Low-speed Rolling Research Articles

Influence of Grease Contamination on Acoustic Emission Monitoring of Low-speed Roller Bearings

Large-scale low-speed rolling element bearings form crucial connections in offshore installations such as heavy-lifting cranes, single point mooring systems, and wind turbines. Due to the stochastic nature of wind and waves, the applied loads on these bearings are hardly predictable. Furthermore, the remote nature of the offshore environment requires a high standard of operational safety and reliability, reinforcing the need for advanced inspection and monitoring methods. Acoustic emission (AE) is a continuous monitoring technique for condition assessment of low-speed bearings, as it may detect the stress waves associated with developing degradation within the rolling elements. This paper presents an investigation on the influence of grease contamination on acoustic emission (AE) monitoring of low-speed bearings. It discusses several experiments that have been conducted in various regimes of particle contaminated lubrication, and proposes a strategy for condition monitoring. The presented experiments have been conducted with differing bearing geometries in multiple testing environments. The contamination particles have been both naturally developed and artificially introduced. The results total of 9 experimental cases are discussed and compared. All experiments follow the same data-acquisition principles, utilising arrays of three AE transducers sensitive in a range between 40-580 kHz. Data is recorded while operating the bearing at low speed under load. Abrasive wear of the rolling elements and crushing of the particles are concluded to be the main sources of AE due to particle contamination. Processing consists of waveform cross-correlation clustering and feature analysis. The results suggest that hard abrasive particles make significant contribution to the AE signals, which is associated with abrasive wear of the rollers and raceways. Comparisons with the contribution of softer and finer particles are presented as well.

Application of Acoustic Emission Technology for Bearing Condition Monitoring on Passenger Ropeway

Rolling bearings are important stressed components of driving wheels and detour wheels on passenger ropeway, and their failure may lead to accidents and casualties. After the ropeway is installed and put into operation, the rolling bearings in the wheels are difficult to disassemble. Once disassembled, the bearing may be damaged. Due to the installation position and the complex operation environment, the health status of the rolling bearings is a blind spot for ropeway inspection and detection. Acoustic emission detection technology is used to monitor the rolling bearings of passenger ropeways. After a large number of field tests, the acoustic emission monitoring program and signal analysis method of rolling bearing on ropeway were proposed. The parameter characteristics of acoustic emission signals obtained in bearing operating were analyzed from two aspects: the ropeway operating cycle and the bearing operating cycle. Spectrum analysis of typical signals was performed, and the spectrum characteristics of typical acoustic emission signals of rolling bearing during the operation of the ropeway were obtained. On-site tests results show that using acoustic emission technology to perform condition monitoring and fault diagnosis of low-speed rolling bearings on passenger ropeways has good application prospects.

Study on motion states of sphere based on empirical Fourier decomposition

This study uses Empirical Fourier Decomposition (EFD) to decompose the signal of motion of a sphere on a rough planar surface, which is a mixture of three main states: sliding, rolling, and collision. The simulation results show that the decomposed sub- signals are consistent with the original signals. Furthermore, we conducted a laboratory experiment to study the movement of a sphere on a rough inclined chute. The movement of the sphere is divided into two main stages: low-speed rolling and bouncing forward. In the first stage, the sphere motion consists mainly of rolling and collision. In the second stage, the sphere motion consists of sliding, rolling, and collision. We propose for the first time that the duration and peak acceleration of collisions from two stages follow a normal distribution.

Synchronous averaging with sliding narrowband filtering for low-speed bearing fault diagnosis

The health condition of low-speed rolling bearing, such as the main bearing in wind turbines which bears the heavy dead weight and operates under variable speeds, has a big impact on the safe operation of the machinery. Therefore, damage detection of low-speed bearings plays a key role in the health management of large-scale rotating machinery. However, in popular vibration based bearing damage detection algorithms, due to the fact that the additional vibration features incurred by the low-speed operated bearing damage are typically weak in amplitude and low in frequency contents, the additional responses caused by damage are difficult to be isolated by conventional algorithms. Especially, in the cases of variable speed operations, the smearing effect caused by Fourier transform makes it more difficult to extract the damage features by spectrum analysis based methods. To deal with these issues, we developed a damage detection procedure specially designed for bearings operated at low and variable speeds. According to the dynamic properties of the vibration signals incurred by a low-speed bearing with damage, an envelope analysis method based on synchronous averaging with sliding narrow band-pass filters is designed and developed for extracting damage features in the low frequency range. The fundamental theory used in the method is derived first. Then, a damage detection signal processing procedure is constructed based on the elaborated theory. The feasibility and advantages of the proposed methodology are validated by numerical simulations as well as the measured data from a wind turbine field example.

