Intro

 

The application of AI to predictive maintenance has emerged as a transformative force in the oil and gas industry. Establishing predictions regarding asset health conditions allowing for a proactive approach to managing corrosion and other anomalies. This way, the industry can mitigate wear and tear impacts while increasing efficiency and safety.

The use of AI-powered predictive maintenance can avoid costly unplanned downtime. However, the application of such an innovative solution comes with its uncertainties and challenges. In this blogpost, we will dive into the urgency and the bottlenecks of adopting AI to execute predictive maintenance.

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How does AI apply to predictive maintenance?

 

By continuously monitoring equipment data, predictive algorithms can pinpoint patterns and identify potential issues, allowing maintenance professionals to address problems early on. The integration between the Internet of Things (IoT) and Artificial Intelligence (AI) enables automated monitoring of real-time data. The process consists of using historical data and analytics to establish behavior patterns and forecast when an asset is likely to fail and identify the root cause of the problem.

However, historical data does not equal processed data, and AI-powered predictive maintenance requires historical data to be properly contextualized for the accuracy of analysis. By integrating field data into trained algorithms and connecting them to a digital ecosystem, industries can leverage the power of data-driven insights to detect and prevent damages more efficiently and accurately with budget predictability.

 

Additionally, the adoption of pattern recognition algorithms such as Computer Vision can aid in detecting hidden defects and damaged areas that could be difficult to locate in a visual manner. Besides that, deep learning algorithms are also key to guiding the learning process, which allows the computer to reach accuracy by learning from experience.

Therefore, it’s crucial to understand the specifics of how this technology is managed.

 

Obstacles in adopting AI for predictive maintenance

 

Oil and gas companies have been driving huge operations for decades, which generates a massive amount of untreated historical data. However, to properly forecast errors and schedule maintenance plans, all equipment data must be contextualized to a single channel. For this to happen, AI-powered predictive maintenance requires rethinking on how companies safeguard their assets.

Beyond the urgence of properly treating data, the cost of unplanned downtime is enormous. A November 2021 report from IoT Analytics estimated that the $6.9 billion predictive maintenance market would reach $28.2 billion by 2026. To face this scenario, a deep change in the organization’s culture is needed, which would only come by workforce engagement.

 

Furthermore, successfully adopting AI into predictive maintenance requires a series of efforts from the entire operation. These advanced analytics tools alone will not magically establish maintenance plans. In short, the value of this innovation is only realized when it complements human skills and expertise.

This new approach makes it possible for operators to engage in more fact-based discussions, comparing the real impact of different parameters on business outcomes before making decisions and, in many cases, to consider counterintuitive actions that might improve productivity or profitability.

 

Why is AI so important for predictive maintenance?

 

Maintenance in the oil and gas industry can be conducted through a reactive approach, which means that intervention takes place only when the problem in has already occurred, it’s a ‘’break then fix’’ strategy. For this reason, a preventive approach can be very helpful.

On the other hand, a preventive approach is based on scheduling maintenance at certain time intervals, which may sound as a safer decision but it involves executing maintenance even with equipment in optimal conditions. For instance, not-optimized preventive maintenance can’t predict machinery failures. Thus, it generates the need for an optimized maintenance plan.

This is when AI steps up to the plate, optimizing structure treatment and maintenance schedule. The result is a tool that identifies small problems before they turn into big failures.

According to McKinsey, its application decreases between 30 to 50% of equipment stoppages and increases their lifetime by 20 to 40%.

An AI-driven predictive maintenance not only improves the HSE (Health, Safety, Environment) aspects but it enhances equipment uptime and maintenance schedules. In this sense, Its application schedules maintenance prior to equipment failure, increasing the operations efficiency

Finally, completing the concept of predictive maintenance, there is prescriptive maintenance. This method uses machine learning to adjust operating conditions for desired outcomes, it optimizes possible solutions by providing information on how to delay or entirely eliminate equipment failure.

Unlike human planners, this advanced analytics approach takes into account thousands of variables and constraints to help operators figure out what decision to make in order to maximize profits. By doing so, the benefits of predictive maintenance are made clear: improved uptime, reduced costs, and increased safety.

 

Conclusion

 

Predictive maintenance has proven to be a great opportunity in which oil and gas industries can reach a high level of operational efficiency and improve HSE standards. Despite the bottlenecks involved in its application, AI is the most accurate and efficient tool to enhance maintenance management.

As the demand for predictive maintenance practices intensifies, industries are compelled to recognize the pivotal role AI plays in mitigating losses and revolutionizing maintenance strategies for a more efficient operation and a more sustainable future.

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