We present in this work a qualitative experimental study aiming to compare two bearing monitoring methods. One is based on an electrical probing and the other one on vibrational measurements . Electrical and vibrational signal s were acquired according to rotation speed s and radial load s. The electrical method seems to show a complementary approach open ing new ways of monitoring . 1. INTRODUCTION Bearings are one of the most important and f requently used components in rotating machinery, whether in the industrial or transport fields, and present a large range of various appli cations. An undetected defect in a bearing can lead to catastrophic economic and human consequences: statistical studies show that bearing defect s represent more than 40% of mal function in rotating machinery [1-4]. To respond to these safety and economic imperatives, there has been substantial amount of research on the monitoring and the diagnosis of bearing in order to provide and to improve new condition monitori ng techniques . Many experimental methods and various types of signal analysis ha ve been developed for the detection and the diagnosis of bearing defects, such as vibratory analysis [5-8], acoustic emission (AE) [9, 10], stator current analysis [11, 12], thermal and lubrication analysis [ 13]. Over the past decade, several studies have clearly established the relevance of an electrical method which probes directly the contact in order to highlight the presence of a defect [14-16] or to provide information on the contact degradation [17]. This method is based on the analysis of the voltag e noise across the system which is generated by the contact conditions fluctuation during - rolling or sliding - motion , significant of the quality of the contact [18, 19]. In this case, c ontact quality refers to an acceptable electromechanical contact condition defined by the specifications of an application. The monitoring of bearing s lies on signal processing techniques in both time and frequency domains that can be used as well to process the electrical signals as the vibrational ones. In order to highlight the contributions of the electrical method , this paper analyze s data from electrical probing and vibrations analysis through their respective spectral and statistical properties . 2 2. MATERIAL AND METH ODS 2.1 Material The studied roller bearing is a NU206ECP from SKF ( fig. 1 , Table 1 ). This bearing consist s in thirteen cylindrical steel roller with a diameter of 9 mm and a length of 10 mm, which are held by a cage made of reinforced glass fibre “PA66 ”. According to the manufacturer t he material of each part of the bearing is an AISI 52100 (100C6) steel - 1% carbon and 1.5% chrome. The contacting surfaces of the inner ring and a roller were observed using a SEM ( Quanta 200FEG, with a micro -analyzer X INCA Oxford type SDD 80 mm2) and are respectively shown in figure 2 a and 2b. The roughnes s of those surfaces were estimated by contact profilometry using a “DektakXT Stylus Profiler” from Brucker with a 2µm stylus at 3mg of load . A typical measurement is shown on figure 3 . The roughness RMS is computed from a dozen locations for each element and is estimadted to 25 nm (±4 nm) for the roller and 49 nm (±10 nm) for the inner ring ( the roughness of the outer ring is assumed to be the same). Fig. 1: NU206 ECP roller bearing Symbol Quantity Values 𝐷 Outer diameter 62 mm 𝑑 Inner diameter 30 mm 𝐷𝑚 Pitch diameter 46.5 mm 𝑍 Number of roller 13 𝑑𝑏𝑎𝑙𝑙 Roller diameter 9 mm 𝜑 Angle 0° Table 1: Characteristics of the NU206 roller bearing 2.2 Apparatus The apparatus is made of a metallic housing through which a shaft is pl aced and held by two bearings (in red on fig. 4 ). Bearings are mounted with a sliding assembly and a nut tightens the inner ring on the shaft. The shaft is connected to a brushless AC servo motor ( 3000 rpm, 98V, 1.6Nm/A - ref. 190UMB301CACAA). A load is applied at the end of the shaft using a hydraulic cylinder, thus exerting a radial force on bearing s (black arrow on fig. 4). Note that the alignment of the shaft has been checked and is less than 0.1 mm. Electrical measurements are made using a Keithley SourceMeter 2611B . A current flows from the first bearing to the second one via the shaft and the v oltage is measured between each end ( fig. 5 a). Bearings are isolated from the metallic housing using an insulating paint (in yellow on fig. 5 a). Wires are connected to bearings by making a trench on the external part of the outer ring and fixed using a silver lacquer which then was reinforced by a cyanoacrylate glue (fig. 5 b). 