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Highlights • A subspace-based minimum volume ellipsoid model is proposed for performance degradation assessment of aero-engine bearings. • The multimodal homologous features are modelled by subspaces. • The proposed model is validated by run-to-failure tests of aero-engine bearings. • The proposed model outperforms traditional method. Abstract Performance degradation assessment is an important concept in prognostics and health management (PHM) of complex engineering systems. In this study, a novel method utilizing subspace-based minimum volume ellipsoid (SMVE) for bearing performance degradation assessment is proposed. The statistical features extracted from vibration signals are seen as multimodal homologous features in time, frequency and wavelet modalities, thus it needs to consider the differences in variance for the homologous features as well as covariance among them. The subspaces are used to model the homologous features of different frequency bands since they can capture the dynamic information. Based on subspaces, the proposed SMVE model covering most or all of the normal data by a unique ellipsoid with minimum volume considers variance of each dimension adaptively. A performance degradation index is designed based on the SMVE. The proposed method is applied on the vibration signals acquired in run-to-failure tests of aero-engine bearings and common bearings. By comparison with the commonly used time-domain features and traditional minimum volume ellipsoid method, the results demonstrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

The operation conditions are always non-stationary in the operation of rotating machinery. It is the key to realize the fault diagnosis of rotating machinery under different operation conditions by extracting the features which are irrelevant to the operation conditions and contain fault information. Nuisance attribute projection (NAP) is compensation technique to eliminate the influence of interference information in the feature space which has been used in speaker recognition and face recognition. In this paper, the rolling bearing vibration signals under different operation conditions are used to verify the nuisance removing ability of (NAP) in the feature space. Above all, after NAP are applied to the simulation and measured signals under different rotating speeds and loads, the comparisons between the original features and NAP features have shown that NAP can effectively eliminate the effect of nuisance attributes through projection. Moreover, it is verified that the information of fault pattern and fault degree are retained in the features after projection by analyzing the measured signals of different fault pattern and simulation signals of different fault degree under various rotating speeds, respectively. Furthermore, it is confirmed that the NAP can get rid of the nuisance attributes by analyzing twelve bearing tests of whole life under different operation conditions and channels. On the other hand, a feature selection method based on ranking mutual information (RMI) is used in this paper to select the features of more monotonous in bearing performance degradation assessment. The application sequences of RMI and NAP are studied for their influence on the bearing performance degradation index in the last part since the different sequence may affect the assessment results. The comparison results have shown that NAP should be used before feature selection in rotating machinery fault diagnosis. [ABSTRACT FROM AUTHOR]

Highlights • A novel HI based on EEMD, GMM and JRD is proposed for both bearing PDA and RUL estimation. • The developed HI contains minimal noise and increases monotonically towards the end life of the bearings. • SVR combined with multi-step ahead prediction strategy is used to forecast the suggested HI and predict the bearing RUL. Abstract This paper proposes an integrated bearing prognostics approach that is divided into two components: The first component develops a new health indicator (HI) for performance degradation assessment (PDA) of bearings. Many existing HIs suffer from certain shortcomings such as insensitiveness to incipient defects and a highly fluctuating behavior with the increase in degradation severity. To overcome these disadvantages, a HI based on Gaussian mixture models (GMM) and Jensen-Rényi Divergence (JRD) is suggested, which retains its monotonicity as the bearing condition deteriorates. Firstly, the acquired vibration signals are decomposed by ensemble empirical mode decomposition (EEMD) to extract the fault features i.e. singular values and energy moment entropies of the intrinsic mode functions (IMFs). Secondly, the feature vectors under healthy conditions are used to train the GMM. Thirdly, the test feature vectors are supplied to the trained GMM to calculate the components’ posterior probabilities. Fourthly, JRD measure is used to discriminate the defective posterior probability distributions from the healthy ones. Finally, the JRD is converted into confidence value (CV) to realize bearing PDA. The second component utilizes the CV values to train the support vector regression (SVR) model. The particle swarm optimization (PSO) technique is implemented to attain the optimal values of SVR hyperparameters. The optimal SVR model is then used to forecast the CV upto predefined failure threshold and evaluate the bearing remaining useful life (RUL). The experimental results verify that the proposed CV performs better than the GMM-negative log likelihood probability (NLLP) for RUL estimation of bearings. [ABSTRACT FROM AUTHOR]

