Introduction
This article summarizes five common methods and practical tips for bearing fault diagnosis, including listening tests, vibration analysis, temperature monitoring, lubricant analysis, and acoustic emission detection.
1. Acoustic Analysis of Abnormal Rotation Sounds
Listening-based detection analyzes the operating sounds of a bearing to assess its condition. Common tools include a long-handled metal screwdriver used as a stethoscope or a hard plastic tube about 20 mm in diameter. Electronic stethoscopes improve monitoring reliability. A healthy bearing runs smoothly and without stalling, producing a harmonious, noise-free sound—often a steady, continuous whooshing or a low rumble. Different abnormal sounds indicate specific faults:
- Uniform, continuous hissing: Caused by rolling elements rotating on the inner or outer race, producing irregular metallic vibrations that are independent of speed. This often indicates insufficient grease and requires replenishment. If the machine has been idle for a long time, especially in cold conditions, similar sounds can arise from reduced radial clearance or a grease hardening effect; adjust clearance and replace with grease of higher penetration.
- Periodic, regular knocking within a continuous whooshing: Caused by scratches, grooves, or corrosion on the rolling elements or raceways. The sound period is proportional to shaft speed. Replace the bearing.
- Irregular, nonuniform scraping: Caused by foreign particles such as metal chips or sand inside the bearing. Intensity is low and not related to speed. Clean the bearing and relubricate or replace the lubricant.
- Continuous, irregular rasping: Often related to excessive clearance between the inner ring and shaft or between the outer ring and housing. If sound intensity is high, inspect and repair the fits.
2. Vibration Signal Analysis and Diagnosis
Bearing vibration is sensitive to defects such as spalling, indentation, corrosion, cracks, and wear. Specialized vibration measurement equipment (frequency analyzers, etc.) can measure vibration amplitude and frequency distribution to infer fault types. Measured values vary with operating conditions and sensor location, so baseline measurements and comparative analysis for each machine are required. Common bearing diagnostic techniques include vibration signal detection, lubricant analysis, temperature monitoring, and acoustic emission. Vibration-based diagnostics are the most widely used and can be divided into simple diagnostics and advanced diagnostics.
2.1 Simple Diagnostic Methods
Simple diagnostics compare measured vibration values (peak, mean, RMS, etc.) against predefined criteria. If measured values exceed thresholds, further advanced diagnostics are warranted. Judgment criteria for simple diagnostics fall into three categories:
- Absolute criteria: Fixed limit values used to judge whether a measured vibration exceeds acceptable bounds.
- Relative criteria: Periodic vibration measurements at the same location are compared over time; the baseline is the vibration when the bearing was known to be healthy.
- Comparative criteria: Vibration values from several bearings of the same model under the same conditions are compared.
No single absolute criterion fits all bearings, so a combination of absolute, relative, and comparative criteria is typically used for accurate diagnosis.
Common simple diagnostic methods include:
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Amplitude-based diagnosis
Amplitude metrics include peak value XP, mean value X (for harmonic vibration this is the average over half a cycle; for impact-type vibration the mean of absolute values), and root-mean-square Xrms. This is the simplest and most common method, comparing measured amplitudes to threshold values.- Peak value is suitable for faults that produce instantaneous shocks, such as surface pitting.
- Mean value is similar to peak but more stable, useful at higher speeds (for example, above 300 r/min).
- RMS is a time-averaged measure, suitable for faults with slowly varying amplitude, such as wear.
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Probability density diagnosis
A healthy bearing's amplitude probability density typically follows a normal distribution. Faults cause skewness or spreading of the density curve. -
Kurtosis-based diagnosis
For a normally distributed healthy bearing, kurtosis is about 3. As faults develop, kurtosis changes similarly to the crest factor. This method is largely independent of bearing speed, size, and load, and is useful for diagnosing pitting-type faults. -
Crest factor diagnosis
Crest factor is defined as XP/X. It is an effective indicator for simple diagnostics. -
Peak-to-RMS ratio diagnosis
Peak-to-RMS ratio (XP/Xrms) is useful because it is not sensitive to bearing size, speed, load, or changes in the sensitivity of sensors and amplifiers. It is effective for pitting-type faults. Monitoring XP/Xrms over time enables early warning and reflects fault progression:- When a bearing is healthy, XP/Xrms is a small, stable value.
