A Simple Method for Automatic Diagnosis of Rolling Bearing Damage Faults
**1. Introduction**
Rolling bearings are essential components in a wide range of rotating machinery. Monitoring and diagnosing their operational conditions is crucial for maintaining system reliability and preventing unexpected failures. Bearing faults can generally be categorized into two types: damage-type faults, such as pitting, cracking, spalling, and surface scratches on bearing elements, and wear-type faults, which include abrasive wear due to foreign particles or poor lubrication.
Current methods for bearing monitoring and fault diagnosis typically involve either online monitoring or offline analysis. However, these approaches require trained personnel who have a good understanding of fault diagnostics, making it challenging for field workers to perform effective diagnosis. This raises an important question: Is there a simpler and more automated way to detect bearing faults?
After reviewing numerous studies, the author found that resonant demodulation technology offers a promising solution for automatic diagnosis of rolling bearing damage. This method has proven to be both efficient and reliable, especially in detecting early-stage faults that may not be easily identified through conventional vibration analysis.
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**2. Principle of Resonant Demodulation for Rolling Bearing Damage Diagnosis**
When rolling bearings experience irregular damage—such as peeling, pitting, or cracks on the rolling elements, inner ring, or outer ring—shocks are generated. Although these shocks occur at a frequency similar to the overall vibration frequency, their energy is much smaller compared to the sustained energy of the vibration itself. This makes it difficult to detect them using standard frequency analysis.
Resonance is a natural phenomenon where a system amplifies specific frequencies. By utilizing this property, we can extract hidden impact information from the normal vibration signal. A high-frequency resonant system is set up, which filters out the low-frequency background noise and isolates the shock signals caused by bearing faults.
The process involves applying envelope detection and low-pass filtering to a high-frequency waveform that is excited by low-frequency impacts. The resulting resonant demodulation signal contains detailed fault information. Analyzing its amplitude and frequency spectrum allows us to identify the type and severity of the fault. This technique is illustrated in Figure 1.
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**3. Precise Diagnosis Using Resonant Demodulation Signals**
The key to achieving precise diagnosis lies in the Fourier transform of the resonant demodulation signal. This method effectively eliminates background noise and enhances the signal-to-noise ratio, allowing for accurate fault detection without the need for complex frequency domain analysis.
Unlike general vibration diagnosis, which only identifies the presence of a fault, resonant demodulation provides detailed spectral characteristics that help determine the type and severity of the fault. The "no fault, no spectral line" principle ensures that only real fault features appear in the spectrum, improving diagnostic accuracy.
The basic steps for automatic fault diagnosis using resonance demodulation are shown in Figure 2.
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**4. Implementation of Automatic Fault Diagnosis for Rolling Bearings**
When a rolling bearing experiences damage, it generates low-frequency impacts that excite high-frequency resonances. These resonances are then demodulated to produce a low-frequency signal rich in fault information. By analyzing the amplitude and frequency spectrum of this signal, clear peaks corresponding to the fault feature frequency and its harmonics can be observed. This makes it easy to pinpoint the location and type of the fault.
Since different types of rolling bearings have unique characteristic frequencies, it is impractical to test all possible failures. Therefore, an active diagnostic approach is used, where theoretical formulas are applied to calculate the expected fault frequencies and compare them with the actual spectrum obtained from the sensor.
The characteristic frequencies for various bearing faults are calculated as follows:
- **Cage failure frequency:**
$$
f_{cage} = \frac{N}{60} \cdot \left(1 - \frac{d}{D} \cdot \cos(\alpha)\right)
$$
- **Inner and outer ring failure frequencies (assuming the outer ring is stationary):**
$$
f_{inner} = \frac{N}{60} \cdot \left(1 + \frac{d}{D} \cdot \cos(\alpha)\right) \cdot RPM
$$
$$
f_{outer} = \frac{N}{60} \cdot \left(1 - \frac{d}{D} \cdot \cos(\alpha)\right) \cdot RPM
$$
- **Rolling element failure frequency:**
$$
f_{ball} = \frac{N}{60} \cdot \left(1 - \frac{d}{D} \cdot \cos(\alpha)\right) \cdot RPM
$$
Note: The negative sign indicates counterclockwise rotation.
In practice, there may be some frequency error between the calculated values and the actual measured spectrum. To account for this, a small frequency tolerance is usually applied during diagnosis.
The automated diagnostic implementation steps for rolling bearing faults are as follows:
1. Input the bearing type and related parameters.
2. The system calculates the characteristic frequencies and generates a fault frequency table.
3. Place the sensor and collect the vibration signal.
4. Perform spectrum analysis on the collected signal.
5. Compare the spectral peaks with the fault frequency table to identify the faulty bearing and output the results.
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**5. Conclusion**
Combining resonant demodulation with computer technology offers a simple, reliable, and cost-effective method for automatic diagnosis of rolling bearing faults. Compared to complex expert systems, this approach is user-friendly, easy to maintain, and ideal for field applications.
It is particularly useful in scenarios where multiple faults occur simultaneously, as traditional vibration analysis may struggle to distinguish between different sources of vibration. Resonant demodulation excels in such cases, providing accurate and clear fault identification.
This method is not limited to rolling bearings—it can also be applied to other equipment with impact-based failures, such as gears and shafts. Overall, resonant demodulation represents a powerful and versatile tool in modern condition monitoring systems.
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