Safe and reliable transportation of products is the backbone of pipeline companies. In order to avoid costly and hazardous product leaks, pipeline companies spend considerable amounts of money to maintain the integrity of their assets.

Ensuring the integrity of assets, such as pipes, pumping units, meters and valves, requires a robust maintenance strategy that minimizes asset and equipment failures. Predictive analytics can help make asset integrity management more reliable and cost-effective.

Traditional approaches

Some people who own cars are so hard-pressed for time that they neglect to maintain them. When something goes wrong, they take their cars to repair shops to get them fixed. However, this process wastes time, energy and money, when many of these repairs could have been avoided with the proper maintenance.

Similarly, companies that manage physical assets, such as pipes, turbines and vessels, have long used a similar corrective maintenance approach to manage and operate their systems. And while they know the risks and costs associated with it, they are often constrained by other higher-priority activities.

Other car owners are aware that it pays to prevent costly repairs before breakdowns occur. They diligently perform prescribed maintenance activities according to the car manufacturer’s schedule, such as changing the oil every few thousand miles. As a result, the chances of an unexpected outage using this preventive maintenance approach are much lower since inspection and service tasks are pre-planned.

However, a set amount of money is still spent for such activities, and the car owner is left with questions, such as, “Will my car run fine if I delay an oil change for another 1,000 miles?” or “If I drove the same number of miles this summer in hotter, dustier conditions than I did last summer, should I take my car in for service sooner?”

A fixed maintenance schedule cannot answer these questions since it does not take into account operating conditions that are key influencers of the performance of an asset.

The predictive approach

With significant technology advances, companies are much better equipped to remotely monitor assets and put in place a more intelligent system that senses the state of various components and predicts the type of maintenance required based on actual operating conditions. Honda’s Maintenance Minder System for cars is one such example. It shows the remaining oil life and assigns a code that helps the owner identify which service activities should take place during the next visit.

This smarter way of taking care of assets is the predictive maintenance approach to managing integrity, and while it still encompasses preventive maintenance activities, it does so in a more focused and cost-effective way.

Predictive maintenance involves the use of continuous or periodic equipment monitoring or prior events to predict the need for maintenance before an unexpected failure actually occurs. This is different from preventive (or planned) maintenance, in which maintenance is conducted on a scheduled basis, and corrective (or reactive) maintenance, in which maintenance is conducted after a failure has occurred.

The advantages

Predictive maintenance significantly helps to lower costs, improve operational availability and optimize frequency:

1. Cost—Predictive maintenance significantly lowers cost in comparison to corrective maintenance, since a catastrophic event could take longer to fix (compared to a preventive maintenance activity), thus resulting in longer interruptions to operations (e.g. a pipeline that is out of service for a long time).

2. Operational availability—Since predictive maintenance is planned in advance, it allows equipment to be serviced when it is idle or when the outage is planned, whereas reactive maintenance may lead to costly equipment downtime while waiting for spare parts or skilled resources to become available.

3. Optimized frequency— Predictive maintenance is typically based on models that take into consideration the current or latest equipment performance, providing a more optimized maintenance frequency. Preventive maintenance, on the other hand, sometimes happens more often than required (resulting in higher costs), and sometimes less often than required (resulting in potentially faster asset degradation).

Predictive techniques

Predictive analytics is not a new concept or field. In fact, predictive techniques date back to the 1600s when insurance companies used historical data to predict risk and use it for underwriting purposes. The concept still holds true today with the fundamental distinctions in techniques more closely related to who performs them.

In the field of equipment maintenance, two fundamental approaches include:

1. Experience-based prediction by individuals—Business subject matter experts (SMEs) have an understanding of past failure patterns and are able to predict potential failures purely based on their experience.

2. Model-based prediction by systems—Analytical models are created that use historical data as input and provide future failure predictions as output. An element of experience-based prediction is present in models since they need to mimic real-world experiences as closely as possible.

