Bearing Condition Monitoring Research Articles

Enhanced multi-layer Ramanujan decomposition: an effective rolling bearing condition monitoring method

ABSTRACT Due to the influence of strong noise, it is difficult to extract and identify the fault features of rolling bearing when it is slightly damaged, which greatly increases the risk of fault miss detection. In this paper, the enhanced multi-layer Ramanujan decomposition (EMRD) method is proposed. On the one hand, EMRD method adopts accurate spectrum segmentation technology (ASST) to realise adaptive frequency band division. ASST firstly minimises the frequency erosion caused by noise through prior-unknown blind deconvolution method based on SOSO (SOSO-PUBD), then obtains relatively pure frequency distribution through power spectral density function, and finally realises frequency band segmentation by minimum segmentation method. On the other hand, EMRD method obtains the optimal number of decomposition modes in the form of multi-layer decomposition, and selects the optimal mode by Ramanujan spectrum signal-to-noise ratio index. The comparative experimental results show that EMRD method has excellent accuracy of frequency band segmentation and meets the requirements of rolling bearing fault diagnosis.

Nonlinear characteristics in the cylindrical roller bearing with raceway defects

Cylindrical roller bearings (CRBs) are subjected to significant cyclic stress under extreme operating conditions. This leads to the formation of localized wave defects on the raceway surface, such as burn. Thus, a new mathematical model is proposed to consider the local wave defects on the raceway caused by burns in this paper. Further, a dynamic model of CRB incorporating raceway defects is developed based on the contact relationships between components, and the proposed model is verified by measured acceleration responses. The effects of local wave defects on the nonlinear vibration characteristics of the bearings are studied and the results show that an increase in the number of defect waves reduces the additional displacement of rollers passing through the defect area, obscuring signal characteristics induced by the defect. Moreover, the presence of the defect influences the nonlinear interaction between the roller and raceway, which subsequently affects the cage whirling motion. This study of bearing dynamic behavior in this paper can contribute to the condition monitoring of bearings.

Influence of lubrication state transition on dynamic characteristics of deep groove ball bearing with localized defect in thermal environment

The accuracy of the vibration response in a defective bearing dynamics model depends on the precise representation of the lubrication friction state between the rolling element (RE) and the raceway within the model. In this study, a fault dynamic model for outer ring defect of deep groove ball bearings (DGBBs), considering the lubrication state transition in a thermal environment, is established. This model accounts for the asperity contact effect during the lubrication state transition and integrates the lubrication-friction model for temperature changes into the dynamic model when skidding occurs. The influence of lubrication state change on the fault frequency of outer ring of DGBB in thermal environment is studied. The experimental and simulation results indicate that the lubrication state of the bearing is gradually deteriorated from elastohydrodynamic lubrication to mixed lubrication with the increase of working temperature. The transformation of the lubrication state is shown to have a significant effect on friction, resulting in the fault frequency of the outer ring increasing with temperature, which exhibits substantial deviation in the thermal environment. In the temperature range of 30 °C–150 °C, the deviation of defect frequency reaches 15.5%, which affects the accuracy of bearing fault diagnosis. This study may offer recommendations for enhancing the condition monitoring of rolling bearings under extreme working conditions.

Lubrication condition monitoring of journal bearings in diesel engine based on thermoelectricity

There is still a lack of effective lubrication condition monitoring methods in the field of diesel engines. The paper proposes a novel thermoelectric approach to divide the lubrication state of bearings. First, the generation mechanism of thermoelectric potential on bearings is clarified. Then, both experimental and simulation studies are done, and a strong correlation between lubrication and thermoelectric potential is shown. The film thickness and temperature are further confirmed as significant factors influencing thermoelectric potential. Generally, the thermoelectric potential increases with temperature. However, a small film thickness ratio (when the film thickness ratio is less than 4) will suppress the thermoelectric potential. Three typical lubrication states of bearings are distinguished through thermoelectric potential and supported by the Stribeck curve results. Moreover, the significant influence of lubrication on the bearing is confirmed through the analysis of surface morphology and composition.

