Steve Tonissen of SmartSignal describes how predictive analytics and predictive diagnostics for power plants can prevent minor problems developing into major ones. The predictive element involves the intelligent linking of what would in the past have been seen as separate pieces of maintenance information in order to make informed decisions about potential equipment failure.

Steve Tonissen, SmartSignal, USA

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SmartSignal’s Availability and Performance Centre utilizes predictive diagnostics to help customers avoid major plant outages

Experienced power plant operators can attest to the marathon race between data intelligence and equipment failure. Despite sizeable investments in instrumentation, sophisticated OEM devices, sensors and software, many plants contend daily with data overload and a lack of actionable intelligence to prevent surprises.

They are fixing what is about to break, but also fixing what is not. Without adequate direction, they cannot prioritize and optimize their lean resources — and they cannot avoid surprise equipment failures.

While no technology can prevent normal equipment wear or the need for maintenance, predictive analytics and the latest advances in predictive diagnostics now are helping plants to overcome their challenges — by detecting impending problems early and allowing plants to take control of their operations.

Predictive Analytics Set the Pace

History has been referred to as the science of chronicling that which never happens twice. Likewise, every piece of equipment is unique and operates unlike all others—thus the surprise factor.

Even supposedly identical pieces of equipment were manufactured on different days under different circumstances so possess different operating characteristics, have different maintenance histories, and have been working under different ambient conditions, loads and operating contexts.

Ultimately, human analysts are tasked with deciphering mountains of data for all these pieces of equipment to infer equipment health and condition. When it comes to plant equipment, there is no such thing as a welcome surprise.

One erroneous judgment can result in an equipment failure sufficient to reduce or halt production. In some cases, a catastrophic event can rack up seven-figure losses. Predictive analytics provide early and actionable real-time warnings of impending equipment and process problems that otherwise would have gone undetected. These warnings enable operators to fix only what needs to be fixed. 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. Typically, the cost of getting ahead of a problem is 30–80 per cent less with predictive technologies than without.

Predictive analytics work by understanding the essential element – that every piece of equipment is unique. They develop a set of fingerprints for each individual piece of equipment across all known loads, ambient conditions and operating contexts. They calculate the proper operational relationships among all relevant parameters, such as loads, temperatures, pressures, vibration readings, ambient conditions, and so forth.

The system 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 detect and isolate abnormal behaviour, in the context of operating conditions.

Predictive analytics then post these incidents and provide exception-based notifications of developing problems to users. They do this automatically, continuously, 24 hours a day. Predictive analytics can determine, even though a temperature reading is in the middle of the minimum/maximum range, that a sensor value is abnormal for a particular piece of equipment in the context of its individual operating conditions.

Rather than plant personnel sorting through vast amounts of data to extract meaningful nuggets, predictive analytics operate on a real-time model, identifying and flagging these subtle changes from expected behaviour that have been verified to be actionable issues.

In doing so, they identify sensors, 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 the context of the overall plant schedule and 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 all sectors of power generation — coal, gas, nuclear, wind and hydro. In the region of 50 per cent of the US power generation fleet is using it, along with some of the oil 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 view it in the context of the ‘P-F curve’. Reliability engineers use a P-F curve to visualize 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 shutdown for repair. Before predictive analytics, with traditional condition monitoring tools like vibration analysis, this could be a short time envelope, as indicated in Figure 1.

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Figure 1: The P-F curve – potential failure (P) occurs when events lead to component damage that needs repair and functional failure (F) occurs when equipment performance no longer meets design conditions

Given the customized equipment models that automatically adapt to changes in load, ambient conditions and operating contexts, though, predictive analytics provide a more accurate assessment of the condition of each individual piece of equipment, and therefore, earlier warning of developing issues.

Put quite simply, predictive analytics enable operators to move up the curve, i.e. provide extended lead time and enable operators to fix small problems before they become catastrophic, as portrayed in Figure 2.

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Figure 2: Predictive analytics enable operators to move up the P-F curve, i.e. provide extended lead time and enable operators to fix minor problems before they become major ones

Passing the baton to predictive diagnostics

Predictive diagnostics build 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 the apparent cause of the failure, and the priority of the impending failure.

SmartSignal’s SHIELD Predictive Diagnostics software 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 ten years of data resulted in the identification of fault patterns in the context of operating behaviour.

From here, with user input, a new technology was developed that expanded detection of equipment problems to diagnosis and prioritization of them based on severity.

Although there is an unlimited number of root causes for failures, the number of failure effects that can be observed by sensors is limited. Predictive diagnostic algorithms can pinpoint those effects. If it is in the data, predictive diagnostics will find it – and diagnose it to one of the pre-identified performance or mechanical fault patterns.

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 diagnostics notifications give maintenance crews the information they need to prioritize 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.

Power plants can 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, as equipment has then passed its potential failure point.

Here follows 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 1–5 priority scale, with 5 being the lowest rating. A day later, the priority escalated from 4 to 3, based on a third parameter contributing to the diagnosis.

The notification was forwarded from SmartSignal’s in-house Availability and Performance Centre (APC) 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.

But How to Execute?

The good news is that 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 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, prioritizes and verifies customer problems and works with customers to validate, solve, document and capture knowledge. This service option ensures that predictive diagnostics are executed quickly and properly, and customers benefit from the best practices of the APC and other APC users. It reduces risk, optimizes user resources and allows users to easily integrate their systems and processes, ensuring that they meet company objectives and continue to innovate.

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