Condition Monitoring Of Rolling Bearings Research Articles

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.

Investigation on the influence of temperature-variation clearance on the frequency of rolling bearing defects

The clearance between the inner ring (IR) of rolling bearing and rotor is directly proportional to the temperature rise. The increased friction between the IR and rotor, as well as changes in the frequency of rolling bearing defects, and the accuracy of bearing health monitoring decreases. Existing research cannot effectively solve this problem. In response to the above issues, in this paper, the clearance between the bearing IR and the rotor and the internal clearance of bearing were considered, and the clearance was considered as the motion boundary of the rotor inside the IR, dynamic models of the bearing IR, outer ring (OR), and rolling element defects were established, and the trend of rolling bearing defects frequencies with temperature was analyzed, then the rationality of the dynamic models were verified through experimental methods. The research results show that the friction between the rotor and the IR increases with increasing temperature, and the speed of the IR decreases accordingly. The characteristic frequencies of defects in the IR, OR, and rolling element of rolling bearings have decreased by approximately 6.42%, 8.07%, and 6.93% compared to the theoretical values without considering temperature, respectively. This indicates that when identifying the frequency of rolling bearing defects at different temperatures, the frequency deviation caused by the increase in clearance between the IR and the rotor should be considered. The research results may serve as an important basis for fault diagnosis and condition monitoring of rolling bearings.

SEACKgram: a targeted method of optimal demodulation-band selection for compound faults diagnosis of rolling bearing

Rolling bearing plays an important role in carrying and transmitting power in rotating machinery, and the bearing fault is easy to lead to mechanical accidents, resulting in huge losses and casualties. Therefore, the condition monitoring and diagnosis of rolling bearings are very important to improve the safety of equipment. Compound fault is a common fault evolved from the initial defect, which is characterized by randomness, coupling, concealment, and secondary. The existence of these characteristics brings great challenges to the accurate diagnosis of compound faults. In the diagnosis of compound faults, the traditional methods that select the single optimal demodulation frequency band for analysis and identification sometimes cannot completely extract multiple fault components, which are prone to miss diagnosis and misdiagnosis. In order to solve this problem, the SEACKgram method is proposed by constructing a Square Envelope Unbiased Autocorrelation Correlation Kurtosis (SEACK) index. The frequency band of the original signal is divided by the Maximal Overlap Discrete Wavelet Packet Transform, and the SEACK index is used to quantitatively describe the fault signals of different frequency bands. According to the different fault periods, the resonant frequency bands of the maximum SEACK value are selected, then the resonance band signal is analyzed by square envelope spectrum, and the fault type is identified according to the fault characteristic frequency. The simulated and experimental vibration signals of rolling bearings with compound faults are used to verify the feasibility of the proposed method. The results show that the proposed SEACKgram can improve the accuracy of compound faults identification and would be applied in engineering practice to a certain extent.

Health Management of Bearings Using Adaptive Parametric VMD and Flying Squirrel Search Algorithms to Optimize SVM

Bearing, as one of the core parts of rotating machinery, has a running state which is related to the overall operation of the system. Due to the bearing structure and its complex operating environment, running condition monitoring and fault diagnosis is always a key problem in the field of bearing health management, which is of great significance for bearing maintenance and equipment reliability and safety. In view of the difficulty in parameter selection and poor feature extraction ability of variational mode decomposition (VMD) in existing feature extraction, this paper uses the flying squirrel search algorithm (SSA) to optimize the parametric of decomposition layer k and penalty factor α in VMD, and forms an adaptive VMD signal decomposition method. To solve the problem of high dimensionality and long extraction time of multi-domain fault feature set, kernel principal component analysis (KPCA) is used to reduce feature dimensionality. Then, the processed features are input into the support vector machine (SVM) for fault diagnosis and classification, and the parameter optimization ability of SSA is used again to build the SSA-SVM fault diagnosis model. To evaluate the running state of bearings, an alarm threshold method based on the root mean square value calculated by cosine similarity and 3σ is proposed to divide samples of different health states. Finally, the method constructed in this paper is compared with other methods by using simulation and experimental data sets, and the running condition monitoring and fault diagnosis of rolling bearings are successfully realized, which shows the superiority and effectiveness of the method proposed in this paper.