3 Fig. 2: SEM photography of (a) the inner ring surface and of (b) the roller Fig. 3: Roughness profile of contacting surfaces Fig. 4: Experimental b ench 4 Fig. 5: (a) Electrical measurement method, (b) Fixing the wires Vibratory measurements were made using a piezoelectric sensor (DJB A/120 VT) with a sensitivity of 1 mV/(m.s-2) - ± 10%. Furthermore, the data were acquired with an OROS OR36 system . The sensor was placed in the direction of the applied load and fixed with a cyanoacrylate glue (fig. 6 ). Fig. 6: Position of the vibration s sensor 2.3 Protocol Once the wires were glued and the insulating paint applied, each component of bearings is reassembled after being cleaned with commercial grade acetone then with alcohol and finally air dried . The cleaning ensure a dry contact between contacting elements. A study in the presence of a third body will be made later, the aim is here to have a reference situation from the electrical point of view. Bearings are then mounted in their respective places and measurement dev ices set -on. Electrical and vibratory measurements are made simultaneously during rotation for each value of load and speed . The rotation speed takes the following values [20, 50, 100, 200, 500, 10 00] rpm, and the load takes the following values [1 00, 200, 300] daN. The probing current is fixed to 200 mA, considering that this value do es not induce any degradation of the race surface [17]. Each measurement is recorded in a window of 10 seconds with a sample rate of 10 Hz for the electrical method and a samp le rate of 25.6 kHz for the vibrat ory one. 5 3. RESULTS 3.1 Vibratory analysis Figure 7 shows a typical vibrations signal accompanied with its spectrum , at 100 rpm and under 300 daN. Sporadic peaks seem to appear in the signal, however nothing relevant emerges from the vibration spectrum : the bearing being new no defect frequency appears ( Table 2 ). The signature of the rotation can be guessed but i t is only f rom 500 rpm that it really appears and is accompanied with few harmonics . That signature is clearer at 1000 rpm: the amplitude at the rotation frequency (16.67 Hz) is even accompanied with seven distinct harmonics. (a) (b) Fig. 7: (a) Typical v ibration s measurement and (b) its spectrum - applied load: 300 daN, rotation speed: 100 rpm The statistical properties of vibrations, standard deviation, skewne ss and kurtosis (Table 3 ) are shown respectively on figure 8a , 8b and 8c. The s kewness and the kurtosis exhibit similar behaviors and take high values at low speed: the system seems “unstable” before 200 rpm and then rapidly converge to usual values of a healthy case (0 for the skewness, 3 for the kurtosis). The standard deviation seems to strictly evolve following a power law of the rotation speed (𝜎𝑎𝑐𝑐.=𝐴𝜔𝛼). For each of those parameters, nothing seems to allow to distinguish the effect of th e load on the system. Ordinal Name Formula 1 Mean 𝜇=𝐸[𝑥]=1 𝑁∑𝑥𝑖𝑁 𝑖=1 2 Variance 𝜎2=𝐸[(𝑥−𝜇)2] 3 Skewness 𝑆𝑘=𝐸[(𝑥−𝜇 𝜎)3 ] 4 Kurtosis 𝐾𝑢=𝐸[(𝑥−𝜇 𝜎)4 ] Table 2: Moments calculation 6 (a) (b) (c) Fig. 8: (a) Standard deviation, (b) Skewness and (c) Kurtosis of v ibrations according to rotation speed and loads 3.2 Electrical analysis Figure 9 shows typical electrical signal accompanied with its spectrum, at 100 rpm and under 300 daN. The represented signal is obtained from the ratio between the measured voltage 𝑉 and the imposed current 𝐼. Since the current is a constant, the measurement of the voltage is a measurement of the global impedance 𝑍 of the system “bearing -shaft -bearing” ( fig. 5a ). The electrical signal shows oscillations with a n unexpected frequency , usually associated to an o uter ring defect (BPFO). This signature comes back systemically for each measurements from 100 rpm. Several harmonics of this signature can also be observed. Figure 10 show the evolution of the amplitude of this signature according to the rotation speed and loads. Overall, the amplitude seems to decrease with the rotation speed and does not seem to be influenced by the applied load. Note that the signature of rotation frequency rem ains absent in each measurement spectra . 