Bearings In Machinery Research Articles

A Novel Transformer Network Based on Cross–Spatial Learning and Deformable Attention for Composite Fault Diagnosis of Agricultural Machinery Bearings

Diagnosing agricultural machinery faults is critical to agricultural automation, and identifying vibration signals from faulty bearings is important for agricultural machinery fault diagnosis and predictive maintenance. In recent years, data–driven methods based on deep learning have received much attention. Considering the roughness of the attention receptive fields in Vision Transformer and Swin Transformer, this paper proposes a Shift–Deformable Transformer (S–DT) network model with multi–attention fusion to achieve accurate diagnosis of composite faults. In this method, the vibration signal is first transformed into a time–frequency graph representation through continuous wavelet transform (CWT); secondly, dilated convolutional residual blocks and efficient attention for cross–spatial learning are used for low–level local feature enhancement. Then, the shift window and deformable attention are fused into S–D Attention, which has a more focused receptive field to learn global features accurately. Finally, the diagnosis result is obtained through the classifier. Experiments were conducted on self–collected datasets and public datasets. The results show that the proposed S–DT network performs excellently in all cases. With a slight decrease in the number of parameters, the validation accuracy improves by more than 2%, and the training network has a fast convergence period. This provides an effective solution for monitoring the efficient and stable operation of agricultural automation machinery and equipment.

Fault Diagnosis of Rotating Machinery Bearings Based on Multi-Scale Attention Feature Fusion under Few Shot and Complex Working Conditions

Accidents caused by failures in rotating machinery bearings have heightened attention in bearing failure diagnosis. Scholars have explored methods to build fault diagnosis models to tackle the challenge of creating diagnostic models with few bearing failure samples available. However, many diagnostic models may suffer from overfitting issues under insufficient samples, affecting fault diagnosis performance. Additionally, rotating machinery operates under changing, complex conditions and noise interference, further deteriorating the effectiveness of fault diagnosis. This paper proposes a fault diagnosis model based on multi-scale attentional feature fusion (FD-MSAFF) to address the issue above. This model comprises a multi-scale feature extraction module, an attentional feature fusion module, and an MMD-based weighted prototype network. The FD-MSAFF model improves few-shot learning by using its multi-scale feature extraction and depth-wise separable attention modules to blend multi-scale features effectively with contextual information. It also tackles classification issues in few-shot sizes with its MMD-weighted prototype network, which is less susceptible to noise. Simulated tests under complex scenarios, such as changing conditions, noise variations, cross-bearing conditions, and comparisons with other algorithms, have proven the FD-MSAFF model's superior accuracy and generalization in diagnosing bearing faults in rotating machinery.

Intelligent fault diagnosis based on improved convolutional neural network for small sample and imbalanced bearing data

The failure of bearings is a prevalent cause of machinery breakdowns. The rapid development of intelligent technology has significantly promoted the use of deep learning techniques for identifying problems with machinery bearings. To achieve accuracy, deep learning-based diagnostic techniques require a substantial and uniformly diversified amount of training data. However, obtaining artificial labels for bearing fault data poses a major obstacle in engineering practice. This paper proposes an intelligent fault diagnosis method for bearings based on an improved convolutional neural network (CNN) to address the challenges of small training data and imbalanced distribution. To enable intelligent diagnosis of bearings with a small sample and imbalanced distribution, a clustering loss layer is introduced into the CNN. Furthermore, we optimize the parameters of the CNN by utilizing back-propagation of both the clustering loss function and the cross-entropy loss function. This optimization process improves the accuracy of fault diagnosis. Finally, the proposed method is applied to diagnose bearing faults and analyze the simulation results. The simulation results demonstrate the effectiveness of the method in handling small data volumes and imbalanced data distributions, as well as its strong generalization performance.

