Getting ahead of a problem

While no technology can prevent normal equipment wear or the need for maintenance, Steve Tonissen, vice president, SmartSignal, believes Predictive Analytics and the latest advances in Predictive Diagnostics can detect impending problems early and allow plants to take control of their operations

Predictive Analytics provides early and actionable real-time warnings of impending equipment and process problems. These warnings enable operators to fix what needs to be fixed but not fix what doesn't. They allow plants to move from reactive and time-based maintenance to proactive and predictive maintenance. Plants, therefore, improve their availability and reliability, increase efficiency, and reduce maintenance costs.

Predictive Analytics works by understanding the essential element—that every piece of equipment is unique. It develops a set of fingerprints for each individual piece of equipment across all known loads, ambient conditions, and operating contexts. It calculates the proper operational relationships among all relevant parameters, such as loads, temperatures, pressures, vibration readings, ambient conditions, and more. It then takes actual real-time sensor readings and compares them to that particular machine's normal fingerprints. Based upon the differences between real time and normal, along with their persistence, Predictive Analytics detects and isolates abnormal behaviour, in the context of operating conditions. It then posts these 'incidents' and provides exception-based notifications of developing problems to users. It does this automatically, continuously, and relentlessly, 24h/day.

Said differently, Predictive Analytics can determine that, even though a temperature reading is in the middle of the minimum/maximum range, the sensor value is abnormal for a particular piece of equipment in the context of its individual operating conditions.

Instead of plant personnel sorting through vast amounts of data to extract meaningful nuggets, Predictive Analytics operates on a real-time model, identifying and flagging these subtle changes from expected behaviour that have been verified to be actionable issues. Doing so, it identifies sensor, equipment, and operational issues— and sometimes can identify issues weeks and months before failure.With these early warnings, operators can schedule appropriate maintenance or plan further investigation in context of the overall plant schedule. Hence, they avoid surprise equipment failures.

Predictive Analytic technology is scalable to all critical rotating, non-rotating, and process equipment, across the plant, across the fleet, and across industries. It currently is being used in power generation - coal, combined cycle, nuclear, wind, hydro - and in oil and gas - upstream, gas transportation, and downstream. About 50% of the US Power Gen fleet is using it, along with some leading oil and gas super majors, and its use is expanding globally.

Moving up the P-F curve Perhaps the easiest way of thinking about the advantages of Predictive Analytics is to review it in context of the P-F curve.

Reliability engineers use a P-F curve to visualise the activities of managing maintenance and repair activities against the cost of equipment failure. Key points on the curve represent Potential Failure (P) and Functional Failure (F). Potential Failure occurs when events lead to component damage that needs repair. Functional Failure occurs when equipment performance no longer meets design conditions and must be shut down for repair.

Before Predictive Analytics, with some traditional condition-monitoring tools, this could be a short time envelope, as indicated in Figure 2 (below).

Given the customised equipment models that automatically adapt to changes in load, ambient conditions, and operating contexts, though, Predictive Analytics provides an accurate assessment of the condition of each individual piece of equipment and, therefore, early warning of developing issues.

Quite simply, Predictive Analytics enables operators to move 'up the curve', providing extended lead time and enabling operators to fix small problems before they grow large or catastrophic. See Figure 3 (above).

Predictive diagnostics Predictive Diagnostics builds on the powerful foundation of Predictive Analytics, as described above. But, whereas Predictive Analytics tells you what is going to fail, Predictive Diagnostics goes further and also tells you what is the apparent cause of the failure and what is the priority of the impending failure.

Predictive Diagnostics was made possible by the collection and analysis of data from hundreds of millions of machine hours and tens of thousands of incidents across equipment types from the world's largest base of equipment operating data. This in-depth analysis of 10 years' data resulted in the identification of fault patterns in context of operating behaviour. From here, with user input, a new technology was developed that expanded detection of equipment problems to diagnosis and prioritisation of them based on severity.

There are an unlimited number of root causes for failures. Predictive Diagnostic algorithms can pinpoint failure effects. If it's in the data, Predictive Diagnostics will find it - and diagnose it to one of the pre-identified performance or mechanical fault patterns.

When a problem is detected, the SmartSignal SHIELD Predictive Diagnostic software automatically shoots an email to the customer, 24/7, with identification of the problem, diagnostic guidance, and clear prioritisation based upon severity.

Predictive Diagnostics alerts the operator as to whether an item warrants immediate corrective action or represents a future maintenance concern. Detection of minor problems is key to preventing larger ones, as the plant is able to closely track and monitor the problems. The software continually monitors the equipment and will adjust the priority as the number of deviating sensors and the degree of deviation change.

In a plant or fleet with multiple problems, Predictive Diagnostic notifications give maintenance crews the information they need to prioritise their work and focus on the most important issues first. Plant personnel work on the right equipment at the right time, making sure they have the right parts and resources available to do the job. They reduce their parts and labour costs by planning their outages instead of being forced into unplanned events - and they reduce maintenance duration and increase maintenance intervals. In addition, they avoid the higher risk of catastrophic failures that come with forced outages, since, at that point, equipment has passed its potential failure point.

Here's an example of how the increasing priority of a developing equipment issue identified by Predictive Diagnostics enabled a plant to receive early warning of a combustor hot spot.

Predictive diagnostics of a condenser tube leak The failure fingerprints of a condenser tube leak typically present as spikes in chemistry parameters. In this case, Predictive Diagnostics was able to provide early notification of a developing leak, based on a combination of deviations of two parameters.

Initially, the issue was rated 4 on a 1 to 5 priority scale, (5 being the lowest rating). A day later, the priority escalated from 4 to 3, based on a 3rd parameter contributing to the diagnosis. The notification was forwarded from SmartSignal's Availability and Performance Center to the plant.With this advance notification, the plant was able to repair the leak during a subsequent minor outage, preventing corrosion in the boiler and much more serious outages later on.

How to execute? Predictive Diagnostics can be implemented on all critical equipment in one plant or an entire fleet within a matter of weeks to a few months, depending upon size of deployment. It can be flexibly integrated into a user's processes and culture, and it integrates with a user's data infrastructure, thermal performance software, RCM system, and other tools.

Just as every piece of equipment is unique, so, too, is every operation. So, users can obtain services that meet their needs. And, if needs change, so can the services. A customer plant or fleet can host the software itself or use the SmartSignal in-house Availability and Performance Center (APC). The APC engineers provide flexible to full services in deployment, model maintenance, and monitoring. They can monitor the software and communicate with customers when the software identifies abnormalities that require action. SmartSignal identifies, diagnoses, prioritises, and verifies customer problems and works with customers to validate, solve, document, and capture knowledge. This service option ensures that Predictive Diagnostics is executed quickly and properly, and customers benefit from the Best Practices of the APC and other APC users.