- When damage begins, impact signals increase peak values while RMS remains relatively unchanged, so XP/Xrms increases.
- As the fault grows, RMS increases and XP/Xrms decreases toward the healthy value.
2.2 Advanced Diagnostic Methods
Bearing vibration contains a wide range of frequency components; each specific fault corresponds to characteristic frequency components. Advanced diagnostics extract those components via signal processing to indicate specific faults. Common advanced methods include:
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Low-frequency signal analysis
Low-frequency refers to frequencies below 8 kHz. Acceleration sensors are typically used to measure bearing vibration, but low-frequency analysis is performed on vibration velocity. Acceleration signals are converted to velocity via charge amplifiers and integrators, then passed through a low-pass filter with an upper cutoff around 8 kHz to remove high-frequency content before frequency analysis to find characteristic frequencies. -
Mid- and high-frequency demodulation analysis
Mid-frequency ranges from 8 to 20 kHz and high-frequency from 20 to 80 kHz. Acceleration can be analyzed directly: the sensor signal is amplified, high-pass filtered to remove low-frequency content, then demodulated and frequency-analyzed to identify characteristic frequencies.
3. Temperature Analysis and Diagnosis
Bearing temperature can often be inferred from the outside of the bearing housing; when possible, measure the outer ring temperature directly through an oil port. Bearing temperature usually rises gradually after startup and stabilizes in 1-2 hours. Normal temperature depends on machine thermal capacity, cooling, speed, and load. Improper lubrication or installation can cause rapid temperature rise and overheating, in which case operation must stop and corrective action taken.
High bearing temperature typically indicates abnormal conditions and also degrades lubricant life. Prolonged operation above 125°C reduces bearing life. Causes of overheating include inadequate or excessive lubrication, contaminated lubricant, overload, bearing damage, insufficient clearance, or high friction from seals.
Continuous temperature monitoring is recommended for bearings and other critical components. Any temperature change under steady operating conditions can indicate a developing fault. Periodic temperature measurement can use thermometers; for critical bearings that could cause machine downtime if they fail, consider installing temperature detectors. Note that a natural temperature rise occurs immediately after fresh lubrication and may persist for one or two days.
4. Lubricant Analysis
Lubricant analysis uses ferrography techniques that are suited to identifying and predicting rolling fatigue. A sample of bearing lubricant is drawn and passed through a high-gradient magnetic field so ferrous particles deposit on a glass slide by size. Observing particle shape, size, color, and composition helps determine wear types and predict machine condition.
Ferrography primarily targets ferrous particles but can also identify nonferrous metals, sand, organics, and seal fragments. Typical indicators:
- Steel spherical particles 1-5 μm in diameter indicate the beginning of fatigue microcracks.
- Fatigue flake particles with length-to-thickness ratio about 10:1 and length greater than 10 μm indicate abnormal fatigue wear; lengths greater than 100 μm indicate bearing failure.
- Thin fatigue flakes with length-to-thickness ratio about 30:1, lengths of 20-50 μm, often with voids, increase in number as fatigue begins and, together with spherical particles, signal fatigue onset.
5. Acoustic Emission Detection
Acoustic emission (AE) refers to the release of strain energy in the form of elastic waves when a material deforms or cracks. AE detection involves measuring and analyzing these signals to infer the source and internal condition of a structure. AE signals can be impulsive or continuous. Impulsive AE consists of pulses distinguishable from background noise; continuous AE appears as an unresolved series of small impulses, which in practice is a dense train of impulsive events.
In poorly operating rolling bearings, both impulsive and continuous AE can occur. Sources of impulsive AE include relative motion and rubbing between bearing components (inner ring, outer ring, rolling elements, cage), Hertzian contact stresses, and faults such as surface cracks, wear, indentations, spalling, seizure, contaminated lubrication causing rough surfaces, hard-edged contamination particles, and current-induced pitting. Continuous AE typically stems from lubrication failure (loss of oil film or ingress of contaminants into grease) causing oxidational wear and global damage, elevated temperature, or frequent local faults—these produce many closely spaced impulsive AE events that appear continuous.
Because bearing faults produce elastic impact at contact surfaces that generate AE rich in rubbing and impact information, acoustic emission can be used effectively to monitor and diagnose rolling bearing conditions.