7 (a) (b) Fig. 9: (a) Typical electrical measurement and (b) its spectrum - applied load: 300 daN, rotation speed: 100 rpm Fig. 10: Evolution of the amplitude of the peak at the load distribution frequency according to the rotation speed Figure 11 shows the evoluti on of the mean value of the ratio 𝑉𝐼⁄. As expected, the load has a clearly influence on the system: the contact between each rolling element being better, the electrical resistance from the contact interfaces decreases. The impedance of the system decreases with the rotation speed from 50 to 500 rpm and then seems to i ncrease. The error bars associated to each point are the standard deviation comp uted from five measurement repetitions . Fig. 11: Evolution of the mean value of the impedance according to the rotation speed 8 4. DISCUSSION The unexpected presence of a de fect signature in the electric measurements , which is absent in the vibrational one s, needs to be interpreted. Since the bearings are new it is very unlikely that a mechanical defect be present. Nothing in the vibrational analysis seems to contradict this hypothesis: at high speed, the statistical parameters converges to highlight the healthy st ate of bearings. Two possibilities remains: (i) The signature is due to an electrical defect, i.e. the conductivity of the outer ring race is not homogeneous. Since bearings are cleaned before measurements, it is possible that during this process the surface c hemical properties be altered leading to an inhomogeneity of the conductivity for each element. However, only the signature of BPFO appears: this alteration should lead to the signature of BPFI and BSF ( Table 3 ). Note that this BPFO s ignature is also detec ted in ball bearings that don’t even undergone any treatment. Rotation speed (rpm) Rotation frequency (Hz) Defect frequencies (Hz) FTF BSF BPFO BPFI 20 0.33 0.134 1.658 1.747 2.586 50 0.83 0.336 4.144 4.368 6.465 100 1.67 0.672 8.289 8.737 12.930 200 3.33 1.344 16.577 17.473 25.860 500 8.33 3.360 41.443 43.683 64.651 1000 16.67 6.720 82.885 87.366 129.301 Table 3: Usual d efect frequencies (FTF: Fundamental Train Frequency, BSF: Ball Spin Frequency, BPFO: Ball Passing Frequency of Outer race, BPFI: Ball Passing Frequency of Inner race) (ii) The signature is due to the modulation of the load distribution inside the bearings. As shown on the figure 12 , the load is distributed on the rollers. During the rotation the position of the rollers changes leading to redistribute the load according to the new position of rollers. In a first approximation, assuming that all contacts follow the same behavior, nothing can distinguish the situation a and a’ . This phenomenon is obviou sly periodic and has the same frequency than a BPFO. H owever, this phenomenon is completely absent from previous works [15, 16], therefore this hypothesis remains uncertain. Fig. 12: Load distribution in a roller bearing during rotation The second hypothesis, if it is true, leads to a problem. If the load distribution frequency an d the BPFO are the same, how to distinguish them in the spectrum of signals? This point insists on the need to have a data analysis method which can make the distinction between these two signatures. 9 Figure 13 shows a comparison of the DC component of the power spectral density (PSD) of each signals between both methods. This last figure shows the relevance of the electrica l method: the DC component of electrical signals is clearly influenced by the load and by the rotation speed while that of the vibration s is monotone and load independent. Those results alone sums up all the interest brought by electrical measurement: in this range of forces and speeds, electrical probing provides reliable elements allowing to estimate the operative parameter s of the syste m. Fig. 13: Evolution of the DC component of the power spectral density according to the rotation speed and load (electrical data are in V²/Hz and vibrational data in m²/s4/Hz) 5. CONCLUSION Support by previous work [14-17], including a patent [19], this work demonstrates the relevance of electrical probing in order to monitor mechanical components such as bearings. This study shows the possibility to access easily - due to its high sensitivity - to operative parameters that a classical method based on vibrations is not able to do. Moreover, electrical probing open s ways to access to the load distribution through a specific signature that could be related to stresses i nside the component, which are one of the most important parameters to complete and making more reliable current ageing models. This study also opens perspectives to research and to compare defect signatures from different methods in order to know the poss ibility that can offer electrical probing in the monitoring of mechanical components. ACKNOWLEDGMENTS The authors t hank the University of Reims Champagne -Ardenne for its contribution to the funding of this work. The authors t hank also the “Hub de l’énergie” for allowing the use of their surface characterization machines. 10