An Introduction to Vibration Analysis

In this story, I’m going to talk about vibration analysis fundamentals. The idea of this story is to take a quick look at all the primary vibration analysis techniques.

What is Vibration Analysis?

Vibration Analysis (VA), applied in an industrial or maintenance environment aims to reduce maintenance costs and equipment downtime by detecting equipment faults.[3][4] VA is a key component of a Condition Monitoring (CM) program, and is often referred to as Predictive Maintenance (PdM).[5]

When to use vibration analysis?

Most commonly VA is used to detect faults in rotating equipment (Fans, Motors, Pumps, and Gearboxes etc.) such as Unbalance, Misalignment, rolling element bearing faults and resonance conditions.

The Goal

Simply, the goal of vibration analysis is to avoid equipment fails.

which causes secondary damage, which causes downtime, potentially safety incidents, we lose production, we have much higher costs associated with the repair and a lot of other reasons we want to avoid that. So we can use vibration analysis alongside other technologies to get the earliest warning at early stages of failure.

Why Vibration Analysis?

Vibration analysis is a terrific tool for condition monitoring because vibration analysis in a sense enables us to look inside the machine and see exactly what’s going on.

How to Measure Vibration?

For measuring the vibration, we have to use sensors to measure one of these parameters:

  • Displacement
  • Velocity
  • Acceleration

As you know, these parameters are simply convertible to each other.

the most common way to measure vibration is using an accelerometer to measure acceleration. accelerometer uses a piezoelectric ceramic to measure the vibrations. I will cover the sensors in detail (types, installation, configuration, etc) in the next story.

Waveform

The output of the accelerometer is voltage. Displaying the output in a diagram per time, create a “time waveform (TWF)” for us.

In the above image, we have a simple machine which is unbalanced so it generates a motion and the installed sensor capture that motion. A single motion generates a sine wave with a single frequency and amplitude.

Spectrum

The above example is very simplified version of reality. In reality, a machine doesn't work in ideal state and also it has much more components which all of them may have motions, so one motion cause one sine wave and the sensor captures all that vibrations and we will see the summation of all the vibrations which is a complicated waveform like this:

By looking at this diagram, we can’t understand much about machine’s situation, since it’s complicated and we can’t figure out which component of the machine is not in the healthy situation or what’s the problem with that component. by looking at waveform diagram we may just figure out that how severe is the vibrations; that’s why we use spectrum analysis. spectrum makes each source of vibration easier to see.

Spectrum is like looking at waveform diagram at the frequency axis:

Using the Fast Fourier Transform (FFT) enables us to break a waveform down to its generator components and generate the spectrum. So the spectrum is generated from the waveform.

Analysis Time

Different fault condition generates different patterns in the spectrum. for example, when the machine is unbalanced, we expect to see a high amplitude frequency in 1x (which means the frequency of running speed of machine):

or for a machine which is misalignment, we expect such a spectrum like this:

and there are patterns for other faults like angular misalignment, dynamic unbalance, looseness, etc as well.

However, it’s worth mentioning that the real spectrum is far more complicated than idealized patterns as the above examples since there are many week motions in a machine. A real spectrum for parallel misalignment (the above example) could look like this:

Therefore, if you want to become a good vibration analyst, you should understand the machine, understand where the vibration comes from, understand the analyzer, and understand what you’re seeing in the data then you can figure out what’s going on inside the machine. Not just look at a spectrum and say that spectrum looks a bit like that one so it must be a misalignment.

In addition, time waveform also can help you in the analysis. However we converted waveform to spectrum for an easier analysis, but you should be aware that this conversion ignores some data. When the spectrum is not enough to make a decision about what’s going on inside the machine, it’s a good idea to go for waveform, time waveform analysis provides details not found in the spectrum.

Conclusion

  • Vibration analysis can be used as a troubleshooting tool to avoid failures.
  • Vibration analysis can be used to detect the fault in early stage so reduces maintenance costs and increases up-time.
  • Spectrum analysis is the most commonly used vibration analysis tool — the picks usually relate to components within the machine.
  • We look for changes in pattern to determine if the condition of the machine may have changed.
  • We look at the amplitude of the peaks to assess the severity of the fault condition.
  • We can use more analysis tools like time waveform analysis and phase analysis (we didn’t talk about it) to verify/confirm the diagnosis made with the spectrum.
  • Time waveform shows us what’s happening inside the machine from moment to moment.
  • Too few people utilize time waveform.

Further Reading

  • Phase Analysis
  • High-Frequency Analysis
  • Orbit Analysis
  • Wictor, W. “Machinery vibration: measurement and analysis.” McGraw. Inc Singapore, (1991).

References

Experienced UX designer of enterprise products. Focused on ideation, strategy, visual design, prototyping, and design systems. Certified, holistic, planner.