TFC-TFECR feature extraction and state recognition of acoustic emission signal of cylindrical roller bearing

ABSTRACT Rolling bearings are widely used in rotating machinery, such as aero-engine spindles, flying machines, wind turbines, etc. Bearing condition monitoring is of practical importance. The acoustic emission (AE) signal has impact and rapid attenuation characteristics. Most existing research on fault diagnosis is not focused on rapid attenuation characteristics. According to this characteristic, a time-frequency coherent and time-frequency energy change rate (TFC-TFECR) method is proposed to identify the AE signals of bearing faults. This paper investigates the effect of the time-frequency coherent (TFC) method on attenuation coefficient. It also focuses on the deviation of the time-frequency energy change rate of the TFC-TFECR method, which is superior to the time-frequency energy. Feature extraction of AE signals from cylindrical roller bearings is carried out through three typical states of cylindrical roller bearings. The feature values of the TFC-TFECR method are input into the SVM model, and the sparrow search algorithm optimises the SVM model. The experimental results show that the method can effectively realise the feature extraction and state recognition of the AE signals of cylindrical roller bearings, and the accuracy rate reaches 99.3827% at 600 r/min and 98.7654% at 1200 r/min. This paper provides a new method for non-destructive testing of rotating machinery bearings.

Automatic Fault Detection Method of Rotating Machinery Bearing Based on Complexity Analysis

In order to improve the working condition stability of rotating machinery bearings, an automatic fault detection method of rotating machinery bearings based on complexity analysis is proposed. The parameters of rotating machinery bearings are detected by using discrete sensor detection method, and the fault characteristics of rotating machinery bearings are extracted by fault type parameter extraction method. The complexity of rotating machinery bearings is detected by wavelet transform and fuzzy logic analysis. Considering the load mutation, noise disturbance, control strategy and other factors of rotating machinery bearings, the fault diagnosis and feature analysis model of rotating machinery bearings is established. The clustering and feature detection of rotating machinery bearings’ fault types are realized by the method of discrete complexity fusion clustering. Based on adaptive feedback compensation and parameter estimation, the output diagnostic error is taken as the fault detection index parameter, and the feature estimation in normal state and fault state is realized by harmonic level detection, so as to realize automatic fault detection of rotating machinery bearings. The simulation results show that this method has high accuracy and small deviation in fault detection of rotating machinery bearings, which improves the ability of automatic fault diagnosis.

Fault Diagnosis of Rotating Machinery Bearings Based on Improved DCNN and WOA-DELM

A bearing is a critical component in the transmission of rotating machinery. However, due to prolonged exposure to heavy loads and high-speed environments, rolling bearings are highly susceptible to faults, Hence, it is crucial to enhance bearing fault diagnosis to ensure safe and reliable operation of rotating machinery. In order to achieve this, a rotating machinery fault diagnosis method based on a deep convolutional neural network (DCNN) and Whale Optimization Algorithm (WOA) optimized Deep Extreme Learning Machine (DELM) is proposed in this paper. DCNN is a combination of the Efficient Channel Attention Net (ECA-Net) and Bi-directional Long Short-Term Memory (BiLSTM). In this method, firstly, a DCNN classification network is constructed. The ECA-Net and BiLSTM are brought into the deep convolutional neural network to extract critical features. Next, the WOA is used to optimize the weight of the initial input layer of DELM to build the WOA-DELM classifier model. Finally, the features extracted by the Improved DCNN (IDCNN) are sent to the WOA-DELM model for bearing fault diagnosis. The diagnostic capability of the proposed IDCNN-WOA-DELM method was evaluated through multiple-condition fault diagnosis experiments using the CWRU-bearing dataset with various settings, and comparative tests against other methods were conducted as well. The results indicate that the proposed method demonstrates good diagnostic performance.

