring asset condition.

Such a framework should include:

• a clear taxonomy of assets and their components,
• uniform defect terminology,
• objective condition-rating criteria,
• a distinction between material condition, functional behaviour and structural behaviour,
• an understanding of deterioration mechanisms,
• a method for integrating criticality,
• rules for escalating to more detailed inspections or engineering analyses,
• a data structure compatible with digital tools and computerized maintenance management systems (CMMS).

Without a common reference framework, each inspection remains an isolated exercise. A structured framework instead produces knowledge that grows over time.

This knowledge makes it possible to track asset evolution over time, compare facilities, detect trends, prioritize interventions and support investment decisions.

From Data to Decisions

For those responsible for engineering, operations and asset management, the challenge now extends beyond technical considerations to information management and organizational practices.

Data may come from many sources: inspection reports, maintenance histories, CMMS, telemetry, sensors, non-destructive testing, material analyses, modelling, structural studies or field observations. These data do not all have the same frequency, accuracy, reliability or confidence level.

A mature approach gives meaning to this body of information.

Collecting data is not enough; the data must help answer management questions:

• Is the asset still fit for purpose?
• What mechanisms influence its aging?
• Is the assessment’s confidence level sufficient?
• What are the risks if the intervention is deferred?
• Which option maximizes value over the life cycle?
• Should the intervention be treated as OPEX or CAPEX?
• Is the available information sufficient to estimate remaining service life?

This is where knowledge becomes strategic.

When presenting to an executive committee, it is not enough to discuss a crack, vibration, loss of thickness or coating defect. These observations must be translated into impacts on risk, availability, performance, service levels and future investments.

Artificial Intelligence: An Accelerator, Not a Substitute for Judgment

Artificial intelligence will certainly play an important role in asset management.

It can accelerate the analysis of large volumes of historical reports, help structure non-uniform data, support image analysis, detect trends, improve information retrieval and contribute to standardizing observations.

However, it must be used with critical judgment.

In critical infrastructure, decisions carry significant consequences and risks are high. Misinterpretation can lead to major costs, affect safety, reduce service reliability or result in poorly prioritized investments.

Artificial intelligence does not replace asset knowledge. It can amplify that knowledge when data are reliable, structured and interpreted by experts. It can help organizations make better use of available information, but it does not bear responsibility for the decision.

Engineering judgment, an understanding of the operating context and risk management remain essential.

Before discussing algorithms, organizations must first understand asset behaviour.

The Asset Health Index: An Outcome, Not a Starting Point

Only once these foundations are in place does a health index become truly useful.

A health index is not simply a mathematical formula, dashboard or composite score. It is the result of a structured asset knowledge process.

To be credible, it must be supported by:

• consistent inspections,
• reliable condition data,
• an understanding of deterioration mechanisms,
• a criticality assessment,
• knowledge of asset behaviour,
• an explicit confidence level,
• and rigorous engineering judgment.

It must also avoid the pitfalls of overly simplistic averages.

An accumulation of minor defects should not conceal a critical defect. Conversely, a localized defect should not necessarily lead to an alarmist conclusion if its actual impact is controlled. The index should therefore be built by component, with criticality thresholds, escalation rules and a clear understanding of failure modes.

The health index then becomes a bridge between engineering and management. It supports better trade-offs among routine maintenance, rehabilitation, replacement, life extension and risk acceptance. It also connects technical decisions with value, performance and life-cycle considerations.

Its relevance, however, depends on a clear understanding of its components.

A Progressive, Mature Approach

A less mature organization inspects its assets.

A more advanced organization structures the data generated by its inspections.

A high-performing organization integrates this knowledge into its decision-making processes to optimize costs, risks, performance and service levels throughout the asset life cycle.

This progression enables the shift from reactive management to truly sustainable management.

Asset sustainability is not simply about extending useful life for as long as possible. It is about making the right decisions, at the right time, with the right level of information.

Extending an asset’s service life is sound when the decision is based on a clear assessment of its condition, behaviour, risks and the value it generates. Conversely, deferring an intervention without sufficient information can significantly increase risks: unplanned outages, emergency repairs, production losses, safety incidents or environmental impacts.

Technical and environmental sustainability converge here. Rehabilitating at the right time, avoiding premature replacements, reducing emergency interventions and maximizing the performance of existing assets all contribute directly to better use of resources.

In a context where hydropower infrastructure will be called upon to support the energy transition for decades to come, the quality of decisions is becoming as important as the investments themselves. Organizations that can transform asset data into actionable knowledge will be better equipped to prioritize interventions, optimize investments and maximize the long-term value of their infrastructure.

Better knowledge of infrastructure is not merely a matter of technical monitoring. It is the starting point for more informed, resilient and sustainable decisions.