Bearing Degradation Process Research Articles

Performance degradation assessment of rolling bearings for electrostatic monitoring based on IDDAE and ADPC

Abstract To improve the early degradation detection capability of the electrostatic monitoring system for rolling bearings, a performance degradation evaluation method based on improved deep denoising autoencoder (IDDAE) and adaptive density peak clustering (ADPC) is proposed in this paper. Firstly, the fusion of electrostatic charge signal features with conventional time-domain, frequency-domain and time-frequency-domain features constitutes the characteristic parameter set of the electrostatic monitoring system indicating the status of the bearings. Then, in order to improve the feature extraction ability of DAE, the deep network DDAE is constructed, and L1 regularisation and Dropout mechanism are applied to avoid overfitting in the deep network, so as to achieve non-linear mapping dimensionality reduction of high-dimensional features. Moreover, to eliminate the error caused by manually selecting the clustering centre, the parameters are adaptively determined by entropy value method and comprehensive optimisation search on the basis of DPC, thus avoiding the "chain effect" that occurs in traditional DPC when data are incorrectly aggregated due to incorrect assignment of clustering centres. Consequently, an improved ADPC algorithm is used to establish a model to measure the health status of bearings, calculate the Mahalanobis distance (MD) be-tween the test set and the cluster centre, and quantitatively characterize the degree of performance degradation of rolling bearings. Finally, combining the 3σ principle, a repair method that can satisfy online monitoring and adapt to spurious fluctuations in different situations is established on a sliding window to obtain an improved index IMD that can accurately characterise the rolling bearing degradation process. The experimental results show that the proposed method can identify early bearing degradation earlier and has better monotonicity, robustness and tendency than other performance degradation assessment methods.

Small-sample health indicator construction of rolling bearings with wavelet scattering network: An empirical study from frequency perspective

As a critical issue of diagnostics and health management (PHM), health indicator (HI) construction aims to describe the degradation process of bearings and can provide essential support of domain knowledge for early fault detection and remaining useful life prediction. In recent years, various deep neural networks, with end-to-end modeling capability, have been successfully applied to the HI construction for rolling bearings. In small-sample environment, however, the degradation features would not be extracted well by deep learning techniques, which may raise insufficient tendency and monotonicity characteristics in the obtained HI sequence. To address this concern, this paper proposes a HI construction method based on wavelet scattering network (WSN) and makes an empirical evaluation from frequency perspective. First, degradation features in different frequency bands are extracted from vibration signals by using WSN to expand the feature space with different scales and orientations. Second, the frequency band with the optimal scale and orientation parameters is selected by calculating the dynamic time wrapping (DTW) distance between the feature sequences of each frequency band and the root mean square (RMS) sequence. With the feature subset from the determined frequency band, the HI sequence can be built by means of principal component analysis (PCA). Experimental results on the IEEE PHM Challenge 2012 bearing dataset show that the proposed method can work well with only a small amount of bearing whole-life data in obtaining the HI sequences with high monotonicity and correlation characteristics. More interestingly, the critical frequency band whose information supports decisively the HI construction can be clarified, raising interpretability in a frequency sense and enhancing the credibility of the obtained HI sequence as well.

Lock-in spectrum: a tool for representing long-term evolution of bearing fault in the time–frequency domain using vibration signal

PurposeThis study aims to propose a method for monitoring bearing health in the time–frequency domain, termed the Lock-in spectrum, to track the evolution of bearing faults over time and frequency.Design/methodology/approachThe Lock-in spectrum uses vibration signals captured by vibration sensors and uses a lock-in process to analyze specified frequency bands. It calculates the distribution of signal amplitudes around fault characteristic frequencies over short time intervals.FindingsExperimental results demonstrate that the Lock-in spectrum effectively captures the degradation process of bearings from fault inception to complete failure. It provides time-varying information on fault frequencies and amplitudes, enabling early detection of fault growth, even in the initial stages when fault signals are weak. Compared to the benchmark short-time Fourier transform method, the Lock-in spectrum exhibits superior expressive ability, allowing for higher-resolution, long-term monitoring of bearing condition.Originality/valueThe proposed Lock-in spectrum offers a novel approach to bearing health monitoring by capturing the dynamic evolution of fault frequencies over time. It surpasses traditional methods by providing enhanced frequency resolution and early fault detection capabilities.

