The advancement of semiconductor technology and capabilities provides new opportunities for industrial applications (especially status monitoring solutions) detection, measurement, interpretation, and analysis data.The combination of a new generation of sensors based on MEMS technology is combined with the advanced algorithm of diagnostic prediction applications, which expands the opportunities to measure various machines and improve capabilities, help efficient monitoring equipment, extend normal operating time, enhance the quality of the process, and increase production.
In order to achieve these new capabilities and obtain the benefits of status monitoring, the new solutions must be accurate, reliable, and stable, so that real -time monitoring can be extended to the basic detection of potential equipment faults, and provides insight and operational information.The combination of the performance of the new generation of technology with the system -level insight will help people understand the applications and requirements required to solve these challenges more deeply.
Vibration is one of the key elements of machine diagnosis and has been reliable to monitor the most critical equipment in various industrial applications.There are a large number of documents to support the various diagnosis and prediction capabilities required to achieve high -level vibration monitoring solutions.However, there are not many documents about the relationship between the performance parameters of the vibration sensor (such as bandwidth and noise) and the final application failure diagnosis ability.This article introduces the main machine failure type in industrial automation applications, and determines the key performance parameters of the vibration sensor related to specific faults.
The following focuses on several common types of faulty types and their characteristics, so that some key system requirements that must be considered when understanding the development status monitoring solution.The type of failure includes but is not limited to unbalanced, unpopular, gear failure, and rolling bearings defects.
Incadence refers to the uneven quality distribution, which will cause the load to deviate from the rotation center.The system imbalance can be attributed to improper installation (such as the bias of the axis), the system design error, component failure, and even the accumulation of debris or other pollutants.For example, the built -in heat dissipation fan of most inductive motors may become unbalanced due to uneven accumulation of dust and oil or damage to the fan blades.
The unbalanced system will produce too large vibrations. These vibrations will mechanically coupling other components in the system, such as bearings, couplets and loads, which may cause components that are in a good operating state to accelerate and deteriorate.
Increased overall system vibration may indicate a potential failure caused by an unbalanced system, but the root cause of the increase in vibration needs to be diagnosed by frequency domain analysis.The unbalanced system generates a signal with the system's rotation rate (usually referred to as 1 ×).2 .1 × component usually exists in the frequency domain. Therefore, the unbalanced system can be recognized by measuring the amplitude of 1X harmonic.If the amplitude of 1 × is higher than the baseline measurement and the harmonic is far less than 1 ×, there may be an uneven system.The component of horizontal and vertical phase shift vibration may also appear in an unbalanced system1。
The noise must be very low to reduce the effects of the sensor and support the detection of a small signal generated by an unbalanced system.This is very important for sensors, signal conditioning and collection platforms.
In order to detect tiny imbalances, the collection system requires sufficient resolution to extract signals (especially baseline signals).
In addition, sufficient bandwidth is needed to capture sufficient information (not only the rotation rate) to improve the accuracy and reliability of diagnosis.1 × harmonic may be affected by other system faults, such as not accurate or mechanical loosening, so the harmonic of analyzing rotation rate (or 1 × frequency) can help distinguish between system noise and other potential faults1EssenceFor slow rotation machines, the basic rotation rate may be much lower than 10 RPM, which means that the low -frequency response of the sensor is crucial to capture the basic rotation rate.ADI's MEMS sensor technology can detect signals as low as DC, and can measure slow rotating devices. At the same time, wide -band width can also be measured to obtain higher frequency content related to bearing and gear box defects.
Figure 1. Increasing the amplitude of the rotation rate or a frequency of 1 × may mean an unbalanced system.
S, as its name implies, when the two rotation shafts are not on time, the system will not be accurate.Figure 2 shows an ideal system that starts from the motor, and then the shaft and coupling, until the load (this example is a pump).
Figure 2. The ideal right system.
It is not possible to occur in the parallel direction and angle direction, or it can be a combination of the two (see Figure 3).When the two axes are dislocated in the horizontal or vertical direction, it is called parallel to be unpopular.When one of the axes and the other form an angle, it is called the angle that the angle is not aligned2。
Figure 3. Different examples of alignment, including the (a) angle, (B) parallel or the combination of the two.