Research on global optimization mechanism of intelligent compaction parameters of soil subgrade based on difference method

The global optimization mechanism plays a crucial role in enhancing construction efficiency and ensuring construction quality of soil subgrade such as building foundation or roadbed, within the context of intelligent compaction technology. There are three parts in the global optimization mechanism, which are determining the optimal work parameters, judging the compaction quality, and the feedback adjustment method. To establish a global optimization mechanism for intelligent compaction of subgrade soil, this paper developed a numerical simulation model for subgrade intelligent compaction, defined the necessary numerical simulation conditions for the global optimization mechanism of intelligent compaction. In this way, it provides a source of data for the establishment of global optimization mechanisms. Furthermore, the optimal working parameters are determined by the method of multivariate nonlinear fitting and calculating its extreme value. Meanwhile, an intelligent compaction quality judgment mechanism is established. Additionally, a feedback adjustment method for intelligent compaction was developed using the difference method to form a comprehensive global optimization mechanism. Finally, the intelligent compaction's global optimization mechanism has been validated through field experiments. The results of this study demonstrate that the boundary between low speed and medium speed rolling is 1.7 m/s. The optimal number of rolling passes is four and the ideal rolling speed ranges from 1.2 to 1.3 m/s when initial compaction degree reaches between 80 % and 90 %. The compaction degree, CMV (Compaction Meter Value) and AICV (Acceleration Intelligent Compaction Value) all exhibit a decreasing trend of subgrade compaction quality with increasing rolling speed, and the rate of decrease gradually accelerates.

Rolling speed dependence of cryogenic rollability of bimodal non-basal textured AZ31 Mg alloy sheet

The rolling speed dependence of cryogenic rollability of bimodal non-basal textured AZ31 Mg alloy sheet has been investigated through multi-pass rolling experiments with a constant 5% thickness reduction per pass and characterization experiments via X-ray diffractometer (XRD) and electron backscatter diffraction (EBSD) techniques. The chosen rolling speeds include 30 mm/s (low-speed), 300 mm/s (mediate-speed) and 600 mm/s (high-speed). Rolling results show that the accumulated thickness reduction (ATE) of low-speed rolled sheet reaches up to about 22.6%, which is 58.0% bigger than that (14.3%) of high-speed rolled sheet. This means that the rollability of studied sheet under high-speed rolling is obviously worse than that under low-speed rolling. Characterization results confirm that high-speed rolled sheet possesses the smallest tilted angle (about 16.3°) between the maximum intensity point in (0002) pole figure and normal direction (ND) by comparison with other two sheets, leading to the enhancement of difficulty to accommodate the thickness plastic strain during rolling at liquid-nitrogen temperature. Moreover, high-speed rolling can not only realize the saturation of {10-12} extension twin (ET) nucleation at the beginning of cryogenic rolling, but also promote the growth of {10-12} ET nuclei. This issue benefits in improving the frequency of occurrence of Ortho-position (OP) -typed {10-12} ET variant interaction, which would block the motion of dislocation slip effectively. The deteriorated rollability of high-speed rolled sheet is mainly attributed to these two issues.

Luminal endothelialization of small caliber silk tubular graft for vascular constructs engineering.

The constantly increasing incidence of coronary artery disease worldwide makes necessary to set advanced therapies and tools such as tissue engineered vessel grafts (TEVGs) to surpass the autologous grafts [(i.e., mammary and internal thoracic arteries, saphenous vein (SV)] currently employed in coronary artery and vascular surgery. To this aim, in vitro cellularization of artificial tubular scaffolds still holds a good potential to overcome the unresolved problem of vessel conduits availability and the issues resulting from thrombosis, intima hyperplasia and matrix remodeling, occurring in autologous grafts especially with small caliber (<6 mm). The employment of silk-based tubular scaffolds has been proposed as a promising approach to engineer small caliber cellularized vascular constructs. The advantage of the silk material is the excellent manufacturability and the easiness of fiber deposition, mechanical properties, low immunogenicity and the extremely high in vivo biocompatibility. In the present work, we propose a method to optimize coverage of the luminal surface of silk electrospun tubular scaffold with endothelial cells. Our strategy is based on seeding endothelial cells (ECs) on the luminal surface of the scaffolds using a low-speed rolling. We show that this procedure allows the formation of a nearly complete EC monolayer suitable for flow-dependent studies and vascular maturation, as a step toward derivation of complete vascular constructs for transplantation and disease modeling.

