3-Axis Vibration Sensors

A larger frequency bandwidth improves performance by capturing fault signals that narrower-bandwidth sensors cannot detect—especially high-frequency indicators of early bearing and gear damage. For example, expanding from 4000 Hz to 5300 Hz allows detection of subtle, high-frequency impacts that may not appear below 4 kHz. This enables earlier intervention and makes the sensor effective across a broader range of machine speeds and fault types.

A scalar value is a single number that expresses the magnitude of a measurable property, without any directional component. It tells you how much of something, but not where it’s going—like °F for temperature or km/h for speed. By contrast, a vector has both magnitude and direction—like velocity; for example, 30 m/s to the east. In signal analysis, scalar values are used to simplify complex waveforms into meaningful metrics. RMS returns a scalar representing average amplitude over time, while FFT produces a series of scalar values representing signal amplitude across distinct frequency bands.

RMS (Root Mean Square) is a method, and a resulting scalar value, for calculating the average amplitude of a signal over time. It captures the full contribution of both positive and negative values, which carry amplitude regardless of sign. This contrasts with frequency-domain methods like FFT, which measure how much amplitude is present at each frequency. In signal analysis—whether it’s mechanical vibration, acoustic pressure, or electrical oscillation—RMS gives you a meaningful measure of a signal’s amplitude over time. In the case of sound, RMS is used to calculate values expressed on a dB scale, which relate to perceived loudness. In mechanical systems, RMS reflects how much force, displacement, velocity, or acceleration is being delivered on average—typically expressed in units like millimeters per second (mm/s) or meters per second squared (m/s²), depending on what’s being measured. These values are useful for assessing wear, stress, or overall performance—unlike peak values, which only capture brief extremes.