Low-speed Bearing Research Articles

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.

Correction: Extremely Low-Speed Bearing Fault Diagnosis Based on Raw Signal Fusion and DE-1D-CNN Network

Correction: Extremely Low-Speed Bearing Fault Diagnosis Based on Raw Signal Fusion and DE-1D-CNN Network

Extremely Low-Speed Bearing Fault Diagnosis Based on Raw Signal Fusion and DE-1D-CNN Network

Extremely Low-Speed Bearing Fault Diagnosis Based on Raw Signal Fusion and DE-1D-CNN Network

A Comparative Experimental Study of Vibration and Acoustic Emission on Fault Diagnosis of Low-Speed Bearing

A Comparative Experimental Study of Vibration and Acoustic Emission on Fault Diagnosis of Low-Speed Bearing

An Acoustic Emission Event Filtering Method for Low-Speed Bearing Outer Race Defect Localization

Rolling element bearing can be diagnosed by find the location of the fault with acoustic emission (AE) technology, this method is independent to the rotating speed. However, bearing AE signals collected under low-speed conditions are susceptible to interference from other components, which will affect the accuracy of localization. This paper proposes an AE event filtering method for low-speed bearing defect localization. Firstly, an improved denoising method based on discrete wavelet transform (DWT) is applied to the interfered AE signal, to suppress the interference component effectively. Secondly, an AE events filtering method is developed based on continuous wavelet transform and Neyman-Pearson criterion (CWT-NP), which includes searching for peaks in the continuous burst time of AE events through CWT, and calculating the peak group ratio (PGR). According to the PGR value of the AE event, the threshold is set by the NP criterion to filter the noise AE event. Lastly, the proposed method achieves satisfactory results in the outer race defect localization of low-speed bearing by constant and variable speed localization experiments. The results of the experiments demonstrate that the proposed approach is capable of filtering noise events, and find the exact location of the source of bearing damage.

Low-speed bearing fault diagnosis based on improved statistical filtering and convolutional neural network

Low-speed bearing fault signal is weak and overwhelmed by background noise, and it is ineffective in diagnosing bearing faults. In this study, an improved statistical filtering and convolution neural network (CNN) low-speed bearing fault diagnosis method is presented. Statistical filtering is a convenient and effective signal-filtering method that can extract fault signals from strong background noise original signals. The improved method can deal with drawbacks in selecting the standard distinction index of statistical filtering algorithm. CNN is used to extract signal features automatically and build the diagnosis model for fault classification. An engineering experiment is performed with different condition-bearing fault signals to test this presented method, and the results show that the improved statistical filter and CNN are effective in diagnosing low-speed bearing signals. Comparison with traditional methods proves that this study has superior performance in low-speed bearing fault diagnosis.

Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis.

To increase the competitiveness of wind energy, the maintenance costs of offshore floating and fixed wind turbines need to be reduced. One strategy is the enhancement of the condition monitoring techniques for pitch bearings, because their low operational speed and the high loads applied to them make their monitoring challenging. Vibration analysis has been widely used for monitoring the bearing condition with good results obtained for regular bearings, but with difficulties when the operational speed decreases. Therefore, new techniques are required to enhance the capabilities of vibration analysis for bearings under such operational conditions. This study proposes the use of indicators based on entropy for monitoring a low-speed bearing condition. The indicators used are approximate, dispersion, singular value decomposition, and spectral entropy of the permutation entropy. This approach has been tested with vibration signals acquired in a test rig with bearings under different health conditions. The results show that entropy indicators (EIs) can discriminate with higher-accuracy damaged bearings for low-speed bearings compared with the regular indicators. Furthermore, it is shown that the combination of regular and entropy-based indicators can also contribute to a more reliable diagnosis.

Research on Feature Enhancement in Low-speed Bearing Fault Based on Symmetric Difference Analytic Energy Operator

Research on Feature Enhancement in Low-speed Bearing Fault Based on Symmetric Difference Analytic Energy Operator

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.

