Setting alarm limits and adjusting the alarm structure will help stave off inadequacies.
by Eugene Vogel
December 19, 2019

Many maintenance teams and service centers rely on vibration-based machine condition monitoring programs to maximize uptime and control maintenance costs. This places some of the responsibility for predicting machine failures on personnel who may be managing vibration data for hundreds of machines, sometimes in multiple facilities. Given the complexity of vibration data (amplitude, frequency and phase), the task can be daunting.

With the sheer volume of raw vibration data, it is impractical to properly analyze every routine collection event on every machine. Even simple, four-bearing machines have three to 12 measurement points, each with spectra and waveform data—sometimes with multiple parameters like velocity, acceleration and special bearing band parameters. There are software tools that can screen routinely collected data and spot machines that likely have developing faults. Valuable analysis time can focus on identifying which need repair to avoid more damage, downtime or safety issues. The problem is how to use those tools effectively.

Image 1. Frequency band alarms Image 1. Frequency band alarms (Image courtesy of EASA)

Screening for high overall vibration levels might seem like a logical first step. But overall vibration alarms will miss many emerging faults, because acceptable vibration levels from imbalance and flow turbulence on pumps and fans and background vibration often will mask low amplitude vibration from bearings and other critical components. By the time the fault becomes evident in overall vibration levels, it is far advanced, or failure has already occurred.

Detailed Screening Techniques

Among several more detailed techniques for screening vibration data, frequency band alarms and enveloping are the most common. There are also rules-based “intelligent” algorithms that can screen for known fault patterns. Maintenance professionals responsible for the success of vibration-based condition monitoring programs should thoroughly understand these data screening techniques.

Each of the following screening techniques employs proprietary computer software integrated with a vibration spectrum analyzer or a data collector instrument. Such systems are available, and data filtering and analysis techniques vary among them.

Band Alarms

Certain vibration frequencies (relative to the machine rotating speed) relate to specific groups of machine faults—for example, rotor mass imbalance will cause 1x rpm vibration in the radial directions. For that reason, it makes sense to allow the software to scan the data collected for a group of machines and trigger an alarm if the 1x rpm vibration exceeds expected levels. Shaft (coupling) misalignment is known to cause vibration at 1x, 2x and 3x rpm.

That band of frequencies should also be evaluated. Other frequency bands associated with various faults typical of some specific machine can also be targeted to trigger alarms.

Since a vibration spectrum is stored in the computer database as a sequence of amplitude values at specific frequencies, it is a trivial task for a computer to add up the amplitudes in any frequency bands (actually it is a root mean square [rms] sum) and compare the total to an alarm value.

Alarms can be set for each frequency band, which means low amplitude vibration from an early rolling element bearing fault could trigger an alarm, even if the vibration at 1x rpm was much higher. The amplitude at which to set alarm levels is a topic for another article, but for machine faults relative to shaft speed, it is important to set up frequency bands based on orders (1x rpm, 2x rpm, etc.), especially when machine speed may vary.

If bands are based on fixed frequencies (cycles per minute [CPM] or hertz [Hz]), changes in machine speed will cause the frequency of the fault to drift out of its proper band. When orders are used, accurate machine speed must be determined at the time the data is collected.

Enveloping or Spectra Alarms

An alternate technique is to allow the software to build an alarm envelope based on a spectrum or some average of a group of spectra. Rather than defining the alarm envelope in terms of discrete frequency bands, this technique essentially forms an arrangement of narrow bands based on a reference spectrum that has been recorded for a measurement. In the simplest implementation, the reference spectrum is the baseline spectrum collected at the initiation of a condition monitoring program. When a machine is repaired, replaced or operational conditions change, a new reference spectrum would be specified.

Most software that implements envelope alarms can calculate the reference spectrum as an average from a group of spectra. The group could simply be all the measurement points on one machine, all the measurement points on a group of machines, all the horizontal measurement points on a group of machines, and so forth. The group could also be the data collected for a single point over some time span, or a combination of measurement points and time span.

The tolerance for how far above the spectrum the alarm is set can be based on absolute value or a percentage. Tolerances are also set for the bandwidths. Envelope alarms are sensitive to speed, so if machine speed variation may occur, the spectra must be set up on an orders basis. And, machine speed must be recorded accurately when orders are used.