The oil-line electrostatic sensor (OLS) is a developing debris monitoring sensor. Previous work has shown that electrostatic charge signals can indicate the debris by calculating the Root Mean Square (RMS) value or the correlation-based indicator, but the precision of these methods is not high. This paper further developed the more accurate methods to obtain detailed debris information. Firstly, to interpret the monitoring principle of OLS and provide the guidance for developing the debris recognition methods, this paper analyzed the possible charge sources in the lubrication system and obtained the characteristics of the OLS by establishing its mathematical model. Further, a new OLS test rig was designed and verified the correctness of the sensor’s characteristics and its mathematical model. Based on the characteristics of the sensor, two new debris recognition methods were proposed. Finally, the effects of the new debris recognition methods were verified by the practical industrial gearbox bench test. Results showed that, compared to the traditional methods, the new methods could recognize the debris effectively and provide more detailed information of the debris.
Oil monitoring of lubrication system is an important way to monitor the friction pairs, such as bearings and gears [
Wood from the University of Southampton first proposed the electrostatic induction method at the World Tribology Conference in 1997 trying to monitor the oil-lubricated gear gluing or adhesive wear [
A series of experiments were designed and attempt to explain the mechanism of charge generation in the lubrication system. Through experiments, Harvey found the debris produced by adhesive wear to be positively charged and the magnitude of charge was directly related to the total volume loss [
Compared with the study of the charge generation mechanism in the lubrication system, there is much less research on the sensor system itself, such as the model and the sensing characteristics of the sensor. However, in other areas, there are some similar studies. In the field of gas-path electrostatic monitoring, Honor proposed a simple equation of a rob-shaped electrostatic sensor to indicate the relationship between the measured charge and the real signal [
In the field of OLS application, the most famous experiment is the engine test conducted by Pratt and Whitney [
To summarize, it can be seen from the above reviews that although the mechanisms of charge generation in the oil system are still not fully understood, it is believed that the debris will carry a certain amount of charge in the lubricating oil system. The characteristics of the OLS are essential to the application of the sensor, but only a few articles have been reported, which needs further research. Although several simulation experiments have been carried out, including the engine bench experiment, the data analysis methods did not reach the level of debris recognition. Some researchers proposed the correlation-based and RMS-based parameters to characterize the debris, but their physical meanings are unclear and the effects are not so good, which leads to the limitation of the sensor’s applications.
This study aims to improve the oil-line electrostatic monitoring technology further and try to solve the above problems by detailing the research on the characteristics of the sensor and its recognition methods of debris. To this end, by constructing the detailed mathematical model of the OLS, the characteristics of the sensor and its change rules were analyzed in-depth first, which could provide the guidance for developing the debris recognition method. Then, a new OLS calibration device was constructed by creatively using a charged oil droplet to simulate the charged abrasive debris, which has the ability to verify the established mathematical model and the correctness of the derived sensor characteristics. Thirdly, according to the characteristics of the sensor signal, a new method for identifying abrasive particles suitable for practical applications and an abnormal monitoring method based on electrostatic signals are proposed. Moreover, with the practical industrial gearbox bench test, the proposed new methods along with the traditional methods for debris recognition are analyzed and compared. The rest of this paper is organized as follows. In Section
The research on the principle of OLS mainly includes the study of the charge generation mechanism in the lubrication system and the sensor’s characteristics, while establishing the mathematical model of OLS is a good way to determine its characteristics. Therefore, this section will discuss the three parts separately.
To detect the debris successfully, the electrostatic monitoring technology under the lubrication condition requires an in-depth study of the charging mechanism of the debris and the analysis of other possible charge sources in the oil system. The exact physical reasons for these charge generation events are still under investigation, but evidence shows that they are linked to the onset of debris formation and tribo-charging [
When the lubrication failure or oil film rupture occurs, the friction surface will contact directly. In severe cases, gluing will occur and lead to destructive failure damage. During this case, the touching asperities adhere together, and the plastic shearing removes the tip of the softer asperities, leaving them adhering to the harder surface. Subsequently, the asperities are detached and contribute to the generation of debris [
In the lubrication oil system, besides the formation of debris accompanied by the generation of charge, studies show that the oil’s relative motion over a solid surface will also lead the oil to carry some charge, which is a well-known electrochemical concept named tribo-charging [
As Figure
The structure of OLS.