The model-based approach

Simply put, a model-based predictive maintenance initiative involves gathering equipment and operating data that would be relevant to the analysis, constructing a statistical or mathematical model (typically some form of regression model) that the data fit into, and using that model to extrapolate into the future—thus making predictions about unknown events. These unknown events may or may not materialize, but actual data related to them will continue feeding the models which can then be further tweaked to help increase the accuracy of future predictions.

Model-based prediction can further be divided into three sub-types when considering the types of data as well as how frequently that data is fed into the model:

1. Failure Event Data—Only past equipment failure (or near-failure) events are captured on a timeline to create a relationship between events and time. A suitable regression model is created based on this relationship and future events are predicted.

2. Operational Data Monitoring—Periodic--Periodic operational data from equipment—such as vibration, temperature, viscosity of commodity flowing in the pipe—is used to create a model that establishes a baseline relationship between operational data and equipment performance. A deviation of the equipment’s baseline performance and actual performance is regressed to predict future equipment failure.

3. Operational Data Monitoring- (Near) Real-Time—Real-time (or near real-time) operational data from equipment is used to create a model that creates a baseline relationship between the operational data and equipment performance. The deviation of the equipment’s baseline performance and actual performance is regressed to predict future equipment failure.

Modeling requirements

Irrespective of the sub-type of predictive modelling being considered, there are a few key requirements for the model to succeed in terms of data, people and technology.

1. Data quantity and quality—Just as more experience typically leads to better decisions, similarly the more data there is, the more accurate a model’s predictions are likely to be. Failure event extrapolation and periodic operational monitoring-based models require at least 15 to 20 valid, historical data points under varying operational conditions to provide a semblance of meaningful predictions. Real-time monitoring-based models do not need a lot of history since they can ingest and use the required number of data points within minutes.

The quality of historical data is of vital importance. Random bias or low precision in historical data will skew predictions. The analysis of past data can help identify potential data capture issues and provide a roadmap to improving the data capture process and other data governance processes in the future.

2. People—Historical failure event extrapolation needs few inputs from business users or technical SMEs. However, periodic and real-time monitoring based predictions need deeper involvement from business users who can guide technology teams on the engineering concepts involved with data point readings in order to interpret their relevance to the prediction.

For a technology implementation team, historical failure event extrapolation needs only a basic knowledge of statistical concepts. However, periodic and real-time monitoring based predictions will require business analysts who are not only adept at mathematics, statistics and information technology, but can also grasp the basics of the engineering concepts involved with equipment operations.

3. Technology assets and equipment, and information—Historical event extrapolation needs a smaller technology footprint when compared to operational data-based models that need sensors to monitor operational parameters and transmit them (in real time or near real time if required) to Supervisory Control and Data Acquisition systems.

Information (i.e., IT) for historical event extrapolation has minimal software requirements (a database and visualization/presentation layer will suffice in most cases). Periodic operational data monitoring based prediction models may be created using similar, minimal software requirements with optional statistical modeling tools depending on the sophistication of the requirements. Near real-time operational data monitoring-based predictions have higher software requirements primarily to deal with the acquisition and storage/management of large volumes of data, along with more sophisticated statistical modeling to deal with potentially one new data point every second.

Conclusions

The pipeline workforce continues to be burdened by administrative tasks, especially sifting through and analyzing data. These impair the ability to accomplish important value-added functions. As the power of analytics continues to be leveraged, it is important to use technology not just to provide people with reports, but to actually perform the burden of analysis and to provide insights and predictions.

Employees will then be able to focus more time and effort on important decisions. The future is even more exciting with the prospects of full automation, such as analytics solutions integrated with maintenance management systems that order replacement parts directly with minimal human intervention. But until that day arrives, pipeline companies can begin taking steps in that direction. Doing so offers firms an opportunity to leverage the benefits of predictive analytics, stay ahead of the competition and make their workplace a much better environment for employees.

Ashish Tyagi is a manager leading analytics engagement for midstream clients and Jay Rajagopal is a director focused on building and executing strategic initiatives for midstream companies with Sapient Global Markets.