Exploration of the dynamic osmotic membrane bioreactor in low-speed rolling motion for membrane fouling mitigation

A specific dynamic osmotic membrane bioreactor (DR-OMBR) in low-speed rolling motion was designed to alleviate membrane fouling. Using a low rotation speed (2 rpm) together with the cross-flow velocity in this designed DR-OMBR, the shear stress (−1.57 × 10−4 N/m2) and the centrifugal force (1.31 × 10−4 N) were generated to reduce the attachment of foulant to the membrane surface and achieve a relatively stable water flux. A parallel experiment indicated that the water flux of the conventional OMBR (without rotation) was 10% lower than that of the DR-OMBR, and total organic carbon and phosphate removals still achieved 99.9 and 97.0%, respectively. The ammonium rejection was slightly lower as a result of the Donna effect. Furthermore, compared to rotation speeds (0–5 rpm), the rotation speed of 2 rpm achieved better water flux (6.4 LMH) and lower reversal salt diffusion (3.7 gMH) because the centrifugal force had a positive effect on the reversal salt flux and a negative impact on the water flux, and a higher rotation speed could generate a considerable centrifugal force to limit the water flux and increase the reverse salt flux. Furthermore, the mitigation of membrane fouling was further confirmed by the relatively low amount of extracellular polymeric substances (EPS) and soluble microbial products (SMP) on the membrane surface of the DR-OMBR.

Study on spacecraft formation capture control method based on disturbance observer

In the presence of compound disturbances, a multi-spacecraft cooperative collision avoidance capture control method based on disturbance observer was proposed, which can solve the problem of low speed rolling non-cooperative target close-range capture in space. Firstly, a relative motion model of attitude and orbit coupling is established. Secondly, the disturbance observer is used to estimate and cancel the compound disturbance in the capture process. At the same time, the hyperquadric surfaces are used to describe the shape of space non-cooperative targets and capture spacecraft to establish a composite artificial potential field, and a robust control law with collision avoidance function is also designed. Finally, the stability of the controlled system is proved by using Lyapunov function, and the collision avoidance performance of the system is analyzed. Numerical simulations are carried out to evaluate the effectiveness of the proposed control scheme.

Microstructural, Mechanical, and Tribological Properties of Mg-3Al-1Sn-1Nd-Mn Alloy

In this study, Mg-3Al-1Sn-1Nd-Mn alloy was produced by low-pressure die casting method and homogenization heat treated (HHT) at 400 °C for 16 hrs. Microstructural characterization of the alloy was done by XRF, XRD, LOM and SEM–EDS analyses. The microstructure of the alloy was composed of α-Mg, Al2Nd and Al11Nd3 phases. Hot rolling was applied to the HHT alloy at two different speeds 13.78 rpm [low speed rolling (LSR)] and 26.96 rpm [high-speed rolling (HSR)] at 350 °C. Microstructural changes were also carried out by LOM and SEM analyses. Mechanical properties (hardness and tensile properties) and wear performances of HHT, LSR and HSR samples were compared. SEM fractography was conducted on tension test samples to visualize the deformation and fracture behaviors. Also, worn surfaces of samples were investigated by SEM–EDS analysis. Abrasive, oxidation and delamination mechanisms were observed for worn surfaces of samples. To sum up, the mechanical properties of the HSR sample were higher compared to the HHT and LSR samples, and the wear rate was lower than HHT and LSR samples.