Friction Characteristics and Mechanisms of Two Lithium Greases in Elastohydrodynamic Lubrication

Two types of lithium greases were fabricated by using PAO and polyol ester as two base oils. The friction tests for the lithium soap-based greases were performed under different operating conditions by mini-traction machine, and the data were compared and analysed. The results show that when the lithium-based soap thickens different types of base oils with the same viscosity, the greater the elastohydrodynamic friction coefficient of the base oil, the greater the elastohydrodynamic friction coefficient of the grease. In a low-speed bearing or contact pair, both base oil and thickener go through the contact zone to contribute to the friction of the grease. When the same lithium-based thickener thickens the different base oils, the greater the interactions between the base oil molecules, the greater the interactions between the base oil molecules and the thickener molecules, the wider the soap fibres, and the greater the friction coefficient of the grease. The friction coefficient of oil PAO–lithium-based soap grease is higher than that of its base oil PAO. The friction coefficient of oil polyol ester–lithium-based soap grease is less than that of its base oil polyol ester when the viscosities of the two base oils are 68 mm2/s.

Low-Speed Bearing Fault Diagnosis Based on Permutation and Spectral Entropy Measures

Despite its influence on wind energy service life, condition-based maintenance is still challenging to perform. For offshore wind farms, which are placed in harsh and remote environments, damage detection is critically important to schedule maintenance tasks and reduce operation and maintenance costs. One critical component to be monitored on a wind turbine is the pitch bearing, which can operate at low speed and high loads. Classical methods and features for general purpose bearings cannot be applied effectively to wind turbine pitch bearings owing to their specific operating conditions (high loads and non-constant very low speed with changing direction). Thus, damage detection of wind turbine pitch bearings is currently a challenge. In this study, entropy indicators are proposed as an alternative to classical bearing analysis. For this purpose, spectral and permutation entropy are combined to analyze a raw vibration signal from a low-speed bearing in several scenarios. The results indicate that entropy values change according to different types of damage on bearings, and the sensitivity of the entropy types differs among them. The study offers some important insights into the use of entropy indicators for feature extraction and it lays the foundation for future bearing prognosis methods.

Numerical Noise Transfer Analysis of a Flexible Supported Gearbox Based on Impedance Model and Noise Transfer Function

To investigate the gearbox radiation noise properties under various rotational speeds, a noise prediction method based on impedance model and noise transfer function (NTF) is proposed. One only needs to extract the NTF of the housing once rapid gearbox noise prediction under different working conditions is realized. Taking a flexible supported gearbox as a research object, the external excitation of the housing (the bearing excitation load and isolator excitation load) is calculated through a gear-housing-foundation-coupled impedance model, and the noise transfer function is simulated through the vibroacoustic-coupled boundary element model; then the radiation noise is obtained. Based on this model, the noise transfer analysis of the housing is carried out, different excitation components and NTF components are compared, and the contributions of different excitation components to noise are compared. Results show that the radiation noise of gearbox is mainly excited by the high-speed bearing, while the low-speed bearing and isolator have little influence on noise. At low speed, vertical force, axial force, and moment excitation of bearings all contribute to the radiation noise while at high speed, the gearbox radiation noise is mainly generated by vertical excitation force of bearings.

Low Speed Bearing Fault Diagnosis Based on EMD-CIIT Histogram Entropy and KFCM Clustering

In view of weak defect signals and large acoustic emission (AE) data in low speed bearing condition monitoring, we propose a bearing fault diagnosis technique based on a combination of empirical mode decomposition (EMD), clear iterative interval threshold (CIIT) and the kernel-based fuzzy c-means (KFCM) eigenvalue extraction. In this technique, we use EMD-CIIT and EMD to complete the noise removal and to extract the intrinsic mode functions (IMFs). Then we select the first three IMFs and calculate their histogram entropies as the main fault features. These features are used for bearing fault classification using KFCM technique. The result shows that the combined EMD-CIIT and KFCM algorithm can accurately identify various bearing faults based on AE signals acquired from a low speed bearing test rig.