Correlation warping radius tracking for condition monitoring of rolling bearings under varying operating conditions

Health indicators (HI) are crucial in early fault alarm and degradation monitoring of mechanical failures. In recent years, many HIs are developed and reported, however, a constructed HI which is more sensitive to incipient defect, robust to varying operating conditions and is interpretable to characterize the degradation process is still remain to be studied. To address this issue, a more powerful and effective HI applicable for nonstationary operating condition is studied in this paper. Inspired by chaos fractal theory (CFT) and slowly varying damage dynamics theory (SVDDT), this paper calculates the range of correlation warping radius (CWR) of each segment of signal through phase space reconstruction, and then finds that the boundary of correlation warping radius range (CWR-B) in normal conditions follows a fixed linear relationship. Through the application of this linear relationship, a new health monitoring indicator for vibration signals of rotating machinery has been proposed, through correlation warping radius tracking measurement (CWRTM). The proposed indicator not only has outstanding monitoring advantages for rotating machinery in a cyclo-stationary condition, but also applicable for machinery condition monitoring under variable operating conditions, achieving the unity of methods for condition monitoring in stationary and variable operating conditions. Compared with some typical health indicators, the experimental results on bearing run-to-failure dataset and wind turbine generator bearing field dataset show that the proposed indicator in this article is not only sensitive to early-stage fault, but also validated robust to varying operating conditions.

Investigation of Feature Engineering Methods for Domain-Knowledge-Assisted Bearing Fault Diagnosis.

The engineering challenge of rolling bearing condition monitoring has led to a large number of method developments over the past few years. Most commonly, vibration measurement data are used for fault diagnosis using machine learning algorithms. In current research, purely data-driven deep learning methods are becoming increasingly popular, aiming for accurate predictions of bearing faults without requiring bearing-specific domain knowledge. Opposing this trend in popularity, the present paper takes a more traditional approach, incorporating domain knowledge by evaluating a variety of feature engineering methods in combination with a random forest classifier. For a comprehensive feature engineering study, a total of 42 mathematical feature formulas are combined with the preprocessing methods of envelope analysis, empirical mode decomposition, wavelet transforms, and frequency band separations. While each single processing method and feature formula is known from the literature, the presented paper contributes to the body of knowledge by investigating novel series connections of processing methods and feature formulas. Using the CWRU bearing fault data for performance evaluation, feature calculation based on the processing method of frequency band separation leads to particularly high prediction accuracies, while at the same time being very efficient in terms of low computational effort. Additionally, in comparison with deep learning approaches, the proposed feature engineering method provides excellent accuracies and enables explainability.

Advanced Detection of REB Defects through Sound Emission using Envelope Analysis and Spectral Kurtosis

Rolling elements bearings (REBs) are considered between the critical components in rotating machinery and their failures can provoke severe damage to the machine. Monitoring the condition of these components is essential to ensure the availability of the machine and improve its reliability. This article presents a low-cost acoustic approach based on the smartphone to monitor the bearing components. This approach stands on the use of a stethoscope connected to the smartphone via input Jack, to acquire the acoustic emission of the bearing at specific points. Firstly, the Hilbert transform (HT) was performed on acoustic signals to derive the envelope signal. Then, the Fast Fourier Transform (FFT) was applied to calculate the spectrum of the envelope signal. In the case of a noisy envelope spectrum where the fault signature is not noticeable, the Spectral kurtosis (SK) will be implemented to design an optimal filter to filter the acoustic signal using the Fast Kurtogram. After the filtering step, the process will be repeated to calculate the envelope spectrum. This study evaluates a defective bearing with a small inner race fault under different operating speeds (648, 1240, and 1816 rpm). Finally, the experimental results indicate that the proposed approach shows good results compared to the theoretical results for the early detection and identification of bearing failures. Furthermore, this technique is highly cost-effective and practical for rolling bearing condition monitoring.

Integrated intelligent fault diagnosis approach of offshore wind turbine bearing based on information stream fusion and semi-supervised learning

Offshore wind turbines play a vital role in transferring wind energy to electricity, which could help relieve the energy crisis and improve the global climate. In general, offshore wind turbines are installed open sea to avoid the potential interruption of people’s daily life. In such kind of harsh operating environment, the wind turbine transmission system is prone to failure, especially for the rolling bearings. Therefore, it is crucial to conduct condition monitoring of rolling bearings to ensure the safe and efficient operation of offshore wind turbines. Intelligent fault diagnosis is a research hotspot for condition monitoring of rolling bearings. However, the existing intelligent fault diagnosis techniques have some limitations. For example, most of the existing techniques were developed based on single sensory data, which can lead to inaccurate and unstable diagnostic results. Moreover, most existing techniques implicitly assume that there are sufficient labeled samples for classifier training. This may not be the case for offshore wind turbines where the labeled samples are limited. To address the aforementioned issues, an intelligent fault diagnosis technique by integrating an information stream fusion and a semi-supervised learning approach is proposed in this study. In the proposed method, a coupled convolutional residual network is proposed to realize the information streams fusion, in which the vibration signal and acoustic emission signal are served as the inputs of the proposed network, and then a concatenation operation is used to fuse the features obtained from two information streams. Meanwhile, a semi-supervised learning approach is also proposed, which can utilize the labeled samples, the correctly predicted samples, and the unlabeled samples to improve diagnostic accuracy. The diagnostic result on the experimental offshore wind turbine bearing dataset demonstrates that the proposed method achieves the highest diagnostic accuracy compared to existing comparative methods.