Fault Diagnosis Method of Low-Speed Rolling Bearing Based on Acoustic Emission Signal and Subspace Embedded Feature Distribution Alignment

Vibration signal always performs poorly in the fault diagnosis of low-speed rolling bearings. The fact that rolling bearings running under different speed conditions further increases the difficulty of fault diagnosis on low-speed bearing. To address the above problems, this article proposes a fault diagnosis method for low-speed rolling bearings based on acoustic emission (AE) signal and subspace embedded feature distribution alignment (SADA). First, the AE signal of low-speed rolling bearing is collected and the spectral dataset is constructed. Second, subspace alignment is used to align the basis vectors for both domains in order to prevent feature distortion. Then, a base classifier is trained to predict the pseudolabels of the target domain, which is used to quantitatively estimate the weight of the edge distribution and conditional distribution of the two domains for adaption. Finally, following the structural risk minimization (SRM) framework, a kernel function is constructed to establish the classifier f, which iteratively updates the pseudolabels in the target domain and obtains the coefficient matrix of the final framework to complete the identification task. The feasibility and effectiveness of the proposed method are verified by two AE datasets of low-speed rolling bearing.

Study on bone volume harvested from the implant sites with different methods

To compare the volume of autogenous bone particles harvested utilizing different techniques and various implant systems during implant surgery, and to determine the advantageous method to collect autogenous bone particles. Homogeneous epoxy resin simulated jaw bone model was enrolled. Bicon, Bego implant systems and Straumann tissue level implant systems were utilized. The two techniques were investigated. One method was low-speed drilling (50 r/min) without water irrigating, and the other one was drilling with cold water irrigating to the ideal depth, then closing the water and drilling out with low speed (50 r/min). The bone particles in the drill groove and implant beds were collected. The volumes of the bone harvested were compared between the different techniques and also among the three implant systems, then they were compared with the volume of the bone harvested by the special bone drill. The sample size of each sub-group was 10. The bone particles were weighed by electronic balance after drying. The harvested bone volume between the latch reamers and hand reamers of Bicon system with the first method was not significantly different. When the same size implant bed was prepared, the volume of the bone particles produced during the implant surgery with low-speed drill without water was significantly higher than that with the other method no matter Bicon [3.5 mm×10 mm hole for example (28.42±6.04) mg vs. (6.30±2.51) mg, P<0.001] or Bego system [2.8 mm×10 mm hole for example (28.95±5.39) mg vs. (4.61±3.39) mg, P<0.001] was used, and the ratio of bone volume between the first method and the second one was approximately 3.3 to 7.0 times. When using the second method to prepare the similar size implant bed, the bone volume was not significant different among Bicon, Bego and Straumann implant systems [Bicon (9.90±3.42) mg, Bego (8.70±4.09) mg, and Straumann (10.56±5.66) mg, P=0.69]. When preparing a 5 mm-diameter-10 mm-length hole with Bicon implant system and a 4.7 mm-diameter-10 mm-length with Bego implant system, the bone quantity harvested from each group was less than that harvested by special bone drill from Neo Biotech [Bicon (82.54±12.26) mg, Bego (85.07±12.64) mg vs. Neo Biotech (96.78±13.19) mg, P<0.05]. More autogenous bone can be harvested from implant beds by preparing with low-speed rolling without water than the method with water irrigation. When utilizing the same preparing method, the implant system has no impact on the volume of the bone harvested.

Using learning time as metrics: an artificial intelligence driven risk assess framework to evaluate DDoS cyber attack

The cloud computing and Internet of Things (IoT) have become two key technologies to meet future business requirements. However, a massive scale of Distributed Denial-of-Service (DDoS) has been widely applied to congest network critical links and to paralyze the cloud and IoT service. This is mainly due to DDoS is easily implemented, obfuscated, and occulted by launching large-scale legitimate low-speed flows and rolling target links to paralyze target network areas. Many metrics and risk access management frameworks to evaluate the impact of DDoS are proposed. However, they all lack time granularity to evaluate the cost of different scales of attacks in IoT or large-scale network structure. This study proposes an AI Driven Evaluation framework, called ADE, that applies Convolution Neural Networks to statistically evaluate the network status through end-to-end functionality (Input: network status; Output: DDoS detected or not) without any manual intervention. ADE provides quantitative security risk analysis by using learning time as the control variable, network structure as the independent variable, and time to identify DDoS as the dependent variable. The learning time to detect DDoS event and recover the system is then applied to evaluate the scale of this DDoS, the reasonability of the regulated RTO, and the vulnerability of the current net-work topology and the improvement due to the new security solution. The experiment results demonstrate the contributions of ADE are (1) providing objective and quantitative analytical security risk assessment indicator, (2) providing an autonomic DDoS defense framework without any manual intervention which allows cloud computing and Internet of Things company focuses on their service and leaves security defending to ADE, and (3) demonstrating the possibility of AI assisted risk assessment which enables security defense solution buyer with less security domain experts to evaluate suitable network defense strategy.