Performance degradation assessment is crucial to realize equipment’s near-zero downtime and maximum productivity. In this paper, a new method for performance degradation assessment of rolling element bearings is proposed based on hierarchical entropy (HE) and general distance. First, considering the nonlinear dynamic characteristics of bearing vibration signals, the HE method is utilized to extract feature vectors, which can obtain more bearing state information hidden in the vibration signals than sample entropy (SampEn) and multi-scale entropy (MSE). Then, the general distance between the feature vectors of the normal data and those of the tested data is designed as a degradation indicator by combining Euclidean distance and cosine angle distance. The experimental results indicate that this indicator can detect the incipient defects well and can effectively reflect the whole degradation process of rolling element bearings. Moreover, the designed indicator has some advantages over kurtosis and root mean square (RMS) values. [ABSTRACT FROM AUTHOR]

Bearing-supported shafts are widely used in various machines. Due to harsh working environments, bearing performance degrades over time. To prevent unexpected bearing failures and accidents, bearing performance degradation assessment becomes an emerging topic in recent years. Bearing performance degradation assessment aims to evaluate the current health condition of a bearing through a bearing health indicator. In the past years, many signal processing and data mining based methods were proposed to construct bearing health indicators. However, the upper and lower bounds of these bearing health indicators were not theoretically calculated and they strongly depended on historical bearing data including normal and failure data. Besides, most health indicators are dimensional, which connotes that these health indicators are prone to be affected by varying operating conditions, such as varying speeds and loads. In this paper, based on the principle of squared envelope analysis, we focus on theoretical investigation of bearing performance degradation assessment in the case of additive Gaussian noises, including distribution establishment of squared envelope, construction of a generalized dimensionless bearing health indicator, and mathematical calculation of the upper and lower bounds of the generalized dimensionless bearing health indicator. Then, analyses of simulated and real bearing run to failure data are used as two case studies to illustrate how the generalized dimensionless health indicator works and demonstrate its effectiveness in bearing performance degradation assessment. Results show that squared envelope follows a noncentral chi-square distribution and the upper and lower bounds of the generalized dimensionless health indicator can be mathematically established. Moreover, the generalized dimensionless health indicator is sensitive to an incipient bearing defect in the process of bearing performance degradation. [ABSTRACT FROM AUTHOR]

Rolling element bearings are an important unit in the rotating machines, and their performance degradation assessment is the basis of condition-based maintenance. Targeting the non-linear dynamic characteristics of faulty signals of rolling element bearings, a bearing performance degradation assessment approach based on improved fuzzy entropy (FuzzyEn) is proposed in this paper. FuzzyEn has less dependence on data length and achieves more freedom of parameter selection and more robustness to noise. However, it neglects the global trend of the signal when calculating similarity degree of two vectors, and thus cannot reflect the running state of the rolling element bearings accurately. Based on this consideration, the algorithm of FuzzyEn is improved in this paper and the improved FuzzyEn is utilized as an indicator for bearing performance degradation evaluation. The vibration data from run-to-failure test of rolling element bearings are used to validate the proposed method. The experimental results demonstrate that, compared with the traditional kurtosis and root mean square, the proposed method can detect the incipient fault in advance and can reflect the whole performance degradation process more clearly. [ABSTRACT FROM AUTHOR]