Intelligent fault diagnosis method for rolling bearing using WMNRS and LSSVM

A weighted multi-neighborhood rough set (WMNRS) algorithm is designed to resolve the issue in which the neighborhood radius must be adjusted iteratively and cannot be automatically determined in the neighborhood rough set. This algorithm combined with the least squares support vector machines (LSSVM) is used for analyzing rolling bearing condition monitoring data; consequently, an intelligent fault diagnosis method is proposed. Specifically, the time-domain and frequency-domain features are extracted from the collected vibration signals to construct an original feature set. The WMNRS algorithm is then applied to screen the primary sensitive components from the constructed feature set. Finally, an optimized LSSVM is utilized to recognize the fault types. The developed method is validated on a public dataset and a measured rolling bearing dataset. The results demonstrate that the method achieves excellent diagnostic performance. Furthermore, the proposed method has some supremacy regarding running time.

Use of two-dimensional refined composite multi-scale time-frequency dispersion entropy for rolling bearing condition monitoring

Rolling bearing vibration signals with local failure usually contain rich fault characteristic in the time and frequency domains. Two-dimensional multi-scale dispersion entropy (MDE2D) is a nonlinear dynamic analysis method widely used in image signal processing. However, the multi-scale coarse-grained process of MDE2D is relatively complex and the computational cost is high. Therefore, this paper develops a two-dimensional multi-scale time-frequency dispersion entropy (MTFDE2D) to solve the problem of high calculation cost of MDE2D. Unfortunately, the length of the coarse-grained sequence used in MTFDE2D will become shorter as the scale factor increases, resulting in a loss of potential useful information. To address this, a two-dimensional refined composite multi-scale time–frequency dispersion entropy (RCMTFDE2D) is proposed. In RCMTFDE2D, the utilization of information contained in the original date is first effectively improved through the coarse-grained process of sliding values of adjacent elements, and then the refined process is used to avoid the fluctuation of entropy on large scale factors. Furthermore, the rolling bearing test data under two different operating conditions are analyzed. The results illustrate that RCMTFDE2D can successfully extract the rolling bearing fault information in time–frequency domain, and the condition monitoring method based on RCMTFDE2D can effectively identify the rolling bearings in different health states and has the highest recognition rate.

Rolling bearing condition monitoring method based on multi-feature information fusion

In machining operations, the misalignment of the bearing assembly or imbalanced load often leads to deflection and failure of the tool spindle. The use of single feature information does not accurately monitor the complex working conditions. Considering this, this paper proposes a rolling bearing running condition monitoring method which is based on multiple feature information. Firstly, a multi-dimensional feature matrix is obtained by extracting the features of a single type of raw data in the time domain, frequency domain, and time-frequency domain, and then the dimensionality of the matrix is reduced by principal component analysis (PCA). An entropy weight improved the D-S(EWID-S) evidence theory is proposed. By updating the initial evidence source, and applying the Euclidean distance of the spatial centroid, the fusion results were evaluated. Finally, a test rig for eccentric bearing load operation is developed to obtain the vibration signals at two distinct locations and to confirm the proposed method. The test results show that the condition monitoring method based on the PCA and EWID-S evidence theory can effectively identify the bearing operating at different degrees of deflection. At the same time, by comparing with other improved D-S evidence theory methods, it is verified that this method has more advantages in information fusion and bearing condition monitoring.

A Three-Dimensional Vibration Data Compression Method for Rolling Bearing Condition Monitoring

In condition monitoring for rolling bearings, it has achieved good diagnostic performance and clear mechanistic interpretation based on vibration data. The high sampling frequency of data collection preserves fault characteristics but brings the problem of big data. An effective way to reduce this problem is to apply data compression. However, in order not to affect the diagnostic performance of data, it is difficult to improve the compression ratio further. Inspired by the binarization method, the compression dimension of the bit cost of a single sample point is first introduced into the fault-mechanism-based method in this article. On this basis, a three-dimensional data compression method is proposed, and it is subsequently validated with two real-bearing datasets. Two performance metrics, including a newly defined one, are utilized to compare the proposed method with the five existing methods. The comparison results show that the proposed method significantly improves the compression ratio of data but maintains good diagnostic performance.