Coupled Hybrid Stochastic Resonance With Multiobjective Optimization for Machinery Dynamic Signature and Fault Diagnosis

Most traditional stochastic resonance methods pose three fundamental limitations in extracting fault signature: (1) simulated impulse signals are insufficient to match the complicated mechanical vibration signals; (2) one-dimensional signals are vulnerable to interference from multiscale noise, which potentially yields false detection; and (3) optimization function in the signal-to-noise ratio largely depends on the advanced requirements of the characteristic frequency, thereby restricting the development for actual operation. Accordingly, this study investigated the dynamic model of rolling bearings and explored the benefits of the coupled hybrid stochastic resonance (CHSR) method for identifying weak signal signatures. In addition, we proposed multi-objective optimization and established a dynamic model of a rolling bearing with four degrees of freedom under a rotor system with external excitation. Overall, the proposed system offers three main advantages: (1) the dynamic model facilitates correspondence with real vibration signals; (2) the CHSR method can process two-dimensional signals and overcome the saturation phenomenon, which enhances the weak useful signal signature describing machinery health; and (3) the cross-correlation spectral kurtosis and the critical amplitude are defined within the multi-objective function to adaptively optimize the parameter adjustment of the CHSR. Thereafter, the proposed method exhibited adequate enhancement ability integrated the study on dynamic simulation and machinery bearings of wind power generator. Moreover, the applied machine learning model further clarified the superior performance of CHSR method in identifying the weak fault signature.

Semi-Supervised Transfer Learning Method for Bearing Fault Diagnosis with Imbalanced Data

Fault diagnosis is essential for assuring the safety and dependability of rotating machinery systems. Several emerging techniques, especially artificial intelligence-based technologies, are used to overcome the difficulties in this field. In most engineering scenarios, machines perform in normal conditions, which implies that fault data may be hard to acquire and limited. Therefore, the data imbalance and the deficiency of labels are practical challenges in the fault diagnosis of machinery bearings. Among the mainstream methods, transfer learning-based fault diagnosis is highly effective, as it transfers the results of previous studies and integrates existing resources. The knowledge from the source domain is transferred via Domain Adversarial Training of Neural Networks (DANN) while the dataset of the target domain is partially labeled. A semi-supervised framework based on uncertainty-aware pseudo-label selection (UPS) is adopted in parallel to improve the model performance by utilizing abundant unlabeled data. Through experiments on two bearing datasets, the accuracy of bearing fault classification surpassed the independent approaches.

Spinning rigid bodies driven by orbital forcing: the role of dry friction

A "circular orbital forcing" makes a chosen point on a rigid body follow a circular motion while the body spins freely around that point. We investigate this problem for the planar motion of a body subject to dry friction. We focus on the effect called reverse rotation (RR), where spinning and orbital rotations are antiparallel. Similar reverse dynamics include the rotations of Venus and Uranus, journal machinery bearings, tissue production reactors, and chiral active particles. Due to dissipation, RRs are possible only as a transient. Here the transient or flip time $t_\textrm{f}$ depends on the circular driving frequency $\omega$, unlike the viscous case previously studied. We find $t_\textrm{f}\sim\omega^{\gamma-1}\mu^{-\gamma/2}$, where $\mu$ is the friction coefficient and $\gamma=0$ ($\gamma=2$) for low (high) $\omega$. Whether RRs really occur depends on the initial conditions as well as on $\mu$ and $H$, a geometrical parameter. The critical $H_\textrm{c}(\mu)$ where RRs become possible follows a $q$-exponential with $q\simeq1.9$, a more restrictive RR scenario than in the wet case. We use animations to visualize the different dynamical regimes that emerge from the highly nonlinear dissipation mechanism of dry friction. Our results are valid across multiple investigated rigid body shapes.

Методы повышения надёжности работы подшипниковых узлов в тяговых асинхронных машинах