A novel RUL prediction method for rolling bearings based on dynamic control chart and adaptive incremental filtering

Abstract Degradation of rolling bearings typically consists of two stages: a stable stage (Stage I) characterized by stable fluctuations in the health indicator (HI), and a degradation stage (Stage II) where early damage leads to HI degradation, eventually reaching the failure threshold. Therefore, to achieve online RUL prediction for bearings, three aspects should be studied: 1) Degradation modeling; 2) Inter stage change point identification; 3) Online degradation state updating. Firstly, a two-stage degradation model is constructed by simultaneously considering inherent randomness, individual differences, and measurement errors. Then, a dynamic statistical process control (SPC) method is proposed to identify the change point from Stage I to Stage II. The SPC is designed to dynamically control limits based on the bearing's condition monitoring (CM) data to prevent false alarms. An adaptive incremental filtering (AIF) is proposed to update the degradation states by simultaneously considering the state increment and the dynamics of the system noise and measurement noise. The effectiveness of the proposed method is validated on 16004 bearing test data and XJTU-SY bearing data. Results show that the proposed method can accuracy identify the change point and improve the accuracy of the prediction result during stage II.

Highly Robust and Self-Adhesive Soft Strain Gauge via Interface Design Engineering.

Highly robust soft strain gauges are rapidly emerging as a promising candidate in the fields of vital signs and machine conditions monitoring. However, it is still a key challenge to achieve high-performance strain sensing in these sensors with mechanical/electrical robustness for long-term usage. The multilayer structural design of sensors enhances sensing performance while the interfacial connection of heterogeneous materials between different layers is weak. Herein, inspired by the efficient perception mechanism of scorpion slit sensilla with tough interface interconnections, the synergy of ultra-high electrical performance and mechanical robustness is successfully achieved via interface design engineering. The developed multilayer soft strain gauge (MSSG) exhibits a strain sensitivity beyond 105, a lower detection limit of 8.3µm, a frequency resolution within 0.1Hz, and cyclic stability over 63000 strain cycles. Also, the tough interface improves the level of heterogeneous integration in the MSSG which allows to endure different stresses. Furthermore, an MSSG-based wireless strain monitoring system is developed that enables applications on different complex dynamic surfaces, including accurate identification of human throat activity and monitoring of rolling bearing conditions.

A novel deep learning framework for rolling bearing fault diagnosis enhancement using VAE-augmented CNN model

In the context of burgeoning industrial advancement, there is an increasing trend towards the integration of intelligence and precision in mechanical equipment. Central to the functionality of such equipment is the rolling bearing, whose operational integrity significantly impacts the overall performance of the machinery. This underscores the imperative for reliable fault diagnosis mechanisms in the continuous monitoring of rolling bearing conditions within industrial production environments. Vibration signals are primarily used for fault diagnosis in mechanical equipment because they provide comprehensive information about the equipment's condition. However, fault data often contain high noise levels, high-frequency variations, and irregularities, along with a significant amount of redundant information, like duplication, overlap, and unnecessary information during signal transmission. These characteristics present considerable challenges for effective fault feature extraction and diagnosis, reducing the accuracy and reliability of traditional fault detection methods. This research introduces an innovative fault diagnosis methodology for rolling bearings using deep convolutional neural networks (CNNs) enhanced with variational autoencoders (VAEs). This deep learning approach aims to precisely identify and classify faults by extracting detailed vibration signal features. The VAE enhances noise robustness, while the CNN improves signal data expressiveness, addressing issues like gradient vanishing and explosion. The model employs the reparameterization trick for unsupervised learning of latent features and further trains with the CNN. The system incorporates adaptive threshold methods, the "3/5" strategy, and Dropout methods. The diagnosis accuracy of the VAE-CNN model for different fault types at different rotational speeds typically reaches more than 90 %, and it achieves a generally acceptable diagnosis result. Meanwhile, the VAE-CNN augmented fault diagnosis model, after experimental validation in various dimensions, can achieve more satisfactory diagnosis results for various fault types compared to several representative deep neural network models without VAE augmentation, significantly improving the accuracy and robustness of rolling bearing fault diagnosis.