Multilayer-graph-embedded extreme learning machine for performance degradation prognosis of bearing

As a key component in electromechanical systems, the health condition monitoring of rolling bearings is crucial for the safe operation of the whole system. For this purpose, the prediction of rolling bearing performance degradation is indispensable. To improve the accuracy of Extreme Learning Machine (ELM) based algorithms for the bearing performance degradation prediction, a novel Graph embedded ELM autoencoder (GEELM-AE) is first constructed via combining the graph embedding framework and then a performance degradation prediction method of Multilayer-Graph-embedded ELM (MGEELM) with a deep framework is developed by stacking multiple GEELM-AEs. The MGEELM algorithm not only extracts the abstract features in the data by virtue of its deep structure, but also maintains the local structural information and non-local structural information of the data during feature extraction. Accordingly, the performance degradation trend of rolling bearings can be accurately predicted with the proposed MGEELM. Moreover, the MGEELM algorithm does not require reverse fine-tuning, which greatly reduces the training time and improves the prediction efficiency. The advantages of the proposed method are validated by rolling bearing life-cycle vibration data. In the experimental analysis, the four performance indicators of the proposed method obviously outperform the comparative methods, which indicates the superior ability of the proposed method in tracking the bearing operating condition evolution.

Impact of noise model on the performance of algorithms for fault diagnosis in rolling bearings

Condition monitoring of rolling bearings is attracting much interest since most of the production slowdowns depends on the damaging of these components. Several algorithms for fault detection have appeared in the technical literature in the last decade. In most cases, performance is assessed over both synthetic and experimental data. Unfortunately, the computer simulations adopt signal models that are trivial and are not able to predict the actual performance on the field. In this work we propose a framework suitable to fairly, quantitatively and objectively compare different algorithms for fault detection in rolling bearings. The vibration signal is obtained through computer simulations. The signal entailed by the damage is generated through the model at “impact-delay-line” already available in the technical literature. The machine noise is generated as a wideband component with the possible superposition of narrowband components. The wideband component has been modeled as additive white Gaussian noise, additive white noise drawn from an α-stable distribution and additive noise stemming from an autoregressive process. Narrowband components are modeled through trains of Gaussian pulses. The performance of three well known algorithms for fault detection are compared in terms of capability in identifying the theoretical cyclic frequencies related to a damage. In these scenarios the behavior of fault detectors are definitely far from that predicted by classical wideband noise models like, for instance, additive white Gaussian noise.

Rolling bearing fault diagnosis based on information fusion and parallel lightweight convolutional network

With the development of technologies such as Internet of Things and big data, the realization of fusion and cross analysis of multi-sensor signals provides the possibility for comprehensive condition monitoring and intelligent fault diagnosis of rolling bearings. Considering the bottleneck that deep neural networks with complex parameters and a single convolution kernel style may cause high computational effort and information loss, an intelligent fault diagnosis method based on information fusion and parallel lightweight convolutional network is proposed. Firstly, a normalized pulse energy kurtosis weighted rule is constructed to enable the fusion of multi-channel vibration signals. What’s more, a novel lightweight convolutional neural network is designed to achieve feature extraction and classification. Finally, a novel nonlinear piecewise activation function is introduced to further improve nonlinear learning ability. The effectiveness and superiority are verified by bearing data sets with different speeds and loads. Compared with other models, our diagnostic framework has better effect and provides a new idea for intelligent fault diagnosis.

Intelligent bearing fault diagnosis method combining mixed input and hybrid CNN-MLP model

Rolling bearings are one of the most widely used bearings in industrial machines. The technical condition of the rolling bearings has a significant impact on the total condition of rotating machinery. Typically, the rolling bearing condition monitoring is based on signal processing from accelerometers mounted on the machine housing. AI-based signal processing methods allow achieving high results in the diagnosis of rolling bearings. Moreover, hybrid NN-based signal processing methods provide the best diagnostic results. However, the diagnostic value of each signal from the housing-mounted accelerometers is highly depend on the location of the corresponding accelerometer. On the other hand, mounting a sensor on the rotating shaft allows expanding and increasing the signal diagnostic value. This paper proposes a novel hybrid CNN-MLP model-based diagnostic method which combines mixed input to perform rolling bearing diagnostics. The method successfully detects and localizes bearing defects using acceleration data from a wireless acceleration sensor which is mounted on a rotating shaft of the machine. The detection and localization efficiency of the hybrid model with various size datasets (more than 27 k samples and about 1900 samples) for training was estimated. Moreover, the detection and localization efficiency of the trained hybrid model with a dataset of another shaft rotating speed for testing was estimated. The experimental results show that applying the hybrid model allows detecting and localizing the bearing faults with up to 99.6% accuracy.

