The 4-20 mA transmitters are used in condition and process monitoring because theyprovide 24/7 online protection for critical equipment, increase efficiency and reduce downtime. Vibration sensing transmitters make up a small portion of these sensors, but are quickly gaining in practicality and implementation.
These sensors are accelerometers, which have been configured for looppower and generally provide output signals that are representative of the overall vibration levels. This vibration signal can interface with many types of commercially available currentloop monitoring equipment, such as recorders, alarms, programmable logic controllers (PLCs) and digital control systems (DCSs).
Vibration transmitters capitalize on the use of existing process control equipment and human-machine interface (HMI) software for monitoring machinery vibration and alarming of excessive vibration levels. This practice offers the ability to continuously monitor machinery and provide an early warning detection of impending failure. With this approach, existing process control technicians may be utilized for monitoring the vibration levels, while skilled vibration specialists are called upon only in the event the vibration signal warrants more detailed analysis.
Typically, this technology has been dominated by pressure, temperature, flow and load sensors, but the new generation of vibration transmitters has greater capability and is becoming more common for condition monitoring, protection and even process feedback. The overall root mean square (RMS) vibration levels transmitted are good for monitoring the overall health of machinery and protecting against catastrophic failure. However, it is not successful at detecting high frequency faults, typically caused by the early stage of bearing wear. Most traditional vibration transmitters have limited frequency responses (1 kHz maximum) and cannot distinguish high frequency bearing impacts, which have a much lower amplitude compared to the broadband vibration. Therefore, traditional vibration transmitters only detect bearing defects at an advanced stage of failure, once the energy has shifted to lower frequency, high amplitude vibration.
Two groups of methods are widely adopted for the determination of rolling bearing health and the presence of faults. Although not always possible, the best results are obtained when both methodologies are adopted.
The first group of methods, which are diagnostics orientated, is based on the separation and analysis of discrete components of certain frequencies that make up excited oscillations in the bearing. This requires frequency components of the spectrum and characteristics of the signal pulse shape to be compared with the characteristic frequencies of the bearings. Vibration signal spectra, ranges of narrow band high frequency components, must be used to analyze these parameters and diagnose bearings. Such a complex method cannot be automated and requires the time and resources of a skilled analyst.
The second group of methods is based on the determination of the technical condition of the bearing as a whole. Diagnostic parameters included in this method are amplitude distribution, moment characteristics, crest factor, peak value of the vibration waveform, and the comparison of vibration parameters in various frequency bands. One important property of field applicable methods is a strong ability to separate current process characteristics, such as speed and loading, from bearing defects. Previously, these methods could not be automated and inputted into a control system, however, recent improvement in sensor technology has allowed the sensor industry to build specialized filtered signal conditioning right into the body of a vibration transmitter.
Five calculated parameters are being used today in this new generation of vibration transmitters for the early detection of faults in rolling element bearings. These are high-pass filtered RMS acceleration, true peak acceleration, crest factor, crest factor plus and compensated peak acceleration.
High-Pass Filtered RMS Acceleration
Description: This is the mathematical average of all values captured within the sampling window.
Use in Monitoring Bearings: RMS acceleration can be used when running a bearing close to failure before alarming is preferred. It is also helpful for processes that involve violent impacting and high amplitude short pulses that can transfer through the machinery to the bearing. Using RMS acceleration in this case avoids false trips caused by these high energy, but short duration vibrations. The true peak acceleration output may increase greatly with one of these impacts, but RMS acceleration will not significantly increase.
True Peak Acceleration
Description: This is the highest acceleration value captured within the sampling window.
Use in Monitoring Bearings: This is the best and most commonly used method for early detection of bearing wear for fixed speed machinery. The peak capture and high pass filtering provide values that strongly correlate to the severity of cracks, spalling, or brinelling. The short pulse caused by these faults has low energy compared to the overall broadband vibration, therefore, it would typically get lost in a traditional RMS measurement.
Description: This is the ratio of true peak to RMS acceleration. Therefore, this value increases as the amplitude of high frequency impacts in the bearing increase compared to the amplitude of overall broadband vibration. The output is a unit-less severity scale from one to 16. Refer to Table 1 for a guide that relates the output value to the bearing condition.
Use in Monitoring Bearings: Crest factor is better suited for variable speed machinery because the ratio of peak to RMS acceleration should not vary that much at different speeds. This is due to the fact that both will increase as speed increases, however, only true peak acceleration will increase if the fault severity increases in the early stages of bearing failure. Once the bearing condition is poor enough, the fault vibrations will actually start to cause the RMS acceleration value to increase, therefore, the crest factor starts to decrease as the condition gets worse. Trending this crest factor value can capture that decrease and still provide accurate awareness of the bearing condition. If it is not possible to trend these values or a similar output that always increases as bearing condition worsens is preferred, consider using the crest factor plus output described next.
Crest Factor Plus
Description: This is similar to crest factor, but the output has been adjusted so that the value always increases as the bearing condition gets worse. This uses a sum of crest factor, RMS acceleration and true peak acceleration. They are weighted differently, such that the sum of the three always increases as bearing condition worsens, even if one value decreases. This primarily corrects for the fact that the crest factor decreases when the fault is very severe. By adding RMS acceleration into the equation, the decreasing crest factor is counteracted by the increasing RMS acceleration as bearing condition becomes very poor. The output is a unit-less severity scale from one to 16, as shown in Table 1.
Use in Monitoring Bearings: Crest factor plus is best suited for variable speed machinery when trending is not possible or not desired. The value will always increase over the entire lifecycle of the bearing as its condition worsens. This allows the user to simply set an alarm value for the severity scale and not worry about keeping historical data for trend monitoring.
Figure 1: Crest factor plus for bearings of different conditions
Compensated Peak Acceleration
Description: This is a normalized ratio of the acceleration of impacts within the bearing to the linear speed of the rolling element passing over a defect. This uses the speed and diameter of the bearing that were programmed into the sensor to determine the linear velocity of the rolling element. The output is a unit-less severity scale from one to 16, as shown in Table 1.
Use in Monitoring Bearings: Compensated peak acceleration is most helpful when the user wants to monitor rolling element bearings in machinery ofvarious sizes and speeds. By normalizing the output using the shock pulse method described above, the values will be similar for machines with the same bearing condition, even if these machines vary greatly in size and speed. Therefore, a single alarm limit can be set in the control system for all monitored bearings across different pieces of machinery.
Table 1 – Bearing Condition Guide for Crest Factor, Crest Factor Plus and Compensated Peak Acceleration
These five outputs greatly improve the effectiveness of monitoring rolling element bearings with 4-20 mA technology. Many of them can be found in various products across the industry, with some monitoring solutions incorporating all five outputs to improve upon existing in-line transmitters.
With the new generation of vibration transmitters, predictive maintenance departments and vibration technicians alike can have more confidence and flexibility when employing 4-20 mA technology for monitoring the condition of bearings online.
Stephen Arlington is an engineer and Product Manager for IMI Sensors in Buffalo, NY. He has assisted withthe development and marketing of IMI’s industrial sensing solutions, and is known for his work with the newly launched Echo® Wireless Vibration Monitoring System.Stephen earned his Bachelor’s in Mechanical Engineering from the University at Buffalo and is certified as a Category II Vibration Analyst.
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