Detecting and Learning Unknown Fault States by Automatically Finding the Optimal Number of Clusters for Online Bearing Fault Diagnosis

This paper proposes an online fault diagnosis system for bearings that detect emerging fault modes and then updates the diagnostic system knowledge (DSK) to incorporate information about the newly detected fault modes. New fault modes are detected using k-means clustering along with a new cluster evaluation method, i.e., multivariate probability density function’s cluster distribution factor (MPDFCDF). In this proposed model, a heterogeneous pool of features is constructed from the signal. A hybrid feature selection model is adopted for selecting optimal feature for learning the model with existing fault mode. The proposed online fault diagnosis system detects new fault modes from unknown signals using k-means clustering with the help of proposed MPDFCDF cluster evaluation method. The DSK is updated whenever new fault modes are detected and updated DSK is used to classify faults using the k-nearest neighbor (k-NN) classifier. The proposed model is evaluated using acoustic emission signals acquired from low-speed rolling element bearings with different fault modes and severities under different rotational speeds. Experimental results present that the MPDFCDF cluster evaluation method can detect the optimal number of fault clusters, and the proposed online diagnosis model can detect newly emerged faults and update the DSK effectively, which improves the diagnosis performance in terms of the average classification performance.

Fault feature extraction of low speed roller bearing based on Teager energy operator and CEEMD

The fault signals of low-speed rolling elements bearing are non-stationary and non-linear, and consequently it is difficult to extract the fault characteristics by the traditional time and frequency domains analysis methods. Furthermore, the vibration signals suffer from severe signal attenuation and noise corruption during the signal transmission process. In order to effectively enhance and extract the fault characteristics from weak bearing signal, it requires effective signal processing strategies or high sensitive sensors to detect the low energy bearing vibration signals. In this paper, one such signal processing method is proposed to detect fault characteristics combined Teager energy operator and Complementary Ensemble Empirical Mode Decomposition (CEEMD). In this method, firstly Teager energy operator is used to strengthen the signal after wavelet noise reduction since it has good temporal resolution and adaptive ability for signal transient changes, and has unique advantages in detecting signal impact characteristics. Then CEEMD algorithm is carried out to extract bearing fault through Intrinsic Mode Function (IMF) decomposition. The proposed method is validated by a scaling model test rig of a wind turbine. The results validate that the method can effectively extract the fault characteristics of low-speed bearings and identify the bearing fault.

Adaptive feature extraction for low speed rolling bearing fault diagnosis based on improved Teager energy operator, Auto-encoder and PCA

Adaptive feature extraction for low speed rolling bearing fault diagnosis based on improved Teager energy operator, Auto-encoder and PCA

LOW SPEED ROLLING BEARING DIAGNOSTICS USING ACOUSTIC EMISSION AND HIGHER ORDER STATISTICS TECHNIQUE

Diagnostics in low speed rolling element bearings is difficult. Not only are normal frequency domain diagnostics methods not appropriate for this application, but the bearing response signals are usually immersed in background noise which make it difficult to detect these faults. Higher order statistics (HOS) techniques have been available for decades but have not been widely applied to machine condition monitoring with the exceptions of skewness and kurtosis. There is however reason to believe that these HOS techniques could play an important role in acoustic emission (AE) based condition monitoring of rolling element bearings at low speeds provided appropriate care is taken. To explore this hypothesis, AE signals at low bearing rotational speeds of 70, 80, 90 and 100 rpm respectively were used in this work for the monitoring of tapered roller bearings. In addition to the well-established statistical parameters such as mean, standard deviation, skewness and kurtosis, higher moments such as hyper flatness are considered in this study. A novel diagnostic method is proposed for fault extraction based on hyperflatness, combined with Kullback-Leibler divergence, and an indicator formula derived with the use of Lempel-Ziv Complexity is given. The Kullback-Leibler divergence is used together with the skewness and hyperflatness to obtain the Kullback-Leibler information Wave (KLW)with which the analysis is performed, and better results obtained as compared to conventional frequency domain analysis.