Graph Entropy-Based Early Change Detection in Dynamical Bearing Degradation Process

Graph Entropy-Based Early Change Detection in Dynamical Bearing Degradation Process

Quantile regression network-based cross-domain prediction model for rolling bearing remaining useful life

Transfer learning improves remaining useful life (RUL) prediction accuracy across domains by aligning data distributions for different operating conditions. However, the uncertainty caused by the complex working conditions and stochastic degradation process of rolling bearings is not considered, leading to poor credibility of the prediction results and affecting the development of the predictive maintenance strategy. In response to this problem, the paper proposes a deep subdomain adaptation time-quantile regression network (DSATQRN) model to compress rolling bearing uncertainty intervals of RUL across prediction. The model uses deep subdomain adaptation to align the feature distribution and introduces temporal correlation to construct a temporal quantile regression network to obtain interval prediction results. Finally, the uncertainty in the prediction results is quantified by kernel density estimation. The model was experimented with using the open XJTU-SY Bearing Datasets and IEEE PHM 2012 Challenge Datasets. It verifies the performance of the proposed model from three aspects: point prediction accuracy, interval prediction suitability, and probabilistic prediction overall performance. The experimental results show that the average interval coverage of the proposed model on the two datasets is 91.25% and 90.43%, and the average prediction interval width is 16.65% and 13.69%, respectively. It is demonstrated that the cross-domain prediction results of the DSATQRN possess high prediction accuracy and narrow prediction interval, and the model has good robustness and reliability.

A novel continuous delay hidden layer deep belief network and its application in life prediction of rolling bearings

The traditional rolling bearing trend prediction method is difficult to predict the bearing degradation process comprehensively and scientifically, and the feature extraction is highly dependent on the manual experience method, and the prediction ability of the bearing measurement data with strong dynamic characteristics and nonlinear is very limited. Therefore, how to build a high-precision life prediction model has become an urgent problem and challenge. In this paper, a continuous delay hidden layer deep belief network (CDHLDBN), kernel principal component analysis (KPCA)-CDHLDBN, is proposed based on the KPCA preprocessing method. Firstly, a continuous deep belief network (CDHLDBN) with a delay layer is proposed, which makes the output of the hidden layer more fully consider the influence of the previous moment on the output of this moment, so as to avoid the defect of DBN model which is difficult to process the data with strong dynamic time series. Then, all visible layers of a continuous DBN with a delay layer are de-averaged, and then the simulated bearing sequence data is trained and predicted using the KPCA-CDHLDBN life prediction model. The results show that KPCA-CDHLDBN model can predict nonlinear dynamic time series data better than other deep learning models. Finally, it is applied to the performance degradation life prediction of rolling bearings. The performance of the proposed method was verified by the bearing accelerated life vibration data provided by PRONOSTIA experimental platform, and the performance of the proposed method was compared with that of the traditional DBN and its improved prediction model. The results show that KPCA-CDHLDBN has better prediction accuracy and faster prediction speed.

A new unsupervised health index estimation method for bearings early fault detection based on Gaussian mixture model

Bearings are indispensable components of machinery, playing a critical role in effective health monitoring. This monitoring is vital in detecting equipment incipient failure and reducing maintenance costs. The bearing degradation is a complex nonlinear process that defies straightforward description using physical models or predefined degradation patterns. Bearings exhibit inherent fault frequencies, and the early faults of bearings can alter their monitoring data distribution. Gaussian Mixture Modeling (GMM) can effectively visualize these changes in data distribution. This research focuses on the development of a new unsupervised method for constructing the bearing Health Index (HI) using GMM to estimate vibration signal distributions. Firstly, we introduce a new unsupervised HI construction method, named GMM-HI, designed to provide insight into the bearing degradation process. Secondly, the Wasserstein distance is adopted as the bearing HI, measuring the distance between initial and current health data. Thirdly, isotonic regression is utilized to address spurious fluctuations in bearing monitoring signals. Through extensive experimentation on three bearing datasets, our results demonstrate that the newly introduced GMM-HI allows for accurate detection of bearing early failures. It effectively categorizes the health stages of bearings and provides a unified approach for establishing bearing failure thresholds in estimating their remaining useful life. In comparison to other well-known HI methods, GMM-HI effectively and accurately characterizes the health index of bearings.