Low-Speed Bearing Fault Diagnosis Based on ArSSAE Model Using Acoustic Emission and Vibration Signals

The development of rolling element bearing fault diagnosis systems has attracted a great deal of attention due to bearing components having a high tendency toward unexpected failures. However, under low-speed operating conditions, the diagnosis of bearing components remains a problem. In this paper, the adaptive resilient stacked sparse autoencoder (ArSSAE) is proposed to compensate for the shortcomings of conventional fault diagnosis systems at low speed. The efficiency of the proposed ArSSAE model is initially assessed using the CWRU database. Then, the proposed model is evaluated on actual vibration analysis (VA) and acoustic emission (AE) signals measured on a bearing test rig at low operating speeds (48-480 rpm). Overall, the analysis demonstrates that the ArSSAE model is able to perform an accurate diagnosis of bearing components under low-speed conditions.

Reliable fault diagnosis of bearings with varying rotational speeds using envelope spectrum and convolution neural networks

Determining the optimal features that are invariant under changes in the rotational speed variations of rolling element bearings is a challenging task. To address this issue, this paper proposes an acoustic emission (AE) analysis-based bearing fault diagnosis invariant under fluctuations of rotational speeds using envelope spectrums (ES) and a convolutional neural network (CNN). The ES extracted from the raw AE signals provides valuable information about the characteristic defect frequency peaks and variations to bearing rotational speeds when faults appear on a bearing. The proposed method employs CNN to automatically extract high quality features and classify bearing defects. In the experiment, a CNN trained on a dataset corresponding to one revolutions per minute (RPM) is used to detect patterns from datasets corresponding to other RPMs to verify that the classification is accurate and invariant under rotation speed fluctuations. The efficacy of the proposed method is verified on AE-based low-speed bearing data under various rotational speeds. The experimental results show that the proposed method is effective at detecting bearing failures, provides an average classification accuracy of about 86% under fluctuating RPM, and outperforms other state-of-the-art algorithms.

Reliable Fault Diagnosis for Low-Speed Bearings Using Individually Trained Support Vector Machines With Kernel Discriminative Feature Analysis

This paper proposes a highly reliable fault diagnosis approach for low-speed bearings. The proposed approach first extracts wavelet-based fault features that represent diverse symptoms of multiple low-speed bearing defects. The most useful fault features for diagnosis are then selected by utilizing a genetic algorithm (GA)-based kernel discriminative feature analysis cooperating with one-against-all multicategory support vector machines (OAA MCSVMs). Finally, each support vector machine is individually trained with its own feature vector that includes the most discriminative fault features, offering the highest classification performance. In this study, the effectiveness of the proposed GA-based kernel discriminative feature analysis and the classification ability of individually trained OAA MCSVMs are addressed in terms of average classification accuracy. In addition, the proposed GA-based kernel discriminative feature analysis is compared with four other state-of-the-art feature analysis approaches. Experimental results indicate that the proposed approach is superior to other feature analysis methodologies, yielding an average classification accuracy of 98.06% and 94.49% under rotational speeds of 50 revolutions-per-minute (RPM) and 80 RPM, respectively. Furthermore, the individually trained MCSVMs with their own optimal fault features based on the proposed GA-based kernel discriminative feature analysis outperform the standard OAA MCSVMs, showing an average accuracy of 98.66% and 95.01% for bearings under rotational speeds of 50 RPM and 80 RPM, respectively.

Effects of the Ultra-Dispersed Nano-Diamond Additive on the Grease Boundary Lubrication Performance in the Reciprocating Journal Bearing Test

This study analyzes the tribological performance of the test results of the journal bearing wear tester designed for the simulation of the toggle pin motion in the plastics injection molding machine. Grease with or without the addition of ultra-dispersed nanodiamond (UDD) particles were all tested in the durability tests. It showed that the grease with UDD additive could effectively extend the life of grease more than twice as long as the grease life without UDD additive in the reciprocating heavy-load and low-speed journal bearing tests.

