0 1 Abnormal Rotational Sound Analysis and Diagnosis
Abnormal rotational noise detection and analysis is an analytical method that uses auscultation to monitor the operating condition of bearings. Common tools include long screwdrivers with wooden handles, and rigid plastic tubing with an outer diameter of approximately 20mm can also be used. Relatively speaking, using an electronic stethoscope for monitoring is more conducive to improving the reliability of the monitoring. When the bearing is in normal operating condition, it runs smoothly and easily without any pauses, producing a harmonious and noiseless sound; a uniform and continuous "whooshing" sound or a low "rumbling" sound can be heard. Abnormal sounds indicate the following bearing faults.
(1) The bearing emits a uniform and continuous "hissing" sound. This sound is produced by the rotation of the rolling elements in the inner and outer rings and includes irregular metallic vibrations unrelated to the rotational speed. This usually indicates insufficient grease in the bearing, which should be replenished. If the equipment is shut down for too long, especially in the low temperatures of winter, the bearing may sometimes emit a "hissing" sound during operation. This is related to the reduced radial clearance of the bearing and the reduced working penetration of the grease. The bearing clearance should be adjusted appropriately, and a new grease with a larger penetration should be used.
(2) The bearing emits a uniform, periodic "hooking" sound amidst a continuous "whooshing" sound. This sound is caused by scratches, grooves, and rust spots on the rolling elements and inner and outer raceways. The period of the sound is proportional to the bearing's rotational speed. The bearing should be replaced.
(3) The bearing makes an irregular, uneven "chattering" sound. This sound is caused by impurities such as iron filings and sand falling into the bearing. The sound intensity is relatively low and is not related to the rotational speed. The bearing should be cleaned and re-greased or the oil changed.
(4) The bearing makes a continuous and irregular "rustling" sound. This sound is generally related to the inner ring of the bearing being too loosely fitted to the shaft or the outer ring being too loosely fitted to the bearing bore. If the sound is loud, the bearing fit should be checked and any problems should be repaired in time.
0 2 Vibration signal analysis and diagnosis
Bearing vibration is highly sensitive to bearing damage; for example, spalling, indentation, corrosion, cracks, and wear will all be reflected in bearing vibration measurements. Therefore, by using specialized bearing vibration measuring instruments (such as frequency analyzers), the magnitude of vibration can be measured, and the specific nature of the anomaly can be inferred from the frequency distribution. The measured values vary depending on the bearing's operating conditions or the sensor's installation location, so it is necessary to analyze and compare the measured values for each machine beforehand to determine the judgment criteria. There are many techniques for detecting and diagnosing rolling bearing faults, such as vibration signal detection, lubricating oil analysis, temperature detection, and acoustic emission detection. Among various diagnostic methods, vibration signal-based diagnostic techniques are the most widely used. This technique is divided into two types: simple diagnostic methods and precise diagnostic methods. • Simple diagnostic methods utilize various parameters of the vibration signal waveform, such as amplitude, waveform factor, crest factor, probability density, kurtosis coefficient, etc., as well as various demodulation techniques, to make a preliminary judgment on the bearing to confirm whether a fault has occurred. • Precise diagnostic methods utilize various modern signal processing methods to determine the fault type and cause of the bearing that was considered faulty in the simple diagnostic method.
2.1 Simplified Diagnostic Method In the process of using vibration to perform simplified diagnosis of rolling bearings, the measured vibration values (peak value, effective value, etc.) are usually compared with a pre-defined judgment standard. Whether the measured vibration value exceeds the limit given by the standard is used to determine if the bearing has malfunctioned, and whether further precise diagnosis is needed. The judgment standards used for simplified diagnosis of rolling bearings can be roughly divided into three types:
(1) Absolute judgment standard: It is an absolute value used to judge whether the measured vibration value exceeds the limit;
(2) Relative judgment standard: The vibration test is performed on the same part of the bearing at regular intervals and compared in time. The vibration value under the condition that the bearing is fault-free is the standard. The diagnosis is based on the ratio of the measured vibration value to the benchmark vibration value.
(3) Analogy judgment standard: It is a standard that compares the vibration values of several bearings of the same model at the same location under the same conditions to make a judgment.
Absolute judgment criteria are standards developed based on prescribed testing methods. Therefore, it is essential to pay attention to their applicable frequency range and to conduct vibration testing according to the prescribed methods. No absolute judgment criterion applies to all bearings; therefore, a combination of absolute, relative, and analogous judgment criteria is generally used to obtain accurate and reliable diagnostic results.
