To understand vibration analysis, it’s important to first grasp the concept of vibration itself. Vibration is essentially the back-and-forth movement of a machine part from its normal position. Initially, vibrations might not seem harmful, causing only energy waste and noise. However, if not addressed, they can escalate to the point of making machines unusable. This can lead to financial problems like high maintenance costs, extended downtime, increased spending on spare parts, and even the tough decision to scrap expensive equipment.

Vibrations occur in various machine components, like gearboxes and electric motors, during their operation. These vibrations can result from different issues such as imbalanced rotating parts, lubrication problems, gear damage, or the inherent characteristics of the equipment itself. Vibration is a two-sided phenomenon – it indicates problems and can cause problems. Failing to detect these issues in time can lead to severe damage, which every industry wants to avoid.

Understanding Vibration Data

In vibration analysis, sensors are placed on machine parts to collect data, which is then processed to predict potential failures. These vibration signals, arising from various forces and factors, appear as complex waveforms. Interpreting this fault data by studying time-domain graphs can be challenging. (See our article: Understanding Machine Data)

This is where spectrum analysis comes in – it’s the key to understanding vibration signals. The most commonly used tool in this field is the Frequency-Amplitude graph. Frequency tells us how often vibrations repeat in a second, measured in Hertz (Hz) or revolutions per minute (RPM), especially for vibrations in rotating equipment. Amplitude, on the other hand, indicates the strength of vibrations and can be measured in different units. It’s important to note that variations in units across different software or products can sometimes lead to misinterpretations. Common amplitude units include Peak-to-Peak, Peak, and RMS.

Vibration Spectrum and Trends

In vibration analysis, it’s important to realize that vibration measurement is not a standalone concept; it’s more like a mathematical expression. Mechanical vibrations can be measured in terms of position, velocity, or acceleration. Acceleration, measured in millimeters per second squared (mm/s²), is typically used for frequencies above 1000 Hz, while velocity, expressed in meters per second (m/s), is suitable for frequencies in the range of 10 Hz ≤ f ≤ 1000 Hz. For lower-frequency vibrations (below 10 Hz), position is the preferred unit, measured in microns.

As spectrum analysis is a specialized field, the common practice is to monitor vibration power using the root mean square (RMS) value. While it doesn’t provide all the details, it’s an effective way to track changes in vibration intensity. You can think of the RMS value as a representation of the vibration’s power distribution on the Frequency-Amplitude (RMS) graph.

Vibration Spectrum and Trend Graphs

Fig. 1 Vibration Spectrum and Trend Graphs

The Significance of Spectrum Analysis in Vibration Analysis

For vibration analysis, spectrum analysis plays a central role in unlocking the secrets of machine health and performance. To fully understand its importance, we need to navigate the intricacies of vibration signals, which are shaped by various forces and factors.

The Fourier Transform

Spectrum analysis relies on the transformative capability of the Fourier Transform. This mathematical tool is key to converting complex, time-based vibration signals into a harmonious world known as the frequency domain. Here, vibrations become distinct musical notes, forming frequency spectra that reveal a machine’s condition. Frequencies, much like the tempo in a musical composition, guide us through the machine’s vibrations. Amplitude, the partner of frequency, represents the intensity of vibration. While it can be measured in different units, it’s important to be aware of potential misinterpretations when using different software or products.

Common amplitude units include Peak-to-Peak, Peak, and RMS (Root Mean Square). Amplitude tells us how loud or soft a frequency is within the vibration spectrum, much like the volume of a musical note. A high-amplitude frequency indicates strong vibration, while a low-amplitude frequency suggests weaker vibration. Experts assess the severity of machine damage or defects by examining these amplitudes across frequencies.

Faults and Frequencies

Spectrum analysis reveals an interesting phenomenon – specific machine faults or defects manifest at particular frequencies, similar to signature melodies within the vibration spectrum. If left unaddressed, these “signature frequencies” increase in amplitude, resembling a musical note growing louder and more prominent.

Monitoring, Interpretation, and RMS

In practice, spectrum analysis involves careful monitoring of frequencies and amplitudes within the spectrum. This process is visualized through a Frequency-Amplitude graph, providing expert analysts with a canvas to spot irregularities and trends. It offers valuable insights into the overall health of the machine. At the core of this analysis is the root mean square (RMS) value. While it may not provide detailed information, it serves as a reliable way to track changes in vibration intensity. It quantifies the area beneath the Frequency-Amplitude (RMS) graph, a valuable tool for assessing the overall power of vibration.

Frequency-Amplitude (RMS) graph

Fig. 2 Frequency-Amplitude (RMS) graph

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