Online transient analysis optimises asset utilisation - a case study
Traditional condition monitoring systems are designed for “steady state” operation of the turbine. But a turbine is rarely operating under these conditions. Loads, pressures, temperatures and vibration are changing rapidly, especially during startups, shutdowns and “bumps” in the night.
During these “transient” events, rapidly changing conditions within the machinery should be watched carefully.
All bearings need to be monitored continuously with ongoing comparison to a baseline so that changing conditions can be recognized. This technically challenging mode of operation generally requires advanced collection methods, high level processing, and the services of a turbine specialist to keep a close watch during a startup or shutdown operation, which might take from 24 to 48 hours.
The danger of rapid degradation of machine health is greatest at this time, yet it is the most technologically challenging period to monitor due to the large volumes of data generated and the speed with which changes can occur. New transient analysis technology meets that challenge by automating the process throughout the critical period. Live views give analysts a decision-making tool that can be accessed from another location by dialing into the system. Any machine that is being monitored by protection systems can be upgraded to transient analysis technology.
Since the turbine’s operating parameters are constantly changing, it is important to monitor overall machine health and not just changes in vibration. In transient analysis, turbine engineers and operators have easy access to continuous, real-time vibration information, allowing them to closely monitor the condition of turbines during critical startup and shutdown periods.
Several different plots of live data are available on control room monitors, giving decision-makers an exact picture of what is happening within the machinery. Characteristics of this new transient analysis technology include:
A live turbine dashboard (monitors) that produces new information five times every second and enables immediate action
Up to 40 hours of complete data recorded before, during, and after an event
Easy navigator for improved operator efficiency and a simple setup
A smart data search system that extracts necessary information from voluminous data
Through the “live turbine dashboard” in the control room, users have dual monitors to view the development of seven different plot types: Orbit, Shaft Centerline Bode, Nyquist, waveform, spectrum, and cascade — on up to eight bearings. The data are updated up to five times per second.
A spectral measurement system processes vibration waveforms into frequency and amplitude content (FFT spectral data). Many factors can influence the overall vibration value, but they may not be recognized specifically unless the FFT spectral data are captured, analyzed, and published — real time. Trends of specific defects, such as oil-whirl, rubbing, unbalance and loose-ness are displayed on the monitors.
A data recorder stores continuous data, not just snapshots. Should it become necessary to carefully examine any critical period during a turbine startup or shutdown — or a bump in the night — key data are available. If an operation must be left unattended, auto archiving can be initiated to cover any 40-hour period.
In the past, it was necessary to have a vibration collection strategy for monitoring turbines because of the large volume of data generated. But those collection strategies sometimes missed critical data. Today, setting up data-capture simply requires checking a box on the “configuration” screen. If that box is checked, transient data are collected. A “region of interest” selector enables turbine engineers and operators to pick the window where they want to extract detailed information (Fig. 1, 2). No time is wasted searching through megabytes of data.
Aiding turbine analysts
Transient data help analysts to zoom in on any anomaly in turbine operation in the last 60 hours. The following case study highlights how this detailed data can benefit turbine analysts. When starting up, a turbine is attempting to pass through the critical resonance. Vibration should have begun to decrease at that point. Instead, it took an abrupt turn and vibration began increasing greatly with speed (Figure 3).
The Bode plot (upper right) shows that the critical resonance speed of the machine was changing — it was shifting to the right. For the critical to shift, damping, stiffness or mass must be changing . It is reasonable to assume that mass did not change, but that damping or stiffness of the system has changed . The operator noticed that the turbine did not pass through the critical speed. (In some cases the anomaly is not too great and the operator could continue to increase speed, passing through the critical speed). In this case, the operator was not getting past the critical resonance. Vibration continued to increase, so a decision was made to shutdown the turbine.
Simply re-starting this turbine, with no remedial action, will cause the same result.
Other plots will reveal what action must be taken for a successful startup. The orbit shows that the normal dynamic shaft motion has abruptly detoured (Figures 3, 4). A normal orbit would follow the green path. The shaft was not moving freely in an elliptical path as we would normally expect.
Loading or misalignment could explain the shift in critical resonance speed shown in the Bode plot. When the shaft is misaligned or improperly loaded, stiffening of the system can increase. This increases the critical resonance speed.
The shaft centerline plot in the lower left should not be one dot, as the turbine came up to speed (Figure 4). Ideally the shaft centerline position within the bearing should move up and to the right. However, if the shaft is misaligned or improperly loaded, it would be forced and held in an abnormal position. The spectrum plot shows a high 2x turning speed peak — another indication of misalignment or improper loading. All the above show that the turbine needs to be re-aligned and checked for binding and improper loading.
The above technology provides the user a powerful tool for predicting the future health of machines and developing an optimization strategy to reduce maintenance, thus enhancing their plant's return on assets (ROA).
- Shailesh Naik, Sudip Das