Gas-Expanded Lubricant Performance and Effects on Rotor Stability in Turbomachinery

Gas-expanded lubricants (GELs) are tunable mixtures of synthetic oil and carbon dioxide that enable dynamic control of lubricant viscosity during bearing operation. This control can help reduce bearing power loss and operating temperatures while also providing direct control over bearing stiffness and damping, which can enhance rotordynamic performance. In this work, the bearing and rotordynamic performance of two representative high-speed machines was evaluated when different lubricants, including GELs, were supplied to the machine bearings. The machines chosen for this analysis, an eight-stage centrifugal compressor and a steam turbine-generator system, represent a wide range of speed and loading conditions encountered in modern turbomachinery. The fluids compared for machine performance were standard petroleum-based lubricants, polyol ester (POE) synthetic oils, and POE-based GELs. The performance simulations were carried out using a thermoelastohydrodynamic bearing model, which provided bearing stiffness and damping coefficients as inputs to finite element rotordynamic models. Several bearing performance metrics were evaluated including power loss, operating temperature, film thickness, eccentricity, and stiffness and damping coefficients. The rotordynamic analysis included an evaluation of rotor critical speeds, unbalance response, and stability. Bearing performance results for the compressor showed a 40% reduction in power loss at operating speed when comparing the GEL to the petroleum-based lubricant. The GEL-lubricated compressor also exhibited lower operating temperatures with minimal effects on film thickness. GELs were also predicted to produce lower bearing stiffness when compared to standard fluids in the compressor. Rotordynamic results for the compressor showed that the fluid properties had only minor effects on the unbalance response, while GELs were found to increase the stability margin by 43% when compared with standard fluids. The results from the turbine-generator system also demonstrated increases in low-speed bearing efficiency with the use of GELs, though at higher speeds the onset of turbulent flow in the GEL case offset these efficiency gains. Rotordynamic results for this system showed a contrast with the compressor results, with the GELs producing lower stability margins for a majority of the modes predicted due to increased bearing stiffness in the high-speed turbine bearings and negative stiffness in the lightly loaded, low-speed pinion bearings. These results suggest that GELs could be beneficial in providing control over a wide range of machine designs and operating conditions and that some machines are especially well suited for the tunability that these fluids impart.

The Research of Lubricating and Colling System of the Low-Speed and Heavy-Load Sliding Bearing

This paper analyzes the influence of lubricating oil film of the low-speed and heavy-load sliding bearing on a variety of factors. It introduces the operation of Bearing for lubricating oil film in monitoring method, common fault causes and treatment measures. Finally, the bearing cooling system structure is discussed, providing reference for practical engineering application. There are many reasons to cause the failure of the bearing lubrication such as bearing assembly defects, the error of size and cooling system problems. By detecting and contrast parameters, this study analyze the determine data and correct the fault. Through the use of reasonable structure of the oil tank and cooling water channel, we can improve the lubrication performance, and extend the bearing life. This paper analyzes the factors restricting the low speed and high load sliding bearing lubricant film, and solves the actual bearing lubrication problem.

Study on Fault Diagnosis of Wind Turbine Main Bearing Based on Finite Element Analysis and Wavelet Analysis

A new method based on finite element analysis and wavelet analysis for the fault diagnosis of wind turbine main bearings is presented in the paper. Firstly, take example for low speed bearing, the three-dimension models of the good and fault bearings are set up, which was discussed with using finite element method. The stress, strain and contact stress distribution were computed by this method. Then the wavelet transform of data signals collected in experiment was carried out with db6 wavelet and 4 scales on the basis of the stress wave analysis. The frequency D3 and D4 signals were reconstructed and characteristic frequencies of stress waves with the simulative faults were extracted. It has been shown that the method applying finite element analysis and wavelet analysis is an efficient method for the fault diagnosis of wind turbine main bearings.