The problem of electric rolling stock traction machinery failures caused by electrical erosion of bearings is addressed. Damages inflicted to traction machinery bearings as a result of electric current flowing through them have been known since long ago. In recent time, however, damages of this kind have become more frequent, because frequency and voltage converters for control of three-phase inductor motors are widely used in the electric rolling stock. An overview of factors causing the occurrence of stray currents generated in frequency converter driven traction motors and sources of these currents is presented. Electric current flows through rolling bearings in the area of the contact between the rolling elements with inner and outer rings, thus causing damage to the bearing metal components' rolling surfaces and degrading the quality of lubricants. To study and analyze the currents through bearings, the stray capacitances existing in traction machines are determined, and their calculation methods are defined, the necessity of which is stemming from the fact that these capacitances influence the generation of parasitic currents. The stray capacitances in traction machines include the capacitance between the stator windings and the stator core pack, the capacitance between the stator windings and the rotor, the capacitance between the rotor and the stator core pack, and the capacitances of the bearings. From the electrical point of view, bearings may also be regarded as capacitances. As long as there is a continuous lubricant film between the inner ring and outer rings and the rolling elements, the bearings behave as capacitors. A difference of potentials across the bearing elements may lead to electrostatic discharges and, hence, to discharge currents through the bearings. One of possible ways to enhance the reliability of traction machine bearing units is to use insulated bearings. The change in the impedance of bearings with ceramic coating and hybrid rolling elements depending on the applied voltage frequency and operating temperature is calculated and analyzed. Conclusions regarding the expediency of using hybrid bearings having rings made of bearing steel and rolling elements made of bearing silicon nitride (Si3N4) are drawn.

Research on Rotating Machinery Fault Diagnosis Method Based on Energy Spectrum Matrix and Adaptive Convolutional Neural Network

Traditional intelligent fault diagnosis methods focus on distinguishing different fault modes, but ignore the deterioration of fault severity. This paper proposes a new two-stage hierarchical convolutional neural network for fault diagnosis of rotating machinery bearings. The failure mode and failure severity are modeled as a hierarchical structure. First, the original vibration signal is transformed into an energy spectrum matrix containing fault-related information through wavelet packet decomposition. Secondly, in the model training method, an adaptive learning rate dynamic adjustment strategy is further proposed, which adaptively extracts robust features from the spectrum matrix for fault mode and severity diagnosis. To verify the effectiveness of the method, the bearing fault data was collected using a rotating machine test bench. On this basis, the diagnostic accuracy, convergence performance and robustness of the model under different signal-to-noise ratios and variable load environments are evaluated, and the feature learning ability of the method is verified by visual analysis. Experimental results show that this method has achieved satisfactory results in both fault pattern recognition and fault severity evaluation, and is superior to other machine learning and deep learning methods.

Моделювання ресурсу молотильного барабана зернозбирального комбайна

Abstract. A significant part of agricultural machinery has a seasonal nature of operation, when sudden failures are extremely undesirable. Proceeding from this, modeling the real operating conditions of bearings in design calculations is relevant, and the novelty of solving the problem lies in the use of modern target computer-aided design programs for this. Research goal. Theoretical determination of the service life of agricultural machinery bearings using targeted computer software, taking into account the real conditions of their operation. Making recommendations of a structural nature to eliminate factors that accelerate bearing wear and increase reliability. Methods. Mathematical modeling of the operating conditions of the bearings of the threshing drum of a combine harvester using the target software. Results. The article presents the main reasons for the premature failure of the bearing units of agricultural machinery, considers the types of methods used to calculate and simulate the operating conditions of rolling bearings, taking into account the specifics of machines and mechanisms in which they are used. On the example of the threshing apparatus of a combine harvester, an evaluation characteristic of the adaptation of the existing computer software applications for the calculation of bearings to the operating conditions of agricultural machinery has been carried out. The most reliable software application was selected, which takes into account the maximum number of factors influencing the service life of the bearing assemblies. The reason for the discrepancy between the values of the calculated and actual service life of the bearing assemblies of the threshing drum of a grain harvester is determined, which is due to the still unaccounted temperature deformation factor (drum shaft elongation), which leads to the disappearance of the thermal gap between the rings and rolling elements of the bearings decrease in the efficiency of the nodal seals. Conclusions. 1. The established dependence of the influence of the change and the difference in temperature of the constituent elements of the threshing apparatus unit on the durability of the bearings. It was found that when the difference between the temperatures of the bearing housings and the threshing drum shaft is more than 20 ° C, the thermal gap between its rings and the rolling elements disappears due to the thermal elongation of the drum shaft, which leads to the opening of the seal joints, and therefore to a decrease bearing life. A structural solution is proposed for the modernization of bearing assemblies, which achieves the leveling of the negative impact of thermal elongation of the shaft with minimized interventions in the base structure and associated material costs. A similar solution can be used in most agricultural machinery mechanisms, where, due to low rigidity or for other reasons, it is impossible to have a stable mutual arrangement of components rotating on bearings.