Remaining useful life prediction of rolling bearings based on TCN-MSA

As a pivotal element within the drive system of mechanical equipment, the remaining useful life (RUL) of rolling bearings not only dictates the lifespan of the equipment’s drive system but also the overall machine. An inaccurate prediction of the RUL of rolling bearings could hinder the formulation of maintenance strategies and lead to a chain of failures stemming from bearing malfunction, culminating in potentially catastrophic accidents. This paper designs a novel temporal convolutional network-multi-head self-attention (TCN-MSA) model for predicting the RUL of rolling bearings. This model considers the intricate non-linearity and complexity of mechanical equipment systems. It captures long-term dependencies using the causally inflated convolutional structure within the temporal convolutional network (TCN) and simultaneously extracts features from the frequency domain signal. Subsequently, by employing the multi-head self-attention (MSA) mechanism, the model discerns the significance of different features throughout the degradation process of rolling bearings by analyzing global information. The final prediction for rolling bearings’ RUL has been successfully attained. To underline the excellence of the method presented in this paper, a comparative analysis was performed with existing methods, such as convolutional neural network, gate recurrent unit, and TCN. The results highlight that the model designed in this paper surpasses other existing methods in predicting the RUL of rolling bearings, demonstrating superior prediction accuracy and robust generalization capability.

The transient concept of bearings: a novel strategy for RUL prediction

Abstract Bearings serve as integral components in mechanical devices, providing stability during mechanical transmission and reducing friction coefficients. Hence, the precise prediction of bearing remaining useful life (RUL) is paramount for the health monitoring of mechanical systems. However, traditional techniques which utilize linear degradation processes for constructing health index models often fail to adequately portray the complex relationship between degradation and time. To rectify this, we introduce The Transient Concept of Bearings and determine the degradation rate predicated on this novel concept. We construct a degradation rate model for bearings using a K-means-transformer network and leverage transfer learning methodologies to predict the RUL of bearings. Validation of the proposed concepts and demonstration of their accuracy are achieved using the PHM2012 challenge dataset, even amidst incomplete data scenarios. When compared to existing RUL prediction models, our approach not only significantly improves prediction accuracy but also sheds valuable insights into the bearing degradation process.

Context-aware machine learning for estimating the remaining useful life of bearings under varying speed operating conditions

Remaining useful life estimation is a crucial and complicated task in predictive maintenance in order to reduce downtime and avoid catastrophic breakdowns in industrial plants. Thanks to the recent advances in our machine learning era, deep learning models can effectively deal with modeling complex phenomena such as the bearing degradation process, specifically under varying operating conditions. However, obtaining large labeled datasets for training the data-dependent deep learning models is challenging and expensive. To overcome this limitation, a phenomenological model has been used in this study as an effective approach to creating synthetic run-to-failure datasets under varying operating conditions. The suggested methodology is able to adjust synthetic run-to-failure datasets to the different periodic speed profiles, including the speed ranges that pass the resonance frequency of the structure. A Context-aware Domain Adversarial Neural Network is proposed to remove the domain shift between the simulated signals and the real ones as well as enable the deep learning model to understand the varying speed operating conditions and the sequential order of the measurements. The simulated signals are used as the source domain and a limited number of the real signals are used as the unlabeled samples for the domain adaptation task. Context awareness is introduced to the network by integrating contextual information into the architecture of the Domain Adversarial Neural Network, leading to an improvement in the model performance and its generalization ability. A dataset captured in a bearing test rig is adopted to verify the proposed method. Results show that context awareness can result in better performance and also more robust predictions against major speed changes in varying speed scenarios compared to the non-context-aware models.