The main methods for simple diagnosis are as follows:
(1) Amplitude value diagnosis method
The amplitude values mentioned here refer to the peak value XP, the mean value X (the average value over half a period for simple harmonic vibration, and the average value after absolute value processing for bearing impact vibration), and the root mean square value (effective value) Xrms.
This is the simplest and most commonly used diagnostic method, which diagnoses by comparing the measured amplitude value with the value given in the judgment criteria.
The peak value reflects the maximum amplitude at a certain moment, and therefore it is suitable for fault diagnosis with instantaneous impact, such as surface pitting damage.
The mean value is used for diagnosis in much the same way as the peak value. Its advantage is that the detected value is more stable than the peak value, but it is generally used for high speeds (such as above 300 r/min).
• The root mean square (RMS) value is averaged over time, making it suitable for diagnosing faults where the amplitude value changes slowly over time, such as wear.
(2) Probability density diagnostic method
The probability density curve of the amplitude of a fault-free rolling bearing is a typical normal distribution curve; however, once a fault occurs, the probability density curve may become skewed or dispersed.
(3) Kurtosis coefficient diagnosis method
For fault-free bearings whose amplitude follows a normal distribution, the kurtosis value is approximately 3. As faults appear and develop, the kurtosis value exhibits a similar trend to the crest factor. The advantage of this method is that it is independent of the bearing's speed, size, and load, and it is primarily suitable for diagnosing pitting faults.
(4) Waveform factor diagnostic method
The waveform factor is defined as the ratio of peak value to mean value (XP/X). This value is also one of the effective indicators for simple diagnosis of rolling bearings.
(5) Peak factor diagnostic method
The crest factor is defined as the ratio of the peak value to the root mean square value (XP/Xrms). Its advantage in simple diagnosis of rolling bearings is that it is unaffected by bearing size, speed, load, or changes in the sensitivity of primary and secondary instruments such as sensors and amplifiers. This value is suitable for diagnosing pitting faults. By monitoring the trend of XP/Xrms values over time, early prediction of rolling bearing faults can be effectively achieved, and the development trend of the fault can be reflected.
• When the rolling bearing is fault-free, XP/Xrms is a relatively small and stable value;
• When a bearing is damaged, it will generate an impact signal, and the peak vibration value will increase significantly. However, the root mean square value will not increase significantly at this time, so XP/Xrms will increase.
As the fault continues to expand and the peak value gradually reaches its limit, the root mean square value begins to increase, while XP/Xrms gradually decreases until it returns to the value before the fault occurred.
2.2 Precision Diagnostic Method
Rolling bearings exhibit a rich variety of vibration frequencies, including both low-frequency and high-frequency components, and each specific fault corresponds to a specific frequency component. The task of precision diagnostics is to isolate these specific frequency components using appropriate signal processing methods, thereby indicating the presence of a particular fault. Commonly used precision diagnostic methods include the following.
(1) Low-frequency signal analysis method
Low-frequency signals refer to vibrations with frequencies below 8kHz. Accelerometers are typically used to measure the vibration of rolling bearings, but for low-frequency signals, vibration velocity is analyzed. Therefore, the acceleration signal is first amplified by a charge amplifier and then converted into a velocity signal by an integrator. It is then passed through a low-pass filter with an upper cutoff frequency of 8kHz to remove high-frequency signals. Finally, frequency component analysis is performed to identify the signal's characteristic frequencies for diagnostic purposes.
(2) Demodulation analysis of medium and high frequency signals
The intermediate frequency (IF) signal ranges from 8 to 20 kHz, while the high frequency signal ranges from 20 to 80 kHz. Since acceleration can be directly analyzed from the IF and high frequency signals, the sensor signal, after passing through a charge amplifier, is directly filtered by a high-pass filter to remove low-frequency signals, then demodulated, and finally subjected to frequency analysis to identify the signal's characteristic frequencies.
Temperature analysis and diagnosis of bearing 0 3
The temperature of a bearing can generally be estimated from the temperature outside the bearing housing. However, it's even more accurate to measure the outer ring temperature directly using an oil hole. Typically, the bearing temperature rises slowly as the bearing begins to operate, reaching a stable state after 1-2 hours. The normal bearing temperature varies depending on the machine's heat capacity, heat dissipation, speed, and load. Improper lubrication or installation can cause a rapid rise in bearing temperature, resulting in abnormally high temperatures. In such cases, operation must be stopped, and necessary preventative measures taken.
