Online Analysis Improves Profitability & Protects Mechanical Assets

As power generation plants age, flexible and efficient operational systems are even more critical to meet changing electrical power demands. Plants require continuous production with minimal risk of disruption. As a result, power generation stakeholders are developing online condition maintenance strategies and procedures to prevent failures, ensure optimized maintenance schedules and avoid economic and environmental consequences. A single plant can have several thousand critical and noncritical assets, such as turbines, electric motors and pumps. These assets are vital to reliable, efficient operation and act as leading indicators of a plant’s effectiveness. Fortunately, online condition monitoring and predictive maintenance can reduce rotating equipment and motor failures. The most effective plans combine two elements. They include online condition-monitoring strategies—such as trending factors like leading triggers and alarms that are designed to address degradation—coupled with traditional practices to condition monitoring. Plans that focus on vibration and excessive temperature—the leading causes of equipment failure—can minimize damage and shorten plant downtime.

What Is Condition Monitoring?

Condition monitoring is an aspect of predictive maintenance that provides the necessary information to make maintenance scheduling decisions. It involves comparing key measurement indicators with baseline normal behavior to determine if any equipment health degradation has occurred (see Image 1). Condition monitoring uses data collection, signal processing and analysis to provide a complete picture of machine health. The Electrical Power Research Institute has calculated comparative maintenance costs in U.S. dollars per horsepower (HP) for different maintenance strategies. According to the study, a scheduled maintenance strategy is the most expensive at $24 per HP. A reactive maintenance strategy is the second most costly at $17 per HP, but it can also be dangerous. A predictive maintenance strategy is the most cost-effective at only $9 per HP and reduces the risk of secondary equipment damage or harm to personnel from catastrophic failures.

Automated & Manual Condition Monitoring

Condition monitoring is traditionally conducted through routine manual diagnostic rounds. However, trends such as lower cost sensors and monitoring systems and the emergence of big data analytics are fueling the adoption of automated solutions. Applying online condition monitoring to both critical and noncritical assets provides the greatest insight into the overall reliability of the assets or plant (see Image 2). This helps companies thoroughly understand their operations and make informed business decisions. For large, expensive capital equipment and rotating machinery, the cost of implementing an online condition-monitoring solution is easily justified. The most important benefit is an increase in revenue from maximum uptime and optimal efficiency of production machinery. By monitoring production machines, end users can also detect flaws in product output based on machine behavior, reducing scrap and raw material use while increasing product quality. End users can also see reduced costs when using one of these systems. With strategic repairs, the operating and maintenance costs of machines with a condition-monitoring system can significantly decrease. The system can also identify developing faults with enough lead time for end users to properly schedule maintenance during planned downtimes and avoid expensive plant shutdowns. It can help warn personnel of impending risks of failure and prevent serious injury. Online monitoring systems also eliminate the need for workers to enter dangerous environments to take measurements. Moving from offline, manual data collection to online, automated monitoring and diagnostics for predictive maintenance offers companies several benefits. Workforce optimization—Manual diagnostic rounds can be time-consuming and require significant travel and setup time, leaving less time for specialists to analyze data and assess required maintenance. In addition, many industries are reporting that qualified predictive maintenance and vibration specialists are nearing retirement. Online condition monitoring helps ensure that specialized personnel are spending maximum time on the highest value tasks. Fewer gaps in data—When performing manual rounds to collect data, line operators at companies often can collect only a few measurements for any given piece of machinery each month. A typical power generation utility takes more than 60,000 measurements per month. Line operators can make mistakes or even copy previous results when manually noting data values. Online monitoring removes these errors and helps provide continuous data collection. Improved diagnostics—By using a single database, online condition monitoring programs provide more historical trend and baseline data for making more statistically significant fault predictions, allowing end users to make more informed maintenance decisions. With manual diagnostics, on the other hand, the interpretation of a fault is often based on the experience and knowledge of a specialist, and this experience can differ significantly from one specialist to the next.

Choosing a Condition-Monitoring System

Before choosing a condition-monitoring system, end users should consider which machines and failure modes must be monitored. The breadth and number of machines and the types of measurements needed to detect the failures will form a basis for this decision. For example, fusing measurements from different sensor types will provide a more accurate diagnosis. When implementing a large-scale condition-monitoring system, three main technology considerations come into play. The first is data management, which involves an appropriate data structure, database considerations for easily mining data, alarms and an aging strategy. The second is data analytics, which includes application-specific algorithms and higher-level predictive prognostics. It involves both real-time decisions and embedded intelligence closer to the sensor source, as well as at-rest data analytics on servers using aggregated data from multiple machines. As the use of data acquisition and monitoring systems increases, data management and data analytics become more complex, and a third consideration becomes critical—systems management. Remotely managing several monitoring systems helps increase reliability, serviceability and overall solution availability. This software solution makes it possible for end users to visualize and manage data and results, simplifying remote management for large numbers of monitoring systems.