Bearing performance degradation assessment using adversarial fusion convolutional autoencoder based on multi-source information

Bearing operation states will directly determine the performance of the equipment; thus, monitoring operation status and degradation indicators is the key to ensuring continuous and healthy operation of the equipment. However, most of the research uses single-source information data, which makes it difficult to model when dealing with multi-source information, complex data distribution, and noise. In this paper, a bearing performance degradation assessment method based on multi-source information is proposed to comprehensively utilize the data signals of different structures, spaces, types, and sources. First, the adversarial fusion convolutional autoencoder is constructed for obtaining the degradation index of the bearing, while the adversarial learning strategy is applied to achieve the effect of enhancing the robustness and sensitivity of the degradation indicators extracted by the network. Then the degradation index is input into the support vector data description to determine the fault anomalies of the degradation index adaptively and the fuzzy c-means algorithms to obtain the final rolling bearing performance degradation evaluation results. Through the verification results of two experiment datasets, it is found that the proposed model can achieve accurate evaluation and quantitative analysis of the performance degradation process of bearings. As a result, the entire network ensures the reconstruction accuracy of normal samples while simultaneously stretching the reconstruction error of abnormal samples to achieve accurate monitoring of degradation onset.

A sliding sequence importance resample filtering method for rolling bearings remaining useful life prediction based on two Wiener-process models

The remaining useful life (RUL) prediction of rolling bearings is an important part of prognostic and health management of mechanical systems. The model based on Wiener process can describe the time variability in the degradation process of bearings. However, in practical engineering, the degradation trends of bearings are often inconsistent, and it is difficult to fit the actual degradation trends of bearings with a single Wiener process model-based filtering method. Therefore, to improve the generalization ability, this paper uses linear model and exponential model based on Wiener process to predict bearing RUL. A sliding sequence importance resample filtering algorithm is proposed to track the degradation state of the bearing and reduce the prediction error by combining the two degradation models. Last, the superiority and effectiveness of the proposed method are illustrated by comparing with other commonly used RUL prediction methods on the basis of PRONOSTIA dataset.

Feature extraction of multi-sensors for early bearing fault diagnosis using deep learning based on minimum unscented kalman filter

Bearing fault diagnosis is vital for ensuring reliability and safety of high-speed trains and wind turbines. Therefore, a minimum unscented Kalman filter-aided deep belief network is proposed to extract invariant features from vibration signals collected by multiple sensors. This particularly crucial due to the dynamic nature of environmental noise and internal bearing degradation, which pose challenges to accurate diagnosis. Firstly, the Gramian angular summation field is employed to transform the multi-sensor signals into 2-D feature maps. This transformation retains the absolute temporal relation within the time-series signals, mitigating feature distortion and enhancing noise elimination for early detection. Secondly, a deep belief network is utilized to construct a robust deep learning framework capable of analysing the translated 2-D feature maps for effective diagnosis. In addition, a minimum unscented transform technique and an adaptive scaling process for noise are integrated into the diagnostic model. These components exhibit exceptional dynamic tracking capabilities, allowing for adjustment of key parameters in response to the prolonged and evolving bearing degradation process. The proposed methodology was rigorously evaluated through a comprehensive analysis involving nine distinct methods, utilizing two diverse bearing datasets. The results obtained underscore the superior attributes. Notably, the proposed diagnostic scheme achieved accuracy rates exceeding 98% and 99% for the two datasets, respectively. This achievement underscores the establishment of an intelligent diagnosis model characterized by high precision and exceptional generalisation capabilities for bearings within rotating machinery. Consequently, this work lays a robust foundation for future research endeavours, particularly in the realm of transfer learning.

A piecewise method for bearing remaining useful life estimation using temporal convolutional networks

Most existing research on the prediction of bearing remaining useful life (RUL) focus on building a whole-lifecycle degradation model, which may affect prediction performance due to different states during the bearing degradation process. In addition, traditional network models such as CNN and RNN have limitations in directly dealing with time series problems. This paper proposes an adaptive degradation stage division strategy and a temporal convolutional network (TCN)-based RUL piecewise estimation method, called ADSD-TCNPE. This method mainly includes three steps. (1) Extract features from different domains and select the features that are highly correlated with bearing degradation. (2) Adaptively divide the whole lifecycle of bearing into different degradation stages. (3) Establish a TCN-based piecewise degradation model for different degradation stages to accurately predict bearing RUL. Finally, experimental verification and result analysis demonstrate the effectiveness and superiority of the proposed method.