Bearing is the most critical component in rotating machinery since it is more susceptible to failure. The monitoring of degradation in bearings becomes of great concern for averting the sudden machinery breakdown. In this study, a novel method for bearing performance degradation assessment (PDA) based on an amalgamation of empirical mode decomposition (EMD) and k-medoids clustering is encouraged. The fault features are extracted from the bearing signals using the EMD process. The extracted features are then subjected to k-medoids based clustering for obtaining the normal state and failure state cluster centres. A confidence value (CV) curve based on dissimilarity of the test data object to the normal state is obtained and employed as the degradation indicator for assessing the health of bearings. The proposed outlook is applied on the vibration signals collected in run-to-failure tests of bearings to assess its effectiveness in bearing PDA. To validate the superiority of the suggested approach, it is compared with commonly used time-domain features RMS and kurtosis, well-known fault diagnosis method envelope analysis (EA) and existing PDA classifiers i.e. self-organizing maps (SOM) and Fuzzy c-means (FCM). The results demonstrate that the recommended method outperforms the time-domain features, SOM and FCM based PDA in detecting the early stage degradation more precisely. Moreover, EA can be used as an accompanying method to confirm the early stage defect detected by the proposed bearing PDA approach. The study shows the potential application of k-medoids clustering as an effective tool for PDA of bearings. [ABSTRACT FROM AUTHOR]

To effectively assess different fault locations and different degrees of performance degradation of a rolling bearing with a unified assessment index, a novel state assessment method based on the relative compensation distance of multiple-domain features and locally linear embedding is proposed. First, for a single-sample signal, time-domain and frequency-domain indexes can be calculated for the original vibration signal and each sensitive intrinsic mode function obtained by improved ensemble empirical mode decomposition, and the singular values of the sensitive intrinsic mode function matrix can be extracted by singular value decomposition to construct a high-dimensional hybrid-domain feature vector. Second, a feature matrix can be constructed by arranging each feature vector of multiple samples, the dimensions of each row vector of the feature matrix can be reduced by the locally linear embedding algorithm, and the compensation distance of each fault state of the rolling bearing can be calculated using the support vector machine. Finally, the relative distance between different fault locations and different degrees of performance degradation and the normal-state optimal classification surface can be compensated, and on the basis of the proposed relative compensation distance, the assessment model can be constructed and an assessment curve drawn. Experimental results show that the proposed method can effectively assess different fault locations and different degrees of performance degradation of the rolling bearing under certain conditions. [ABSTRACT FROM AUTHOR]

Bearing performance degradation assessment is a key step of condition-based maintenance. In this paper, a novel indicator of bearing performance degradation assessment is presented based on the mathematical morphology (MM) theory for higher efficiency. Because the traditional mathematical morphology particle (MMP) is unable to exactly describe the bearing's performance degradation, we extended its definition to the general space to address the limited ability of the former definition. On the basis of the innovative definition, we used the morphology erosion operation instead of the morphology open operation to calculate the general mathematical morphology particle (GMMP) of the bearing's whole life data. The analysis of the simulation and practical application demonstrated that the proposed index is feasible and effective to indicate the performance degradation of the bearing. [ABSTRACT FROM AUTHOR]

Rolling element bearing is not only the most important failure unit in rotary machinery, but it is also the most common, and its performance degradation assessment has been proposed to realize equipment’s near-zero downtime and maximum productivity. Therefore, exploring effective indices is crucial. Rolling element bearing’s vibration signals often contain complex, non-stationary and nonlinear characteristics. In this work, the changes of complexity, which are measured by two nonlinear methods: correlation dimension and approximate entropy, in the performance degradation process of rolling element bearing based on simulation and bearing accelerated life test data have been studied. Results show that the complexity decreases with the development of obvious defects, and that correlation dimension and approximate entropy have different sensitivities to different performance degradation stages, with the different ability of reflecting the first degradation stage being explained by their different computation processes. [ABSTRACT FROM AUTHOR]

Rolling element bearings are vital components in rotating machines, and it is important to diagnose bearing faults to avoid serious accidents in equipment. In this paper, singular spectrum analysis (SSA) is utilized to extract the bearing fault features. SSA is a non-parametric technique of time series analysis which decomposes the acquired vibration signals into an additive set of time series. Based on the selected singular features from SSA, a continuous hidden Markov model (CHMM) is introduced to diagnose the bearing fault. The detailed description and identification results of applying the proposed method to rolling element bearing fault diagnosis are shown in experiment 1. In experiment 2, a rolling element bearing accelerated life test is performed to simulate the performance variation of the bearing. The result demonstrates that the singular features and CHMM can reflect the performance degradation of the bearing from health to failure. A conclusion can be made that SSA and CHMM are feasible and effective in bearing fault diagnosis and performance assessment. [ABSTRACT FROM PUBLISHER]

