Implementing Machine Learning for Predictive Maintenance in Cathodic Protection Systems

Machine learning is integrated into systems across most industries within the United States. In 2024, our country’s ML market reached over $21 billion in valuation due to high demand for automation and “taught” systems.

At Dreiym Engineering, we have seen some ML systems used for everything from fire prevention to risk management. One exciting area of integration is in cathodic protection systems.

Traditional approaches to these safeguarding systems involve a series of manual monitoring and maintenance that should not be overshadowed by machine learning. However, there are some advantages to utilizing both traditional and advanced technologies in ensuring your pipelines, storage tanks, marine structures, and buried utilities avoid corrosive damage.

The Limitations of Traditional Cathodic Protection Maintenance

The typical cathodic protection system (whether galvanic or impressed current) functions by redirecting corrosion away from a metal surface. That can be a highly effective tool as long as it is well-designed and properly maintained.

The problem is that routine inspections performed in the traditional model rely on time-based inspections and reactive service. A company might perform monthly “walkaround” visual inspections or annual close interval surveys (CIS).

While these are effective, there is much that is missed from a system providing real-time monitoring. Imagine how many resources could be saved if emerging degradation patterns or performance drops are noticed in a timely manner. That is where the power of machine learning and IoT (Internet of Things) sensors can make a significant difference.

What is Predictive Maintenance?

The concept of predictive maintenance is straightforward. Instead of reacting to what has already occurred, you develop a cathodic protection system that looks forward to what may happen based on real-time monitoring and analytics.

Machine learning algorithms utilize various tools, including historical performance trends, environmental variables (such as temperature, humidity, water level, and soil resistivity), and sensor data, to then calculate potential outcomes. It’s kind of like having an engineer at the “ready station” who doesn’t need any sleep and operates purely on observed data. That way, if any anomalies or degradation trends are noticed, they’re immediately flagged and sent out to notify proper maintenance or management teams.

How Machine Learning Works in CP Systems

There are several stages of ML integration that must occur before you can rely on such systems for predictive maintenance. For example, ML for cathodic protection must include a digitized infrastructure. IoT monitors need to be installed to capture data points for:

• Pipe-to-soil potential (PSP)
• Current output from rectifiers
• Anode current density
• Reference cell voltages
• Local soil conditions
• Environmental factors (temperature, pH, moisture)

All these sensors feed data to cloud-based or intranet platforms. That is where they are calculated and analyzed for anomaly detection, like a sudden drop in PSP. The ML will develop regression models to predict future values or a “forecast” of where protection should be. It will also classify each sensor or component to determine if it needs inspection or is likely to fail.

In some cutting-edge applications, ML and sensor proliferation can lead to advanced neural networks around your protection infrastructure. That is a significant benefit for larger, more complex systems, such as a cross-country pipeline.

A Real-World Example: Pipeline Integrity Monitoring

The value of on-the-ground observation of systems is not going away. ML will not replace engineers manually inspecting pipelines. What ML does is simplify the process and add another layer of protection.

Consider a 200-mile oil pipeline with an impressed current cathodic protection system. That scope of system would require a lot of teams manually going mile by mile to double-check voltage issues and test stations.

ML with IoT sensors provides real-time monitoring across the entire system. Instead of teams systematically going mile by mile, they can focus on the most “at-risk” areas due to the sensor and analyzed data.

Such integrations save companies time and money. Implementing ML for cathodic protection systems moves a company from a compliance-focused mindset to one of strategic asset optimization. Regulatory concerns are reduced as report generation and up-to-the-minute diagnostics ensure proper documentation. For industries like oil and gas, that translates to significant savings and a more stable risk profile.

Challenges and Considerations in Implementing Machine Learning for CP

The integration of machine learning in modern cathodic protection systems is not without some trial and error. There will be growing pains as your company adjusts to advanced monitoring and sensor maintenance.

For one, ML algorithms require a large volume of high-quality data. Without that information, the model cannot accurately predict future needs or operate effectively. Some legacy companies may have data silos that do not communicate with one another or contain information that has yet to be digitized for historical analysis. The success of the ML is largely based on accurate and reliable sensor information.

For another, ML models must be trained. The capabilities of such systems improve over time. That means there must be a human touch to integration based on expertise and familiarity with the goals of the cathodic system. Even the most powerful ML systems cannot function effectively if there are no relevant examples of normal and abnormal behavior to serve as reference points.

Legacy systems also must allow for ML integration. The cathodic protection already in place may operate independently of other systems for compliance issues. API bridges must be built, or edge computing units with cloud infrastructure may be required to ensure the ML can operate with a given system. That will require some upfront costs that may be prohibitive to certain companies or industries.

Finally, there is a concern over cybersecurity and compliance. Introducing an army of IoT sensors exposes a company to cyberattack risk. It is no secret that some malicious actors will seek out infrastructure-related organizations as potential targets of attack. Whatever ML is introduced, it must involve industrial-grade encryption, multi-layered authentication, and regular vulnerability testing to ensure compliance.

Our Role as Engineering Experts

Yes, implementing machine learning for cathodic protection systems that improve predictive maintenance is a powerful tool in lowering risk and improving financial performance for a company. However, you cannot solely rely on data scientists or IT specialists to implement such systems.

Engineers with long histories of success in this field are required for:

• Testing soil chemistry for its potential effect on corrosion
• Measuring electrical interference and what shielding may be required
• Designing different types of cathodic protection systems
• Ensuring full regulatory compliance and safety checks
• Offering failure mode analysis

You want highly skilled teams like ours at Dreiym Engineering to ensure proper corrosion engineering, cathodic protection design, and forensic electrical engineering insight. That will plug the gap from onboarding new technologies, such as ML and IoT sensor management. Our professional teams can audit and assess your current CP systems for ML readiness, offering advice on sensor placement to ensure the most accurate and effective data analysis.

Moreover, a quality engineering firm can offer a roadmap for implementing predictive maintenance that would include:

• Assessment of current cathodic protection infrastructure
• Launching a pilot program with a high-value asset
• Model validation and adjustment using feedback from teams
• Integration of ML into maintenance schedules and training employees
• Ensuring the system is scalable should coverage need to expand

The more expert guidance you have in the early stages of ML implementation, the better the system will perform over the long term. Working with Dreiym Engineering earlier rather than after the install helps prevent many of the growing pains some companies experience when onboarding ML for cathodic protection.

A Smarter Future for Corrosion Protection

There is no question that corrosion is one of the most expensive and persistent threats to critical infrastructure. Cathodic protection has long served as a primary defense against such threats. Integrating machine learning for advanced predictive maintenance is an excellent way to enhance protection.

While there will be some adjustments to new technologies and a reevaluation of employee tasks, the benefits of risk reduction, cost analysis, and resource allocation cannot be overstated. ML is a valuable tool that complements human oversight and helps ensure pipelines, tanks, and other structures are well protected into the future.