High temperatures often indicate that a bearing is in an abnormal condition. High temperatures are also harmful to bearing lubricants. Sometimes bearing overheating can be attributed to the bearing lubricant. Prolonged operation of a bearing at temperatures exceeding 125°C will reduce its lifespan. Causes of high bearing temperatures include: insufficient or excessive lubrication, impurities in the lubricant, excessive load, bearing damage, insufficient clearance, and high friction from the oil seal, among others.
Therefore, continuous monitoring of bearing temperature is essential, whether measuring the bearing itself or other critical components. Any temperature change under constant operating conditions can indicate a malfunction. Regular bearing temperature measurements can be performed using a thermometer, such as an SKF digital thermometer, which accurately measures bearing temperature and displays it in °C or Fahrenheit. Critical bearings, meaning those that would cause equipment downtime if damaged, should ideally be equipped with temperature sensors. Under normal circumstances, bearings will naturally experience a temperature rise after lubrication or relubrication, which can last for one or two days.
0.4 Lubricant Analysis and Diagnosis Lubricant analysis utilizes ferrography, a technique particularly well-suited for identifying and predicting rolling fatigue. A portion of the lubricating oil from a rolling bearing is extracted as an oil sample. A high-gradient magnetic field is used to cause solid foreign matter contained in the oil sample to deposit on a glass slide according to its size ratio. The shape, size, color, and material of the foreign matter particles can be observed, clearly identifying the type of wear, predicting the machine's operating status, and promptly detecting potential problems. Ferrography is primarily designed to identify strong magnetic materials such as steel, but it also has excellent ability to identify non-ferrous metals such as copper, sand, organic matter, and sealing debris. When spherical steel-like particles with a diameter of 1-5 μm appear in the oil sample, fatigue microcracks have definitely begun to appear in the bearing. When fatigue spalling particles with a length-to-thickness ratio of 10:1 appear in the oil sample, and the length is greater than 10 μm, abnormal fatigue wear has begun in the bearing. When the length is greater than 100 μm, the bearing has failed. The third type of fatigue debris consists of fatigue flakes with a length-to-thickness ratio of 30:1, ranging in length from 20 to 50 μm. These flakes often contain voids. The number of these flakes increases significantly at the onset of fatigue, which, along with spherical particles , can serve as a marker of fatigue initiation. 0.5 Acoustic Emission Detection
The principle of acoustic emission detection technology is that when a material is subjected to external or internal forces and deforms or cracks propagate, the phenomenon of releasing strain energy in the form of elastic waves is called acoustic emission.
The technique of using instruments to detect and analyze acoustic emission signals and inferring acoustic emission sources from these signals is called acoustic emission detection technology. It utilizes the phenomenon that particles inside a material release strain energy in the form of elastic waves due to relative motion to identify and understand the internal state of a material or structure.
Acoustic emission signals are classified into two types: burst and continuous. Burst acoustic emission signals consist of pulses distinct from background noise and can be separated in time; individual pulses of continuous acoustic emission signals are indistinguishable. In fact, continuous acoustic emission signals are also composed of a large number of small burst signals, but they are too dense to be distinguishable.
When rolling bearings malfunction, both sudden and continuous acoustic emission signals can be generated. Sudden acoustic emission signals can be generated by the relative motion and friction between the contact surfaces of the bearing components (inner ring, outer ring, rolling elements, and cage), the Hertzian contact stress generated by friction, and faults caused by failure, overload, such as surface cracks, wear, indentation, grooving, seizing, surface roughness caused by poor lubrication, surface hardening caused by lubricating contaminant particles, and pitting caused by current passing through the bearing.
Continuous acoustic emission signals mainly originate from global failures caused by oxidative wear on the bearing surface due to poor lubrication (such as failure of the lubricating oil film or infiltration of contaminants in the grease), excessively high temperatures, and frequent local failures of the bearing. These factors cause a large number of sudden acoustic emission events in a short period of time, thus generating continuous acoustic emission signals.
During operation, rolling bearings may experience failures (whether surface damage, cracks, or wear) that cause elastic impacts on the contact surfaces, generating acoustic emission signals. These signals contain rich information about the contact and friction, and therefore acoustic emission can be used to monitor and diagnose rolling bearings.