Condition Monitoring of Rolling Bearing Based on Multi-Order FRFT and SSA-DBN

Owing to the symmetry of the rolling bearing structure and the rotating operation mode, the rolling bearing works in a complex environment. It is very easy to be submerged by noise and misdiagnosis. For the non-stationary signal in variable speed state, this paper presents a condition monitoring method based on deep belief network (DBN) optimized by multi-order fractional Fourier transform (FRFT) and sparrow search algorithm (SSA). Firstly, the fractional Fourier transform based on curve feature segmentation is used to filter the fault vibration signal and extract the fault feature frequency. Then, the fault features are input into the SSA-DBN model for training, and the bearing fault features are classified, identified, and diagnosed. Finally, the rotating machinery fault simulator in the laboratory of Ottawa University is taken as the practical application object to verify the effectiveness of the method. The experimental results show that the proposed method has higher recognition accuracy and stronger stability.

A Hybrid Method for Condition Monitoring and Fault Diagnosis of Rolling Bearings With Low System Delay

Vibration-based condition monitoring techniques are commonly used to detect and diagnose failures of rolling bearings. Accuracy and delay in detecting and diagnosing different types of failures are the main performance measures in condition monitoring. Achieving high accuracy with low delay improves system reliability and prevents catastrophic equipment failure. Further, delay is crucial to remote condition monitoring and time-sensitive industrial applications. While most of the proposed methods focus on accuracy, slight attention has been paid to addressing the delay introduced in the condition monitoring process. In this paper, we attempt to bridge this gap and propose a hybrid method for vibration-based condition monitoring and fault diagnosis of rolling bearings that outperforms previous methods in terms of accuracy and delay. Specifically, we address the overall delay in vibration-based condition monitoring systems and introduce the concept of system delay to assess it. Then, we present the proposed method for condition monitoring. It uses Wavelet Packet Transform (WPT) and Fourier analysis to decompose short-duration input segments of the vibration signal into elementary waveforms and obtain their spectral contents. Accordingly, energy concentration in the spectral components-caused by defect induced transient vibrations-is utilized to extract a small number of features with high discriminative capabilities. Consequently, Bayesian optimization-based Random Forest (RF) algorithm is used to classify healthy and faulty operating conditions under varying motor speeds. The experimental results show that the proposed method can achieve high accuracy with low system delay.

Feature-Engineering für die Zustandsüberwachung von Wälzlagern mittels maschinellen Lernens

In rotating machinery, rolling bearings are often the components limiting service life. To avoid unforeseen downtimes, they have to be maintained. For reasons of safety and cost optimization, condition-based maintenance is increasingly being used. Knowing the condition of the components that are critical to wear is essential for this maintenance approach. The insight about the condition is achieved by means of suitable measurement variables, which can be used to automatically detect the condition of the components using machine learning. The quality of the condition monitoring is strongly dependent on the available measurement data and its preprocessing. For condition monitoring of rolling bearings, structure-borne sound signals can be used. The decisive factor here is to determine so-called features from the high-frequency sampled structure-borne sound signals. These features are supposed to reflect the characteristic properties of the measured signals. At the same time, the amount of data is considerably reduced. In this article, different methods of feature engineering based on structure-borne sound are investigated. For this purpose, the wear of rolling bearings is considered in the context of endurance tests. A new feature generation method is presented and compared to common methods from literature.

METHODS OF CONDITION MONITORING AND DIAGNOSTICS OF ROLLING BEARINGS OF CENTRIFUGAL MACHINES AND MECHANISMS

The perfection of methods and means of non-destructive testing and technical diagnostics is determined by the level of development of science and modern industrial technologies. The desire to develop technologies determines the growing needs for monitoring the state of substances, materials, products, and now the control of the state of the natural environment is becoming more and more relevant. Methods and means of condition monitoring and diagnostics of rolling bearings have been developed and developed for more than 60 years. Despite certain successes, today there is no information about the veracity of ways to solve this problem. The paper provides a fairly brief overview of methods and methods for monitoring the condition and diagnosis of rolling bearings, and also describes one of the newest trends in this field – the analysis of the properties of the characteristic function of vibroacoustic signals in order to determine the condition of the objects of control and, in particular, rolling bearings. It is shown that the magnitude of the module and the area of the characteristic function of the vibroacoustic signal are very effective criteria for assessing the technical condition of a rolling bearing.