State-Degradation-Oriented Fault Diagnosis for High-Speed Train Running Gears System.

As one of the critical components of high-speed trains, the running gears system directly affects the operation performance of the train. This paper proposes a state-degradation-oriented method for fault diagnosis of an actual running gears system based on the Wiener state degradation process and multi-sensor filtering. First of all, for the given measurements of the high-speed train, this paper considers the information acquisition and transfer characteristics of composite sensors, which establish a distributed topology for axle box bearing. Secondly, a distributed filtering is built based on the bilinear system model, and the gain parameters of the filter are designed to minimize the mean square error. For a better presentation of the degradation characteristics in actual operation, this paper constructs an improved nonlinear model. Finally, threshold is determined based on the Chebyshev’s inequality for a reliable fault diagnosis. Open datasets of rotating machinery bearings and the real measurements are utilized in the case studies to demonstrate the effectiveness of the proposed method. Results obtained in this paper are consistent with the actual situation, which validate the proposed methods.

Load capacity of lubricated bismuth bronze bimetal bearing under elliptical sliding motion

Leaded tin bronze alloys are widely used in heavy machinery bearings operating in boundary and mixed lubrication regions due to the excellent dry lubrication properties of lead. However, restrictions on the use of lead have created an increasing demand for lead-free or low-lead bearing materials. In the present study, suitability of a novel bismuth bronze bimetal material for possible substitution of leaded tin bronze was studied with a special thrust bearing test device, which simulates the contact conditions in the main thrust bearing of mineral crushers. The oil-lubricated test bearings have a flat-on-flat type contact with oil grooves and a constant eccentric motion against a case hardened steel counter plate under a periodically increased axial pressure. The test was continued until a sudden rise in friction, which indicates bearing failure and risk of an imminent seizure. The bismuth bronze showed a load capacity of the same level with the reference material, continuously cast CuSn10Pb10. Characterization by electron microscopy showed that the dry-lubricating bismuth precipitations had a fine grain size and an even distribution, which explains the good load carrying capacity. It was concluded that the bismuth bronze has potential for substituting the leaded tin bronzes in the studied operating conditions.

Frictional analysis of deep-groove ball bearings with varying circumference radial preloads

Circumference radial preload due to the interference fit is of a great significant influence on rolling frictional power loss of bearing and service life of machinery. In this article, an analytical method where circumference radial preload is first involved is presented for determining the value of rolling friction power loss of ball bearing (SKF-6205). The experiments under different operating conditions for the bearing were carried out which verify the validity of our proposal. The work described in this article can be regarded as a foundation for the accuracy thermal modeling and friction power loss analysis of the deep-groove ball bearings in machinery.

Data Acquisition System and Signal Processing Technique for Bearing Fault Analysis

Background/Objectives: In rotating machinery bearings plays an important role to reduce rotational friction and support radial and axial loads. Abnormal vibration affects the lifetime of machinery. This abnormality may causes by bearing Fault. In order to monitor the machines health and it is needed to focus the functionality of bearing components like ball, inner race, outer race and cage component. Methods/Statistical Analysis: There are many methodologies available to measure and analyze the fault of bearing but they utilize expensive resource to identify the bearing system. The proposed system explains and utilizes a cost effective, less time consuming system, is used for bearing faults identification. The novel system provides real-time data acquisition and signal analysis for Fault detection. Low cost Micro Electro Mechanical System (MEMS) Accelerometer sensor is used for vibration measurement, LABJACK U3 Data acquisition system connected with Raspberry Pi-2 CPU which acquires and process with high performance of acquired data by using python application software. Application/Improvements: The online signal processing technique explains the abnormality of bearing time domain data's amplitude easily classify the faulty bearing. Fast Fourier Algorithm converts time domain to frequency domain which easily represents faulty components frequency with high amplitude. Short Time FFT (STFT) shows the color map with high color intensity of vibration amplitude of faulty components and its time instant. These data representation technique is utilized to easily identify fault of bearing components.

