What is asset integrity management?

Modern AIM programs use established processes and cutting-edge technologies like preventive and predictive maintenance, artificial intelligence (AI) and the Internet of Things (IoT) to prevent assets from failing unexpectedly and causing downtime.

AIM programs help preserve asset functionality, safety and fitness-for-service (FFS) through proven maintenance strategies and regular risk-based inspection (RBI).

According to a recent report, the global market for AIM products and solutions was valued at USD 25 billion last year. Also, it is expected to grow at a compound annual growth rate (CAGR) of almost 6% over the next 5 years.¹

Asset integrity management (AIM) helps organizations achieve several critical objectives that directly impact operations, safety and profitability:

• Prevent equipment failure: Sudden and unexpected asset failures can result in costly shutdowns and the interruption of core business practices.

• Improve asset performance: A strong approach to AIM helps ensure that assets perform at peak levels throughout their lifecycles with advanced integrity management programs.

• Ensure compliance: Effective integrity management solutions automate compliance with both national and international regulations, improving worker safety and helping organizations avoid costly fines.

• Support sustainability initiatives: AIM programs can help enterprises reach sustainability goals by reducing waste and improving energy usage.

• Enhance real-time decision-making: Modern AIM systems help managers track asset performance and wear and tear in real time and identify problems before they result in shutdowns.

Today’s most effective asset integrity management (AIM) programs operate on a repeatable, six-stage cycle designed to ensure that assets remain operational, reliable and high-performing throughout their lifecycle.

Here’s a close look at each stage.

The first stage in developing a successful AIM program is to set clear policies and standards for how an organization will manage its physical assets. During this stage, stakeholders must ensure that their proposed asset integrity management program (IMP) is going to meet industry standards for risk and reliability.

For example, API 580 and ISO 55000 are two of the most widely known international standards for assessing risk and the reliability of asset management programs. Created during this stage, a clearly defined framework needs to identify all relevant standards a proposed AIM program is going to meet and how it intends to meet them.

Risk-based inspection (RBI), the practice of inspecting assets and physical systems based on knowledge of their risk of failure, is a core element of most modern AIM programs. RBI helps maintenance teams prioritize which assets and parts are inspected first based on the probability of their failure and the resulting consequences for the core business processes of the organization.

Recently, AI tools have been integrated into RBI practices, helping teams evaluate maintenance factors like corrosion rates, environmental conditions and historical performance more swiftly and accurately. This automated risk assessment approach helps improve the effectiveness of maintenance practices and ensure that AIM programs are successful.  

Condition monitoring (CM) is a predictive maintenance practice that uses real-time data collection and analysis to detect potential problems and monitor asset performance and health. In modern AIM programs, CM is used to identify opportunities for maintenance and repair before an asset’s mechanical integrity is compromised.

Thanks to advances in IoT and edge computing technology, modern CM systems can now analyze a continuous stream of data from sensors and instruments. Non-destructive testing (NDT), a way of using ultrasound and X-ray technology to inspect asset integrity without causing damage, is critical during this stage. Modern digital solutions like cloud-based analytics tools can now perform NDT in real-time, helping maintenance managers make important decisions faster.  

Data management, the fourth stage of modern AIM programs, collects and analyzes data generated by IoT sensors affixed to assets. The data management stage of AIM is critical to many organizations’ digital transformation programs—efforts to leverage the latest digital tools and technologies across every business function.

Modern data management practices relied upon during this stage include:

• Predictive analytics to forecast equipment failures

• Machine learning (ML) algorithms to help improve data accuracy over time

• Digital twin technology to simulate asset performance under different conditions

• Intelligent automation to improve data collection and analysis

By stage five, facilities managers use data from the RBI and condition monitoring stages to implement the maintenance strategies that form the core of their AIM program. This stage of AIM typically develops a maintenance schedule and a detailed plan for how to execute it.

During this stage, asset managers try to streamline maintenance planning and execution using advanced systems, technologies and tools that automate repetitive tasks and increase diagnostic accuracy.

Continuous improvement, or auditing, is the final stage in AIM. Maintenance managers and technicians establish a series of regular audits and performance reviews to ensure that their program is successful. This stage is critical for the continual optimization of practices, systems and procedures that are critical to maintaining asset health, performance and regulatory compliance.  

AI has become a critical tool during this stage, helping organizations streamline manual auditing practices with AI-powered automation and predictive analytics.

Effective asset integrity management (AIM) helps enterprises of all sizes and across a wide range of industries implement comprehensive standards to maintain their most valuable assets. Here are some of the top benefits of a strong AIM program at the enterprise level:

• Increased worker safety: Strong AIM programs help protect workers from dangerous equipment breakdowns and failures. By establishing fitness-for-service standards for every operational asset and helping managers create rigorous process safety programs, AIM helps make workplaces safer.