To understand the characteristics of the electrostatic sensor deeply, the relationship between the debris charge and the electrostatic sensor signal needs to be further studied, which can be easily obtained by establishing the sensor’s mathematical model. Based on the physical structure shown in Figure
Firstly, to build the model, the spherical coordinate system was established as shown in Figure
Spherical coordinate system.
By Coulomb’s law, the electric field intensity at
The charge
To get the entire induced charge on the electrode, a new coordinate system was established, whose origin is the center of the probe and reference plane is the cross section, as shown in Figure
The relative position of point charge and electrode.
Subsequently, the induced charge on the entire electrode is
Substituting (
Equation (
The simulated signal waveform.
Since this paper studies the identification method of debris, it is not necessary to consider the influence of the sensor’s structural parameters in the model. For the sensor whose structural parameters have been determined, the induced charge is only related to the characteristics of debris (radial position, speed, and charge). With the mathematical model, we can simulate the corresponding relationships qualitatively. In this paper, the electrode’s length and radius of the sensor for simulation analysis are 70mm and 19mm, respectively.
From the mathematical model (
The relationship between the debris charge and the signal.
In model (
The relationship between radial position and signal.
It can be seen from Figure
In model (
The relationship between the velocity of the debris and the signal.
Through the above analysis, the characteristics of the sensor can be concluded. Firstly, the amplitude of the signal has a proportional relationship to the charge of the debris and a nonlinear relationship with the radial position of debris. The amplitude of the signal has nothing to do with the flow rate and specifically, the relationship between the amplitude of the signal
In order to verify the correctness of the mathematical model of the sensor and the characteristics derived from it, an OLS verification test rig was designed, which can also be used to calibrate the sensor further. With the test rig, three sets of experiments were carried out to verify the relationship between the input and the output of the sensor in three different conditions, which were different sensory charge, different positions and different speeds.
As Figure
The calibration test rig.
Different voltages were applied to the oil drop generation and charge device to produce oil droplets with different charge. The voltage increased from 0 to 500V with the step 50V. The equivalent speed of the oil droplets was set to 2.96 m/s. The outputs of the Faraday Cup and the electrostatic sensor are shown in Figures
The relationship between the oil droplet charge and the applied voltage.
Three raw signal’s waveform from different oil droplets.
The relationships between outputs and different input charge.
As can be seen from model (
The relationships between the outputs and radial positions.
To verify the effect of speed on the electrostatic signal, the supply voltage was set to 300 V and the height of the sensor was adjusted so as to obtain different velocities of oil droplets, where the equivalent velocities were
The relationships between the outputs and different velocities.
Through the experiments, the correctness of the characteristics of the sensor and the mathematic model was verified. Thus, the debris can be identified by the electrostatic sensor’s signal with the parameters of amplitude and pulse width. If there is debris, a signal with a certain amplitude and pulse width will be produced, which may contribute to the changes of some features that can be used to indicate the debris. Therefore, the next part will focus on the feasibility of electrostatic sensors in practical applications and the development of some methods sensitive enough to identify the debris in practical applications.
The verification experiment in the lab proved the correctness of the principle of OLS. However, due to the differences between the industrial environment and the laboratorial environment, such as the interference problem, in order to further validate the feasibility of the OLS’s application in the industrial environment, with a gearbox manufacturer’s bench test rig, the experiment on the industrial gearbox were carried out. Meanwhile, two kinds of effective debris recognition methods in the industrial environment were developed to analyze the signals.
The experiment was performed in a practical industrial gearbox bench test rig. As shown in Figure
The gearbox test rig.
