The Vibration Spectrum: A Diagnostic Roadmap

Definition: What is a Spectrum?

A vibration spectrum (or frequency spectrum) is a graph that displays the individual frequencies that make up a complex vibration signal. It is the single most powerful tool for diagnosing machinery faults. The spectrum is created by taking a raw time waveform signal and processing it with a Fast Fourier Transform (FFT) algorithm. The resulting plot shows vibration amplitude on the vertical (Y) axis and frequency on the horizontal (X) axis.

Why the Spectrum is the Key to Diagnostics

A machine’s vibration is a mixture of many different signals occurring simultaneously. The time waveform shows this jumbled signal, but the spectrum acts like a prism, separating it into its distinct components. This is critical because different mechanical and electrical faults generate vibration at specific, predictable frequencies. By looking at the peaks in the spectrum, a trained analyst can identify the root cause of a problem with high confidence.

How to Read a Vibration Spectrum

A spectrum plot contains a wealth of information. The key elements to examine are:

1. The Frequency (X-Axis)

This axis shows “what” is vibrating. It can be displayed in various units, such as Hertz (Hz), Cycles Per Minute (CPM), or Orders (multiples of running speed). The location of a peak on this axis is the primary clue to its source.

2. The Amplitude (Y-Axis)

This axis shows “how much” vibration is occurring at a given frequency. The amplitude indicates the severity of the event. It can be measured in units of displacement, velocity, or acceleration, and can be displayed on a linear or logarithmic scale.

3. The Peaks

Each peak in the spectrum represents a specific, periodic event occurring in the machine. Interpreting a spectrum is the process of matching these peaks to known fault frequencies.

Common Patterns in a Spectrum and What They Mean

Analysts look for characteristic patterns to diagnose faults:

• A single high peak at 1x RPM: This is the classic signature of rotor unbalance.
• A dominant peak at 2x RPM: Often accompanied by high axial vibration, this is a strong indicator of shaft misalignment.
• A series of running-speed harmonics (1x, 2x, 3x, 4x…): This pattern is the primary indicator of mechanical looseness.
• High-frequency, non-integer peaks: These often correspond to the calculated fault frequencies of rolling-element bearings.
• A high-frequency peak with sidebands: A peak at the Gear Mesh Frequency (GMF) surrounded by smaller peaks (sidebands) spaced at the gear’s running speed is a definitive sign of a gear fault.
• A raised “noise floor”: A broadband increase in the baseline energy of the spectrum can indicate friction, rubs, or cavitation in pumps.

The Importance of Baseline and Trending

A single spectrum provides a snapshot of a machine’s health at one moment in time. The true power of spectral analysis comes from comparing the current spectrum to a baseline spectrum taken when the machine was known to be in good condition. By trending the amplitudes of specific peaks over time, analysts can track the progression of a fault from its earliest stages, allowing for planned, proactive maintenance long before a failure occurs.

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