The failure error may force the component to work under the stress or load higher than the initial design ability, which affects a larger system and eventually leads to premature failure.
The second harmonics of the system rotation rate are not usually manifested as a system rotation rate, which is called 2 ×.The 2 × component does not necessarily exist in the frequency response, but when it exists, its relationship with 1 × can be used to determine whether there is an unprepared.The increased error can be motivated to 10 ×, depending on the type, measurement location, and direction information of the non -aligned type, measurement location, and direction1EssenceFigure 4 highlights the characteristics of the potential failure to target failure.
In order to detect small non -alignment, low noise and high resolution are required.The machine type, system and process requirements, and rotation rates determine the non -alignment tolerance.
In addition, sufficient bandwidth is needed to capture the full frequency range to improve the accuracy and reliability of diagnosis.1 × harmonic may be affected by other system failures, such as unpopularity, so analyzing the harmonic of 1 × frequency helps to distinguish other system failures.This is especially suitable for higher speed machines.For example, in order to accurately and reliably detect imbalance, machines (machine tools, etc.) with a speed of more than 10,000 RPM usually require high -quality information of more than 2 kHz.
Multi -directional information can also improve the accuracy of diagnosis and help in -depth understanding of the types of unprepared errors and the direction that is not accurate.
The combination of system phase and directional vibration information can further improve the diagnosis of unprepared errors.Different points on the measuring machine and determine the differences between or between the phase measurement values or the entire system, which helps to deeply understand the combination of unprepared angle, parallel or two types of unpatient type1。
Rolling component bearing defects are usually the illusion of stress or lubrication problems caused by mechanical. These problems produce small cracks or defects in the mechanical parts of the bearings, resulting in increased vibration.Figure 5 provides some examples of rolling component bearing and shows several possible defects.
Rolling component bearings are used almost all types of rotating machinery. From large turbines to slow -speed rotating motors, from relatively simple pumps and fans to high -speed CNC spindle.Bearing defects may be signs of lubricating pollution (Figure 5), improper installation, high -frequency discharge current (Figure 5), or increased system load.The failure may cause disaster system damage and have a significant impact on other components.
There are multiple technologies to diagnose bearings faults, and due to the physical characteristics behind the bearing design, the defect frequency of each bearings can be calculated based on the shape, rotation speed, and defect type of bearings, which helps diagnostic faults.The frequency of bearing deficiency is shown in Figure 6.
The analysis of the vibration data of specific machines or systems often depends on the combination of time domain and frequency domain analysis.Time domain analysis can be used to detect the trend of overall vibration level of the system.However, this analysis contains very little diagnostic information.The frequency domain analysis can improve the diagnostic insight, but due to the effects of other system vibrations, the frequency of faults may be complicated.
For early diagnosis of bearing defects, the use of harmonic frequency of defect can identify early or new faults, thereby monitoring and maintaining it before the disaster failure.In order to detect, diagnose, and understand the systemic effects of bearing faults, technologies such as packaging detection (as shown in Figure 7) are combined with spectrum analysis in the frequency domain, which can usually provide more insightful information.
Low noise and high resolution are essential for early bearing defects.When the defects have just emerged, the range of defect characteristics is usually low.Due to the tolerance of the design, the inherent mechanical sliding of the bearing will spread the amplitude information to multiple warehouses in the bearing frequency response, thereby further reducing the amplitude of the vibration. Therefore, low noise is required to detect the signal earlier2。
Bandwidth is essential for early detection of bearing defects.During the rotation, each time the impact defect, the pulse containing high -frequency content is generated (see Figure 7).Monitoring of bearing deficiency frequency (rather than rotation rate) monitoring can be found to find these early failures.Due to the relationship between the bearing deficiency frequency and the rotation rate, these early characteristics can appear within the range of thousands of Hzz, and extend to 2 outside the range of 10 kHz to 20 kHz.Even if it is low -speed equipment, the inherent nature of bearing defects requires broad band width to detect the defects early to avoid the effects of system resonance and system noise (will affect the lower frequency band)3。
Dynamic range is also important for bearing defect monitoring, because system loads and defects may affect the vibration of the system.Increased load can lead to an increase in force on bearing and defects.Bearing defects will also have impact, stimulate structural resonance, zoom in the vibration of the system and sensor2EssenceAs the machine is stopped/starts or the speed of normal operation rises and decreases, the speed of change will create potential opportunities for system resonance, leading to a higher vibration vibration4EssenceThe saturation of the sensor may cause information loss and misdiagnosis, and even damage the sensor element in some technologies.