A Reinforced Noise Resistant Correlation Method for Bearing Condition Monitoring

Condition monitoring plays a significant role in guaranteeing the reliability and safety of rotating machinery, which aims to detect an incipient fault and assess the degradation tendency. The construction of a health index (HI) is a crucial step to realize above tasks. At present, kurtosis, crest factor, and so on have been recognized as popular HIs to depict the operating condition. However, shortcomings of these classical HIs still exist: 1) classical HIs are prone to be affected by strong white Gaussian noise; 2) classical HIs are not sensitive to incipient faults. To deal with these two shortcomings, a reinforced noise resistant correlation method is proposed in this paper. Firstly, a new signal is constructed using the steps of segmenting and averaging to suppress the interference of noise. Then, a novel correlation function is used to get the hidden period. The proposed HI is constructed based on the discrete version of this correlation function to increase the sensitivity of incipient faults. Subsequently, theoretical values of the proposed HI under healthy states are investigated. The effectiveness of the method is demonstrated using simulated degradation processes and two accelerated degradation datasets of rolling element bearings. Through comparisons with other classical HIs, the proposed HI can simultaneously suppress the interference of strong noise and detect incipient faults. The comparison results identify the effectiveness of the proposed method in monitoring the condition of rotating machinery. Note to Practitioners—This work aims to provide a novel health index construction method for rotating machinery condition monitoring. The key issues involved in this problem include 1) how to capture the complex relationship between the measured vibration signals and the underlying health condition of rotating machinery; 2) how to suppress the interference of environmental noise. The novelty of this work is that it develops a method for condition monitoring considering the measured signals with strong white Gaussian noise. It properly captures the complex relationship between measured signals and the underlying health condition. There are four main steps to implement this approach: 1) collecting the vibration signals from rotating machinery; 2) constructing new periodic signal to suppress the interference of strong background noise; 3) constructing novel correlation functions to establish the relationship between the vibration signals and the underlying health condition of the rotating machine; 4) modeling the HI. A simulation and two real cases of the bearing degradation process are used to show that the proposed HI can simultaneously suppress the interference of strong noise and detect the incipient faults compared with some classical HI construction methods. In the future, the problem of how to address the measured signals contaminated by non-Gaussian noise and the machinery containing compound faults should be solved to extend this method in a complex system.

Deep transfer learning-based hierarchical adaptive remaining useful life prediction of bearings considering the correlation of multistage degradation

The multi-stage degradation process of bearings significantly affects the predicted performance of rotating machinery and equipment in long-term operation. However, the inherent correlations between degradation stages, and domain-invariant degradation features of multistage that affect the remaining useful life (RUL) are mostly ignored, leading to RUL prognostics deterioration under cross-working conditions. Therefore, a novel deep transfer learning-based hierarchical adaptive RUL prediction approach is applied to overcome this problem. Firstly, a novel multistage degradation (MD) division method is proposed with a combination of maximum mean discrepancy and statistical process analysis to accurately obtain the varied health indicators (HIs) with MD without setting any threshold. Then, the obtained MD features are fed into the residual network to automatically recognize the HIs and MD in response to multi-variability trend changes. Finally, a novel hierarchical adaptive RUL model based on a deep adaptive-transformer is proposed to transfer the learned domain-invariant degradation features of multistage from one to various working conditions. Meanwhile, hierarchical adaptive tuning (HAT) reduces the RUL model loss to guarantee a more accurate prediction of each degradation stage. To validate the proposed approach, extensive experiments were conducted in seven cross-domain cases on the XJTU-SY dataset to predict RUL. Comparison results indicate that the proposed approach is more accurate than other conventional methods because it considers the RUL of bearing at each stage.

Bearing performance degradation assessment and remaining useful life prediction based on data-driven and physical model

Intelligent health maintenance of bearings usually consists of two stages: constructing effective health assessment indicators and accurate remaining useful life prediction models. However, many prediction models are available only when many constraints are met, and the health assessment indicators may not be able to accurately track the performance degradation process of bearings. This study proposes a bearing performance degradation assessment and remaining life prediction method. First, the health evaluation index family (referred to as the generalized high-order moment coefficient) was constructed based on the generalized power mean and high-order origin moments for health assessment. Subsequently, an improved Paris–Erdogan model is proposed, which uses the optimal health evaluation index as input to predict the remaining life of the bearing after the initial failure. The experimental results show that the proposed method has a higher performance degradation tracking accuracy and a smaller prediction error than the combination of traditional statistical indicators and prediction models.