Time-Series Feature Extraction by Return Map Analysis and Its Application to Bearing-Fault Detection

Developing new features for time-series characterization is a current challenge in data science and machine learning. In this paper, we propose a new metric based on a simple and efficient algorithm, namely, the return map. The return map analysis is well established in the field of non-linear dynamics, in particular, for fitting the parameters of a chaotic system from a waveform, or to attack a chaotic communication channel. We show that our metrics work well for both non-linear dynamics and time-series feature extraction problems in the field of machine learning. In an experiment aiming to classify vibration signals of normal and damaged bearings, we compare our method with two other methods that reported to have excellent accuracy, based on entropy and statistical feature distribution, respectively. We show that our method achieves higher accuracy with almost the lowest time costs, which was confirmed in experiments with two different datasets containing three main classes of bearings: normal, with inner race faults, and with outer race faults, having different damage origins and recorded in various conditions. In particular, for the dataset supplied by Case Western Reserve University, our method reached an accuracy of 100% at signals of 5000 sample points length, with a total time of 0.4 s required for feature estimation, while the entropy-based method reached an accuracy of 95% with a time of 100 s, and a statistical feature distribution method reached an accuracy of 93% with a total time of 1.9 s. Results show that the developed method is better suited to real-time bearing condition monitoring applications than most of the methods reported to date.

Adaptive filter design for cyclostationary processes associated with sine-wave extraction operation

Cyclostationary signals have been generally used in condition monitoring and fault diagnosis of bearings, for which the adaptive filter is important in obtaining the signal-of-interest cyclostationary process from a disturbed signal. However, due to the nonstationarity of cyclostationary signals, their adaptive filters are usually time-varying and difficult to be implemented. In this study, a new optimum filter is obtained by converting the time-varying optimization function to be time-invariant using a sine-wave extraction (SE) operator, for which the adaptive implementation is designed and analyzed. The time-varying linear minimum mean-square error is converted to be time invariant using the SE operator, and its solution is time invariant but without explicit expression. Subsequently, an adaptive implementation, called the SE least mean square (SE-LMS), is introduced, for which the mean and weighted mean-square-deviation (MSD) are explicitly expressed. The scope of step size is determined to obtain a mean-convergent and mean-square-stable SE-LMS. The steady-state SE-operated excess mean-square-error is analyzed, and its explicit expression is obtained. The SE-LMS algorithm uses only a single cyclic frequency, based on which, the combined SE-LMS algorithm is introduced for a cyclostationary signal with multiple cyclic frequencies. Finally, several simulations are designed to verify the superiority of the proposed estimators over the time-average operator-based estimator.

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.

Smart Sensor-Based Monitoring Technology for Machinery Fault Detection.

Rotary machines commonly use rolling element bearings to support rotation of the shafts. Most machine performance imperfections are related to bearing defects. Thus, reliable bearing condition monitoring systems are critically needed in industries to provide early warning of bearing fault so as to prevent machine performance degradation and reduce maintenance costs. The objective of this paper is to develop a smart monitoring system for real-time bearing fault detection and diagnostics. Firstly, a smart sensor-based data acquisition (DAQ) system is developed for wireless vibration signal collection. Secondly, a modified variational mode decomposition (MVMD) technique is proposed for nonstationary signal analysis and bearing fault detection. The proposed MVMD technique has several processing steps: (1) the signal is decomposed into a series of intrinsic mode functions (IMFs); (2) a correlation kurtosis method is suggested to choose the most representative IMFs and construct the analytical signal; (3) envelope spectrum analysis is performed to identify the representative features and to predict bearing fault. The effectiveness of the developed smart sensor DAQ system and the proposed MVMD technique is examined by systematic experimental tests.

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.

Railway wagon bearing fault diagnosis method based on improved sparrow search algorithm optimizing variational mode decomposition and multi-level convolutional neural network.

Ensuring the safe operation of trains hinges on precise bearing condition monitoring, given the pivotal role bearings play in railway wagons. The status and maintenance of wagon bearings are of paramount concern, necessitating a shift from traditional maintenance approaches reliant on schedules and experience, which often lack real-time precision and efficiency. To address this challenge, our research focuses on enhancing the sparrow search algorithm by incorporating logistic chaos mapping and the levy flight strategy. This enhanced algorithm optimizes variational mode decomposition parameters, utilizing intrinsic mode components' average dispersion entropy as the fitness function. This optimization is integrated with a multi-level convolutional neural network for bearing fault diagnosis. Our findings demonstrate the improved algorithm's enhanced spatial search capabilities and reduced modal aliasing in the frequency components. Experimental validation on public datasets and the group's experimental platform for railway wagons shows that multi-level convolutional neural networks have higher diagnostic accuracy and faster convergence speeds than traditional models such as LeNet-5, AlexNet, and convolutional neural network. Our research introduces a highly accurate and widely applicable methodology for mechanical equipment fault diagnosis, aligning with the requirements of the "smart" era.