Gear faults usually occur in the tooth festival of the gear mechanism, reasons for fatigue, peeling or erosion.It is manifested by cracks or metals on the teeth.The causes of wear are wear, overload, poor lubrication, and gaps, and occasionally caused by improper installation or manufacturing defects.5。
Gear is the main component of power transmission in many industrial applications, and is afforded with considerable stress and loads.The health of the gear is critical to the normal operation of the entire mechanical system.There is a well -known example in the field of renewable energy, causing the biggest factor of wind turbine to stop (and the corresponding income loss) is the failure of the multi -level gear box in the initiative system.5EssenceSimilar considerations are also applicable to industrial applications.
Because it is difficult to install the vibration sensor near the fault, and the existence of a considerable background noise caused by a variety of mechanical incentives in the system, the detection of gear faults is tricky.This is especially true in a more complicated gear box system. There may be multiple rotation frequency, gear ratio and meshing frequency6EssenceTherefore, the detection of gear faults may adopt a variety of complementary methods, including sound launch analysis, current feature analysis, and oil residue analysis.
In terms of vibration analysis, the acceleration meter is usually installed on the gear box shell. The main vibration mode is axial vibration7EssenceThe frequency of vibration characteristics generated by healthy gears is the so -called gear meshing frequency, which is equal to the product of the axial frequency and the number of gear tooth.There are usually some modulation bands related to the manufacturing and assembly tolerance.These situations of health gears are shown in Figure 8.When a local failure such as tooth cracks occurs, the vibration signal in each rotation will include a mechanical response of the system on a relatively low -energy short -term impact.This is usually a low -amplitude broadband signal, which is generally considered to be non -periodic and non -static7,8
Due to these characteristics, only the standard frequency domain technology cannot accurately identify gear failure.Because the impact energy is included in the band modulation, which may also include the energy from other gears and mechanical parts, the spectrum analysis may not be able to detect the early gear failure.Time domain technology (such as time synchronization average) or mixed domain method (such as sub -wave analysis and package demodulation) is generally more suitable9。
Generally speaking, the wide band wide is very important for the detection of gear failure, because the number of gear teeth is multiplied in the frequency domain.Even for relatively low -speed systems, the required detection frequency range will quickly rise to the KHz region.In addition, local faults have further expanded bandwidth requirements.
For a variety of reasons, resolution and low noise are extremely critical.It is difficult to install the vibration sensor near a specific faulty area, which means that the mechanical system may cause a high degree of attenuation of the vibration signal, so it is important to detect low -energy signals.In addition, because the signal is not a static cycle signal, it cannot rely on the standard FFT technology of extracting low signals from the high base noise, and the base noise of the sensor itself must be very low.This is especially in the environment that mixes multiple vibration characteristics of different components.In addition to these considerations, the importance of early testing is not only for asset protection, but also for signal conditioning.It has been proven that the situation of single tooth break fault compared with the failure of two or more tooth breaks, the severity of the former may be higher, which means that it may be relatively easy to detect early detection.
Although it is common, unbalanced, unpopular, rolling component bearing defects, and gear tooth fracture failure is only a few of many types of faulty types that high -performance vibration sensors can detect and diagnose.The combination of higher sensor performance with appropriate system -level considerations will help achieve a new generation of status monitoring solutions, and let people understand the mechanical operations of various industrial equipment and applications.These solutions will change the maintenance of maintenance and the operation of the machine, eventually reduce the stop time, improve efficiency, and make the next generation of equipment new capabilities.
Table 1. requirements for each sensor parameter
For Table 1, it is generally believed that the low bandwidth is less than 1 kHz. The medium bandwidth is between 1 kHz and 5 kHz, and the high bandwidth is greater than 5 kHz.Low noise density greater than 1 mg/√Hz moderate noise density is 100 μg/√Hz to 1 mg/√Hz, the high noise density is less than 100 μg/√Hzz.Low dynamic range is less than 5 g, Medium dynamic range in 5 g to 20 gBetween, the high dynamic range is greater than 20 g。
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