A New Stress-Based Fatigue Life Model for Ball Bearings

Life prediction is an essential tool in optimizing the selection of rolling bearings in machinery. Over the last several decades, the life factor-based model, based on the Lundberg and Palmgren (LP) model, has been widely accepted both for industry standards and in manufacturers' catalogs in the bearing industry. However, modeling the increasingly demanding operating conditions of modem machinery, the LP model, even modified by life factors, tends to underestimate bearing fatigue lives, particularly for lower load applications. This is particularly critical for aerospace engine and power transmission applications. A new stress-based fatigue life model is proposed (2). It has-been developed in a way such that fatigue life is associated purely with the induced stress field in a power function form. Because the model is stress-based, the empirical data required to complete the model can be obtained using element testing in lieu of the complete bearing testing employed by LP. The fatigue data for the new fatigue life model were generated using a ball/v-ring test rig in which balls are endurance tested to failure. This model, proposed based on element testing, has been compared with LP and the loannides-Harris model (IH) models in a meaningful number of actual applications. This research is basically the extension of the previous work by Yu. To demonstrate the feasibility of the proposed model, it is desired, from a practical standpoint, to develop relatively simple equations which relate bearing life to the applied load. It starts with the assumption LP made that bearing life could be represented by bearing geometry and applied load under a simple Hertz loading. The actual deep groove ball bearing data are used to evaluate the results among these models. The result of the analysis shows that this simple model derived from the stressbased model could also be a more accurate predictor of bearing life than either the LP or IH model. Presented at the 55th Annual Meeting Nashville, Tennessee May 7–11, 2000

Bearing influenced rotor dynamics

This paper presents a brief summary of the influence of journal bearings in machinery with special reference to rotor dynamic problems. The journal bearings considered are Newtonian, non-Newtonina and magnetohydrodynamic and their influence on the critical speeds and stability of rotors is investigated

Analytic techniques for shipboard structure-borne noise control

Ships like many other modern forms of transportation have become noisier through the years. This is in no small part due to propulsion equipment working at higher horsepowers and pressures and oftentimes packaged more densely into lighter, more efficient structures which can unfortunately be acoustically more transparent. The primary path of sound transmission through ships is via structural waves which propagate throughout the ship's structure. These waves are first generated by the acoustic energy of the machinery source coupling with the adjacent structure or directly via vibrations generated by forces transmitted through the machinery bearings. The ship's structure then provides an efficient transmission medium for sound energy to propagate from one part of the ship to another. The present paper describes the analytical tools that are available to the shipboard noise-control engineer to deal with the structure-borne noise and vibration isolation design problems.

A clearance indicator for high-speed bearing journals : J. L. Huffman, ISA trans., 1 (4) (1962) 329–339; Battelle tech. rev., 12 (2) (1963) 71a

Leaded tin bronze alloys are widely used in heavy machinery bearings operating in boundary and mixed lubrication regions due to the excellent dry lubrication properties of lead. However, restrictions on the use of lead have created an increasing demand for lead-free or low-lead bearing materials. In the present study, suitability of a novel bismuth bronze bimetal material for possible substitution of leaded tin bronze was studied with a special thrust bearing test device, which simulates the contact conditions in the main thrust bearing of mineral crushers. The oil-lubricated test bearings have a flat-on-flat type contact with oil grooves and a constant eccentric motion against a case hardened steel counter plate under a periodically increased axial pressure. The test was continued until a sudden rise in friction, which indicates bearing failure and risk of an imminent seizure. The bismuth bronze showed a load capacity of the same level with the reference material, continuously cast CuSn10Pb10. Characterization by electron microscopy showed that the dry-lubricating bismuth precipitations had a fine grain size and an even distribution, which explains the good load carrying capacity. It was concluded that the bismuth bronze has potential for substituting the leaded tin bronzes in the studied operating conditions.