Time–frequency envelope analysis-based sub-band selection and probabilistic support vector machines for multi-fault diagnosis of low-speed bearings

This paper proposes a highly reliable multi-fault diagnosis scheme for low-speed rolling element bearings using an effective time–frequency envelope analysis and a Bayesian inference based one-against-all support vector machines (probabilistic-OAASVM) classifier. The proposed method first performs a wavelet packet transform based envelope analysis on an acoustic emission signal to select sub-bands of the signal that contain the most intrinsic and pertinent information about the defects. Frequency- and time-domain fault features are extracted only from selected sub-bands for fault classification. Traditional one-against-all SVMs (OAASVM), a widely used multi-class pattern recognition technique, employ an arbitrary combination of a series of binary classifiers yielding overlapped feature spaces, where a data sample can be unclassifiable. To address this limitation, we formulate the feature space of OAASVM as an appropriate Gaussian process prior (GPP) and interpret OAASVM results as a posterior probability estimation procedure using Bayesian inference under this GPP. The efficacy of the proposed probabilistic-OAASVM classifier is verified for low-speed rolling element bearings under various conditions. Experimental results indicate that the proposed method outperforms the state-of-the-art algorithms for multi-fault classification of low-speed bearings, yielding a 4.95–20.67% improvement in the average classification accuracy.

A method for reduction of Acoustic Emission (AE) data with application in machine failure detection and diagnosis

Abstract The use of AE in machine failure diagnosis has increased over the last years. Most AE-based failure diagnosis strategies use digital signal processing and thus require the sampling of AE signals. High sampling rates are required for this purpose (e.g. 2 MHz or higher), leading to streams of large amounts of data. This situation is aggravated if fine resolution and/or multiple sensors are required. These facts combine to produce bulky data, typically in the range of GBytes, for which sufficient storage space and efficient signal processing algorithms are required. This situation probably explains why, in practice, AE-based methods consist mostly in the calculation of scalar quantities such as RMS and Kurtosis, and the analysis of their evolution in time. While the scalar-based approach offers the advantage of maximum data reduction; it has the disadvantage that most part of the information contained in the raw AE signal is lost unrecoverably. This work presents a method offering large data reduction, while keeping the most important information conveyed by the raw AE signal, useful for failure detection and diagnosis. The proposed method consist in the construction of a synthetic, unevenly sampled signal which envelopes the AE bursts present on the raw AE signal in a triangular shape. The constructed signal – which we call TriSignal – also permits the estimation of most scalar quantities typically used for failure detection. But more importantly, it contains the information of the time of occurrence of the bursts, which is key for failure diagnosis. Lomb-Scargle normalized periodogram is used to construct the TriSignal spectrum, which reveals the frequency content of the TriSignal and provides the same information as the classic AE envelope. The paper includes application examples in planetary gearbox and low-speed rolling element bearing.

Low-speed rolling bearing fault diagnosis based on EMD denoising and parameter estimate with alpha stable distribution

When low-speed rolling bearings fail, it is hard to diagnose the extent of their damage. We developed a test rig to simulate the lowspeed rolling bearing operating condition, where bearings with various fault states are installed on the test wheelset and subjected to the same external loading condition. The collected bearing box acceleration time histories are processed with the Empirical mode decomposition (EMD) method combined with kurtosis criterion to filter the trend and noise components. Five characteristic parameters of Alpha stable distribution (ASD) are identified by fitting the ASD distribution to the vibration acceleration signals and computing the Probability density function (PDF). To highlight the advantage of ASD method in feature extraction, kurtosis also has be calculated. Through sensitivity and stability analysis of the six parameters and utilization of Least squares support vectors machine (LSSVM) with Particle swarm optimization (PSO), three most sensitive and stable feature parameters including the characteristic exponent α, the scale factor γ and the peak value of the PDF h are located and applied to evaluate the low-speed rolling bearings’ damage position and damage extent. The proposed method was validated by test data, and the results demonstrated that the ASD characteristics combined with PSO-LSSVM can not only achieve fault diagnosis of low-speed rolling bearings' damage position and damage extent, but also have better diagnosis accuracy and operational efficiency than other methods.