The experiment was a fatigue life test to evaluate the occurrence of wear, pitting and gluing of the tooth surface when the lifetime reached 230 h (13800 min). The input speed, input torque and output torque of the gearbox were 1500 rpm, 4900 Nm and 105000 Nm respectively. The process was divided into four stages and each stage accounted for a quarter of its lifetime. The gear was inspected after each stage. The sensor began to collect the data at the same time when the gearbox started. All data was stored on the computer for further analysis.
According to the characteristics of the debris’ signal, the debris can be directly identified by the amplitude and pulse width of the signal. However, in the industrial environment, the interference could be very serious, which may lead to the low accuracy of recognition. Therefore, a new recognition method based on two sensors was developed. As shown in Figure
The simulation waveforms on the two sensors induced by the debris.
The debris recognition method based on amplitude and pulse width.
Firstly, to distinguish between the debris signal and the random noise signal, an amplitude threshold is set according to the noise level, and the signal above the threshold is considered to be the potential debris signal.
For the signal beyond the amplitude threshold, further identify its peak, and then count the signal sequence backward until 0.1 peak values. The resulting count value multiplied by twice the sampling interval is the pulse width of the debris signal.
According to the pulse width, determine whether the signal is caused by debris or interference. For interference, its pulse width is very short, while, for debris, the pulse width has a certain range corresponding to flow rate, which can be determined by the analysis of its model and experiments. The potential debris signal that has the certain range of pulse width is set as the Important Potential Debris Signal (IPDS).
Verify the IPDS with the two sensors. As the debris passes orderly through the first sensor and the second sensor, there is a delay time between the occurrence times of the feature signal in the two sensors. By looking for the IPDS in the two sensors within the delay time range, the result can be further confirmed and the final recognition results are obtained.
In the case when multidebris passes the sensor at the same time, the superposition of the signal will cause the pulse width to be widened (out of the range determined by the analysis of its model and experiments) and thus be isolated to provide richer identification information. Here, we use a formula to correct large pulse width signals:
As analyzed above, the debris is correlated to the pulse components in the signal. As the debris pulse will cause the extreme value in the corresponding RMS value, the extreme RMS value can also reflect the debris’ generation. General Extreme Value Theory is a branch of statistics concerning data with unusually low or high values, i.e., data that lies in the tails of the distribution [
As shown in Figure
The distribution of the extreme RMS value.
Calculate the RMS value of the signal every 100ms, as the duration time of the pulse in this experiment is around 100ms. Then set the interval to select the extreme value of RMS, where the interval is 1s. At last, set a time window width to calculate the extreme value’s distribution.
Calculate the extreme RMS value’s distribution in each time window of the signal sequence. According to the analysis and statistical results, the distribution of the extreme RMS value can be fitted by the GEV distribution.
Consider the beginning phase as a default healthy running state, whose distribution can be taken as a reference distribution. Then evaluate the differences between all the distribution with the reference distribution and take the results as an indicator of the occurrence of debris. Specifically, as the blue line shown in the Figure
In this experiment, the inspections of the first two stages (0~3450 min and 3451~6900 min) did not find any failure, but after the third stage (6900~10350 min), a slight pitting within the acceptance range occurred. After the experiment, the inspection found the pitting area expanded. By comparing to the inspection results, the effectiveness of the developed two new debris recognition methods was evaluated. Simultaneously, this part also calculated the RMS value of the signal and the cross-correlation function of the two sensors, which were used by the previous researchers. By comparing the results of all the four methods, the effectiveness of each method was researched.
The RMS value can indicate the charge level in the oil flow. Over a period of time, the increased amount of debris will make the electrostatic sensor sense more charge, which will also contribute to the higher RMS value. According to this, this method calculated an RMS value of the signal every minute. From the trend of the RMS value and its abnormal change point, some debris information can be extracted. As shown in Figure
The RMS value of the signal.
Two electrostatic sensors were used to deduce velocity information related to the oil flow. Typically, this is evidenced by generation of debris, sufficient quantities of which will influence the flow quality monitored by the OLS [
The accumulated number of debris calculated by cross-correlation function.
The result of this new method is shown in Figure
The amount of debris per minute counted by pulse width.
