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Asset managers constitute a key role in managing assets on behalf of individuals, businesses, or nonprofit organizations. Making investments, liquidating assets, and tracking performance are generally part of this process.
FREMONT, CA: Every day, utility asset managers have to predict the future performances of their assets based on past performance and current conditions. The scrutiny some asset managers are under from regulators is evolving, along with the changing landscape of distributed energy resources and climate change. Utility companies are becoming high-tech data companies, and asset managers are being challenged with making the right decisions based on the data they collect.
The ability to make informed, data-driven maintenance and investment decisions are critical to ensuring utilities continue to provide optimal service despite multiple challenges and evolving regulatory requirements.
Utilities must consider the risk, cost, and performance of their asset management systems to make these decisions and get ahead of regulators. Any organization operating an important quantity of assets must constantly adjust the delicate balance between these three factors through an asset management system. Monitoring an asset's potential failure risks and criticality based on internal or external factors, such as its age, vegetation proximity, and other factors, can help determine whether intervention is necessary.
It is, therefore, necessary to identify and manage asset failure risks efficiently in order to improve the quality of service, compliance, and financial performance. Asset managers have different objectives and data, but following these five steps can help assess the level of data they have and manage it effectively.
Adapting the asset failure risk assessment methodology to the organizational context: Asset failure modes are numerous, as are their consequences. An exhaustive study of all risks would take a great deal of time and effort. A simplified and proven method should be chosen, taking into account all stakeholders' major concerns and the major risks of failure.
Asset management is a journey, and utilities usually begin by adopting a platform and methodology that will generate value within a few weeks or months. The risk modeling can be improved as more data becomes available.
The creation of an asset registry - a list of assets with relevant information: Failure risk studies cannot be conducted without an asset data register. Data used to calculate failure risk due to the aging of an asset include the asset's age, its direct environment's aggressiveness, its duty rate, and its failure history. Satellites or LiDAR data can be used to estimate failure risk due to vegetation. By using post-processing and computer vision techniques, the risk caused by vegetation can be quantified for easy decision-making. This data must be computerized and stored in an accessible database to be useful on a large scale.
Determining the probability of failure (PoF) for each asset category based on its real condition, using specific criteria for each asset category, is necessary: Based on historical asset failure data and predictive asset failure models, an estimated probability of failure can be calculated. Utilities can use aging models for each asset category when assessing asset failure risk due to aging. Asset information is used as input to apply this method, and the aging model of the asset category is used to calculate the annual probability of failure.
Calculating the consequence of failure (CoF) for each asset in terms of service reliability, safety, environment, and direct costs: There are several consequences associated with each asset failure. Depending on what is affected, some may be insignificant, while others may be disastrous. Asset managers, as well as all stakeholders, are able to see asset failure from a new perspective when they can monetize the consequences of failure.
To extract greater value from infrastructure assets, developing a risk-based investment and maintenance strategy is crucial: It all comes down to numbers at this point. With this strategy in place, asset managers can analyze data to make well-informed decisions that result in optimal outcomes for utilities. There are some use cases that are more long-term in nature, such as planning for energy regulators or optimizing maintenance strategies for a particular asset class.