Remaining useful life prediction of bearings based on self-attention mechanism, multi-scale dilated causal convolution, and temporal convolution network

Effective remaining useful life (RUL) prediction of bearings is essential for the predictive maintenance of rotating machinery. However, the effectiveness of many existing RUL prediction methods depends on expert experience and signal processing algorithms, which limiting the application of these methods in real-life scenarios. This study proposes a novel end-to-end deep learning framework consisting of a multi-scale attention-based dilated causal convolutional (MADCC) module and a multi-layer temporal convolutional network (MTCN) to predict the RUL of bearings using raw vibration data. First, the MADCC module extracts multi-scale temporal features of the bearing degradation process (BDP) and provides fused feature vectors (FFVs) containing comprehensive BDP information for the MTCN module. Subsequently, the MTCN module mines deep temporal dependencies hidden in the FFV to predict the RUL of bearings. Ablation experiments are conducted to analyze the contribution of the framework’s components. Three evaluation metrics (root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R 2)) are used to verify the effectiveness of the proposed framework and other state-of-the-art methods on two public bearing datasets. The experimental results show that the proposed framework achieves the lowest RMSE and MAE and the highest R 2, demonstrating excellent performance and potential for RUL prediction of bearings.

An integrated framework via key-spectrum entropy and statistical properties for bearing dynamic health monitoring and performance degradation assessment

Dynamic health monitoring (DHM) and performance degradation assessment (PDA) is critical for mechanical bearings throughout their long in-service life. For this issue, it is currently rare to find a framework with interpretable and automatic approaches developed from pure signal processing techniques and statistical theories. Therefore, an integrated framework via key-spectrum entropy and statistical properties for bearing DHM and PDA is developed in this paper, which integrates the proposed key spectrum, key-spectrum entropy, joint statistical alarm and fault identification strategy, health phase segmentation strategy, and three-dimensional (3D) key spectrums. First, a Kurtosis-Energy metric is defined to extract the key spectrum, which is reconstructed by two wavelet-decomposed sub-bands where the interference components are suppressed. A new health index (HI) of key-spectrum entropy is then defined to quantify the bearing degradation process. Second, a joint statistical alarm and fault identification strategy via updated HIs and key spectrum is proposed to form a DHM methodology for implementing bearing dynamic fault detection and recognition. Third, a health phase segmentation strategy and 3D key spectrums are developed to form a PDA methodology for implementing bearing health phase assessment and degradation pattern analysis. Comprehensive evaluations and comparisons on eighteen sets of bearing degradation vibration signals demonstrate the validity of the proposed framework, as well as its great practical application prospects.

Remaining Useful Life Estimation of Fan Slewing Bearings in Nonlinear Wiener Process with Random Covariate Effect

Since the degradation process of fan slewing bearings is easily influenced by the external environment, it is difficult to estimate its remaining useful life (RUL) accurately. A nonlinear Wiener degradation model considering the influence of random covariate is established for the prediction of the RUL of fan slewing bearings in this study. Firstly, considering the nonlinear and nonmonotonic properties of the operation process of fan slewing bearings, the degradation model of the nonlinear Wiener process of fan slewing bearings is established. Secondly, the combination of random covariate models and the nonlinear Wiener degradation process is researched. The stress effect which is used as a random covariate is introduced into the nonlinear Wiener degradation model in the form of the additive hazard model. Moreover, the closed expression for the RUL probability density function(PDF) is derived for the random variation of drift coefficients, the individual differences and the random variation of covariates. Thirdly, the maximum likelihood estimation algorithm is used to estimate the RUL parameters depending on the historical degradation data. Finally, the vibration data of fan slewing bearings monitored by sensors are used to verify the effectiveness of the proposed method. The results show that the proposed method can be used to improve the fitting degree of the model and the accuracy of RUL estimation.