The vibration monitor HY-3SF plays a key role in industrial equipment status monitoring and fault diagnosis. Accurate signal processing is the core link of its effective work, which directly affects the judgment of equipment status and the prediction of faults. This article will elaborate on the signal processing process of HY-3SF.

Signal acquisition

1. Sensor output

HY-3SF first obtains the signal from the source of vibration, usually through an acceleration sensor to obtain a time-domain variation analog signal containing equipment vibration information. For example, in the monitoring of large rotating machinery such as turbines or generators, acceleration sensors are installed in key parts of the equipment, such as bearings.

These sensors can convert mechanical vibration into electrical signals, and the characteristics of their output signals such as amplitude and frequency are closely related to the vibration state of the equipment. For example, when the equipment is operating normally, the acceleration signal fluctuates within a relatively stable range; when the equipment fails, such as misalignment or bearing wear, the amplitude and frequency characteristics of the signal will change significantly.

2. Sampling parameter determination

In the digital instrument HY-3SF, to accurately reconstruct the time domain waveform, the sampling rate and number of sampling points must be determined. The length of observation time is equal to the sampling period multiplied by the number of sampling points. For example, if the change period of a vibration signal to be monitored is 1 second, according to the sampling theorem (Nyquist sampling theorem), the sampling frequency must be greater than twice the highest frequency of the signal. Assuming that the highest vibration frequency of the equipment is 500Hz, the sampling frequency can be selected to be above 1000Hz.

The selection of the number of sampling points is also critical. Common choices are 1024, a power of 2 number, which is not only convenient for subsequent FFT calculations, but also has certain advantages in data processing.

Signal conditioning

1. Filtering

Low-pass filter: used to eliminate high-frequency interference noise. For example, near some electrical equipment, there may be high-frequency electromagnetic interference. The low-pass filter can effectively remove these signals that are higher than the normal vibration frequency range of the equipment and retain useful low-frequency to medium-frequency vibration signal components.

High-pass filter: can eliminate DC and low-frequency noise. During the start-up or stop phase of some equipment, there may be low-frequency offset or drift signals. The high-pass filter can filter them out to ensure that the signal that mainly reflects the normal operation vibration of the equipment is retained.

Bandpass filter: Bandpass filter comes into play when it is necessary to focus on the vibration signal within a specific frequency range. For example, for some equipment with a specific rotation frequency component, by setting the appropriate bandpass filter frequency range, the vibration related to the component can be monitored more accurately.

2. Signal conversion and integration

In some cases, the acceleration signal needs to be converted into a velocity or displacement signal. However, there are challenges in this conversion process. When the velocity or displacement signal is generated from the acceleration sensor, the integration of the input signal is best implemented by analog circuits because the digital integration is limited by the dynamic range of the A/D conversion process. Because it is easy to introduce more errors in the digital circuit, and when there is interference at low frequencies, the digital integration will amplify this interference.

FFT (Fast Fourier Transform) Processing

1. Basic Principles

HY-3SF uses FFT processing to decompose the time-varying global input signal sampling into its individual frequency components. This process is like decomposing a complex mixed sound signal into individual notes.

For example, for a complex vibration signal that contains multiple frequency components at the same time, FFT can accurately decompose it to obtain the amplitude, phase and frequency information of each frequency component.

2. Parameter setting

Resolution lines: For example, you can choose different resolution lines such as 100, 200, 400, etc. Each line will cover a frequency range, and its resolution is equal to fmax (the highest frequency that the instrument can obtain and display) divided by the number of lines. If fmax is 120000cpm, 400 lines, the resolution is 300cpm per line.

Maximum frequency (fmax): When determining fmax, parameters such as anti-aliasing filters are also set. It is the highest frequency that the instrument can measure and display. When selecting, it should be determined based on the expected vibration frequency range of the equipment.

Average type and average number: Averaging helps to reduce the impact of random noise. Different averaging types (such as arithmetic mean, geometric mean, etc.) and appropriate average numbers can improve the stability of the signal.

Window type: The choice of window type affects the accuracy of spectrum analysis. For example, different types of window functions such as Hanning window and Hamming window have their own advantages in different scenarios.

Comprehensive data analysis

1. Trend analysis

By performing time series analysis on the processed vibration signal data, the trend of the total vibration level is observed. For example, as the equipment runs longer, does the total vibration amplitude gradually increase, decrease, or remain stable? This helps to determine the overall health of the equipment. If the total vibration amplitude is low at the beginning of normal operation of the equipment and gradually increases after a period of time, it may indicate that the equipment has potential wear or failure risks.

2. Fault feature identification

Identify the fault type based on the amplitude and frequency relationship of each frequency component of the composite vibration signal. For example, when the equipment has an unbalanced fault, a large vibration amplitude usually appears at the power frequency of the rotating part (such as the frequency corresponding to 1 times the speed); and when there is a bearing fault, an abnormal vibration signal will appear at the frequency component related to the natural frequency of the bearing.

At the same time, under the same operating conditions, the phase relationship of the vibration signal of a part of the machine relative to another measuring point on the machine can also provide clues for fault diagnosis. For example, in a pair of rotating equipment parts, if they are not aligned, the phase difference of their vibration signals will be different from normal.

The signal processing process of the vibration monitor HY-3SF is a complex and orderly process. From signal acquisition to FFT processing and the final comprehensive data analysis, each link is crucial. Accurate signal processing can provide a reliable basis for predictive maintenance of industrial equipment, help timely discover hidden faults of equipment, and improve equipment reliability and operating efficiency. Through in-depth understanding and reasonable application of different signal processing technologies and parameters, HY-3SF can better play an important role in industrial equipment status monitoring.

When looking for high-quality, reliable vibration monitors, YOYIK is undoubtedly a choice worth considering. The company specializes in providing a variety of power equipment including steam turbine accessories, and has won wide acclaim for its high-quality products and services. For more information or inquiries, please contact the customer service below:

E-mail: sales@yoyik.com
Tel: +86-838-2226655
Whatsapp: +86-13618105229