• Stronger regulatory compliance: AIM helps organizations ensure that they comply with the requirements set by national and international regulators like the American Petroleum Institute (API) and the Occupational Safety and Health Administration (OSHA). Modern AIM tools leverage AI to automate aspects of regulatory compliance to reduce inefficiencies and the likelihood of human error. According to a recent report, AI can cut the cost of compliance by up to 40% and shrink audit preparation times by 80%.²

• Asset life extension: Effective AIM programs help extend asset lifecycles and prevent asset decommissioning for as long as possible. Through automated condition monitoring and predictive and preventive maintenance programs, AIM helps asset managers ensure that assets perform at peak levels throughout their lifecycle and don’t have costly breakdowns.

• Reduced downtime: Assets that organizations rely on for their core business practices need to experience as little downtime as possible. A strong AIM approach that incorporates predictive maintenance and real-time data analysis helps managers repair or replace components before they break and cause a shutdown.

• Stronger operational efficiency: By integrating AIM practices with modern digital solutions like IoT sensors and intelligent automation systems, organizations can increase real-time visibility into asset health. This approach helps managers and asset operators optimize maintenance schedules, respond faster to changes in asset condition and increase asset uptime.

Modern asset integrity management (AIM) programs help industries maintain their most valuable assets safely and reliably. From helping oil companies minimize risk in challenging physical environments, to ensuring medical devices are safe and reliable for patients and operators in a hospital, here are four popular use cases for AIM at the enterprise level.

AIM originated in the oil and gas industry as a means of protecting workers and equipment from harsh conditions, and it is still used extensively to this day. Today, AIM in the oil and gas industry spans both onshore and offshore platforms, pipelines, refineries and other facilities, helping ensure that these complex assets perform at peak levels and remain operational for as long as possible.

RBI, NDT and real-time condition monitoring are some of the most widely used AIM practices in the oil and gas industry. Technological advancements in AI systems and IoT sensors help equipment managers automate manual tasks and shift from reactive maintenance to more proactive, predictive tactics.

In the healthcare industry, AIM helps ensure complex medical devices and equipment are safe, reliable and compliant throughout their lifecycles. Assets in hospitals and other medical facilities directly affect patient safety, so AIM programs in the healthcare industry are some of the most rigorous and heavily regulated.

These are some examples of how AIM is used in healthcare:

• Maintaining imaging equipment like X-rays and MRIs and ensuring they remain accurate.

• Maintaining the physical integrity of vital infrastructure like emergency rooms (ERs) and maternity wards.

• Verifying the accuracy and reliability of sterilization equipment and procedures.

AIM programs help the renewable energy industry manage complex infrastructure like wind turbines and solar farms, ensuring they remain safe, scalable and efficient.

AIM practices like NDT, RBI and predictive maintenance are used widely, helping asset managers prevent equipment breakdowns and comply with rigorous environmental regulations.

In modern, renewable power plants, AIM programs help maintain the mechanical integrity of boilers, heat exchangers and other types of business-critical equipment. Real-time condition monitoring detects anomalies in asset performance. Predictive analytics helps identify new operational efficiencies and avoid costly shutdowns

In the mining industry, AIM practices help ensure the integrity of complex mechanical systems that must endure harsh environments like high altitude and extreme heat and cold. Through continuous, real-time condition monitoring, AIM helps asset managers detect early signs of wear and tear and establish effective maintenance programs to counter degradation.

The mining industry is constantly integrating AIM with modern supply chain optimization practices and cutting-edge automation systems to reduce costs and extend asset lifecycles. Modern AIM practices in mining help extend asset lifecycles, improve sustainability and ensure safer conditions for workers.

Asset integrity management (AIM) practices have been heavily influenced by recent developments in AI, ML and IoT technologies that have led to breakthroughs in automation and real-time data analytics. New capabilities are transforming how asset managers approach risk and strategic decision-making in AIM.

Here’s a look at three trends shaping the future of AIM at the enterprise level.

Predictive analytics in AIM—using trends and patterns identified from asset data to create a maintenance strategy—has been transformed by recent AI and ML capabilities. AI and ML systems can now analyze real-time asset data to help maintenance managers pinpoint how, when and why a system or part is likely to fail.

As these technologies continue to improve, they are likely to influence further developments in AIM, from increased automation of manual processes to the way supply chains and spare parts inventories are managed.

IoT sensors—internet-connected devices that record data from an asset’s physical environment—are making AIM processes more data-driven and autonomous.

Here are some examples:

• Embedded microprocessors in IoT sensors can perform analytics and diagnostics at the sensor level rather than requiring data to be sent to a data center first.

• Edge AI, the deployment of AI algorithms at the device level instead of in a data center, is enabling IoT sensors to perform complex AI and ML inference on data.  

• Newer IoT sensors can switch between sensing modalities like vibration, temperature and fluid level to make measuring and analyzing data faster and more efficient.

Deeper integration of multicloud environments into AIM programs helps unlock new capabilities and transform some AIM ecosystems. Rather than managing assets onsite, asset managers relying on cloud-based AIM can perform CM and asset optimization from a single, centralized hub.

Deeper cloud-based integration is also bringing digital twin technology to the forefront of AIM programs. Digital twins are digital replicas of physical assets that allow them to be tested against a wide range of conditions so managers can see how their physical counterparts are likely to perform.

Digital twins enable the continuous simulation and analysis of asset performance throughout its lifecycle, optimizing maintenance approaches and performance.