Slice-Oriented Signal Probability Distribution Measure for Wind Turbine Generator Bearing Condition Monitoring Under Variable Speed Conditions

Slice-Oriented Signal Probability Distribution Measure for Wind Turbine Generator Bearing Condition Monitoring Under Variable Speed Conditions

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.

A multi-function integrated PVDF transducer for fault detection and speed measurement of cylindrical roller bearings

Monitoring the condition of bearings in rotating machinery is crucial to ensure reliability, safety, and efficiency. Recently, integrated sensors have become more popular than traditional accelerometers in bearing condition monitoring because of their compact design, cost-effectiveness, and improved efficiency in fault detection through a short transmission path. This study presents a new approach to bearing condition monitoring by developing a bearing with an integrated Polyvinylidene Fluoride transducer. This polymer-based transducer, known as PVDF, has a piezoelectric effect which generates electric charge in response to mechanical excitation. The PVDF transducer was selected for its high sensitivity, flexibility, and thin structure. The developed bearing with the integrated PVDF transducer can detect faults under constant and variable speeds, and the damage size can be estimated with an accuracy of 10%. It outperformed a commercial accelerometer in noisy environments with artificial impacts from an air motor, making it a highly reliable option for bearing condition monitoring in noisy industrial settings. The PVDF transducer can also be used as a low-cost speed sensor with an accuracy of 5.2%, offering a versatile solution for machine monitoring. By simultaneously recording rotational speed and vibration, the developed integrated transducer provides a comprehensive view of machine health and performance as a multi-function transducer, enabling more accurate and efficient maintenance scheduling.

Bearing early fault identification and condition monitoring based on dynamic difference index

Bearing condition monitoring is essential for early fault detection and early warning of large equipment, and signal processing techniques are frequently used to analyse nonlinear and nonstationary sequences. Cross-correlation integral is implemented in bearing condition monitoring because it can analyse the non-stationarity of time series in dynamic systems. This paper proposes a dynamic difference index (DDI) due to the difficulty of determining the threshold in the cross-correlation integral and the roughness and operation caused by sequence similarity of 0 or 1. It is a measure of the similarity between the fuzzy autocorrelation integral of a portion of a time series and the cross-correlation between that portion and other portions of the same time series, and it is used to determine the stationarity of the time series. When bearings begin to degrade or develop structural defects, the DDI changes dramatically. The XJTU-SY dataset and IMS Bearing Data were utilised for algorithm validation. First, the algorithm’s efficacy was demonstrated by optimising the effects of various thresholds, distance measures, and time window sizes on DDI and computational efficiency. Secondly, the comparison with common methods and state of the art shows the superiority of the algorithm in detecting early bearing faults, and reveals its appropriate use in practical engineering applications.

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.

A bearing dynamic model based on novel Gaussian-filter waviness characterizing method for vibration response analysis

Bearing waviness is a kind of geometric unevenness on the surface of bearing components, which has vital influence on lifetime, vibration and noise. In order to accurately evaluate the impact of waviness on bearing operating performance and diagnostic features, it is necessary to reveal the mapping relationship between waviness excitation and vibration characteristics. However, current simulated waviness when modeling bearing vibrations is usually simplified by a uniform or non-uniform sinusoidal function, which cannot characterize the real topography of bearing waviness and lead to inaccuracy of diagnostics and prognostics. To address this issue, a novel waviness characterizing method based on Gaussian filter is developed in this study. Based on the proposed waviness characterizing method, a bearing waviness dynamic model is developed and vibration responses under various amplitude and order of waviness are investigated by simulation and experiment. Results show that the established waviness characterizing method can generate waviness curves closer to the actual shape. The bearing waviness dynamic model and the based vibration responses reveals unusual random phenomenon due to different waviness effects. These findings provide theoretical support for accurate identification of waviness on vibration characteristics, which has great significance on condition monitoring and fault diagnosis of bearing.