Compared with the correlation-based method, the debris information extracted by the pulse width is more detailed. The correlation-based method can only calculate the correlation coefficient over a period of time, which means it can only characterize the presence of debris for a period of time instead of identifying the amount of debris. However, through the pulse width, all the potential debris can be identified. Thus, it can provide more information, such as severity of wear.
The results of this method are shown in Figures
The trend of the mean value.
The trend of CV.
To summarize, only the pulse width-based method and the GEV distribution-based method can indicate the running-in phase lying between around 0~1800 min. During the period 2000~6000 min, all the results of the four methods remained stable, which may correspond to the stable operation period. Beginning at around 6300 min, three methods except the correlation-based method experienced the abnormal increase successively, which was considered to be related to the occurrence of the initial fault. Until 7800 min, the correlation-based method monitored its first anomaly. However, the inspection after the second stage (3451~6900 min) didn’t find any fault, the reason of which might be that the fault was in its early stage and cannot be distinguished by the naked eye. Then, the RMS value (7200~11000 min) and correlation-based method (8200~11200 min) underwent a relatively stable phase, but the results identified by the pulse width and the GEV distribution continued to indicate debris, which may be related to the fine debris in this stage. From around 11000 min, all methods had an upward trend correspond to the failure, which were also proved by the final inspections.
Among the four methods, RMS can indicate the overall charge level in the oil-line, but cannot indicate the instantaneous generation of the debris. Moreover, since the charge sources in the oil are complex, the OLS signals and its RMS value are susceptible. The GEV distribution-based method is an improvement of the RMS-based method. The mean value of the GEV distribution can show the trend of the overall charge, which is similar to the RMS value. However, in addition, the GEV distribution can also reflect the changes of the extreme value in the signal, which is usually corresponding to the debris. Therefore, the GEV distribution-based method can indicate the debris better in each period. RMS-based method and GEV distribution-based method are both based on the statistical analysis of OLS signals to reflect the debris indirectly. While the cross-correlation-based methods and pulse waveform-based methods are intended to directly identify the debris. For the correlation-based method, because of its principle, it is ineffective for fine debris monitoring and situations where the amount of debris is low. What's more, the correlation-based method can only judge if there is debris occurring in the period of time, but the amount of debris can’t be identified. The experimental results also showed the information obtained by the correlation-based indicator was much less than that of the other three methods. The method of pulse width can identify all the debris theoretically by directly recognizing the amplitude and pulse width of the debris signal. The experimental results proved the effectiveness of this method. Compared to the GEV distribution, the method based on identifying the pulse width is more direct in debris recognition and better in real time, but the GEV distribution can provide the information about the overall charge of the oil flow.
This paper further researched the OLS by detailing the characteristics of the sensor and developing two more reasonable and precise debris recognition methods based on the characteristics of the sensor. Through developing the detailed mathematical model of the OLS, this paper proved that the amplitude and pulse width of the signal could be used to indicate the debris and their change rules were obtained as the characteristics of the sensor. Then a new OLS test rig was developed, which creatively used an oil drop charge device to simulate the charged debris. The OLS test rig verified the correctness of the mathematical model and the characteristics of the OLS, the experimental width data of which could fit the simulation data very well. The practical application of OLS in the gearbox evaluated the feasibility of OLS and the two new debris recognition methods. Compared to the traditional methods, the new methods were proved to be able to provide more information of debris and improve the sensor detection capability.
However, as the proposed methods use the raw signals in the time domain directly, only the signals from the stable working conditions of the gearbox can be used so as to ensure the accuracy of the recognition result. In addition, the methods put forward in this paper only focused on the identification of the occurrence and the amount of debris, whereas the morphological information was not involved too much. In the future, the relationships between the signals and the size, the material properties, and other morphological information will be further studied, which can make the electrostatic more effective on fault diagnosis.
The experimental data used or analyzed during the current study are available from the corresponding author on reasonable request.
The authors declare no conflicts of interest.
This work was supported in part by Major Program of Civil Aviation Joint Funds of China (U1733201) and Jiangsu Scientific Research Innovation Plan (KYCX 16-0386).