The bearing is the key component in a rotating machine. It is important to assess the performance degradation of bearings for realizing proactive maintenance and near-zero downtime. In this paper, a methodology based on the zero crossing characteristic features and a coupled hidden Markov model is introduced for estimating bearing performance degradation. Zero crossing features are time domain representations of the vibration signature in the spectrum domain. They discover the change of bearing performance. When zero crossing features are extracted, a coupled hidden Markov model is employed to assess the performance degradation quantitatively. Results from a bearing accelerated life experiment validate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

Performance degradation assessment based on condition monitoring plays an important role in ensuring reliable operation of equipment, reducing production downtime and saving maintenance costs, yet performance degradation has strong fuzziness, and the dynamic information is random and fuzzy, making it a challenge how to assess the fuzzy bearing performance degradation. This study proposes a monotonic degradation assessment index of rolling bearings using fuzzy support vector data description (FSVDD) and running time. FSVDD constructs the fuzzy-monitoring coefficient ε̄ which is sensitive to the initial defect and stably increases as faults develop. Moreover, the parameter ε̄ describes the accelerating relationships between the damage development and running time. However, the index ε̄ with an oscillating trend disagrees with the irreversible damage development. The running time is introduced to form a monotonic index, namely damage severity index (DSI). DSI inherits all advantages of ε̄ and overcomes its disadvantage. A run-to-failure test is carried out to validate the performance of the proposed method. The results show that DSI reflects the growth of the damages with running time perfectly. [ABSTRACT FROM AUTHOR]

The sensitivity of various physical features that are characteristics of bearing performance may vary significantly under different working conditions. Thus, it is critical to extract the most effective information from the original physical features generated from vibration signals for bearing defect classification and performance degradation assessment. This paper proposes a local and nonlocal preserving projection (LNPP)-based feature extraction algorithm, which is different from principal component analysis that aims to discover the global structure of Euclidean space and regular manifold learning algorithms that attempt to preserve local structure in data. LNPP is capable of discovering both local and nonlocal structures of data manifold. This may enable LNPP to find the meaningful low-dimensional information hidden in the high-dimensional feature set and then to serve as a preprocessor for defect classification. Furthermore, an LNPP-based quantification index is proposed for the assessment of bearing performance degradation. An LNPP-based contribution plot for feature selection is developed to improve the degradation detection sensitiveness and to reduce false alarms. A dynamic LNPP for bearing performance assessment is further developed to consider inherent autocorrelation existing in vibration data. Detailed results are very promising and are reported in this paper. This paper will provide guidance for the applications of manifold learning algorithms (e.g., LNPP) in machine fault diagnosis and performance prognostics. [ABSTRACT FROM PUBLISHER]

Performance degradation assessment has emerged to realize equipment’s near-zero downtime and maximum productivity. Exploring effective indices is crucial for it. In this study, taking rolling element bearing as a research object, cyclic energy indicator is proposed for its performance degradation assessment. The proposed cyclic energy indicator is based on signals’ cyclic coherence analysis and its variation with the damage extent of the rolling element bearing is verified through simulation and seeded fault experimental data. Then an accelerated life test of the rolling element bearing is performed to collect vibration data over the whole life time (normal-fault-failure). Results of both simulation and experiments show that the cyclic energy indicator is an effective index for the degradation assessment of rolling element bearings. [ABSTRACT FROM AUTHOR]

Abstract: Bearing performance degradation assessment is crucial to realize condition-based maintenance. In this paper, a new method for it is proposed based on lifting wavelet packet decomposition and fuzzy c-means. Feature vectors are composed of the node energies of lifting wavelet packet decomposition. Normal and final failure data are used as training samples to build assessment model utilizing fuzzy c-means, and the subjection of tested data to normal state is defined as the degradation indicator, which has intuitionistics explanation related to degradation degree. Results of its application to accelerated bearing life test show that this indicator can reflect effectively performance degradation of bearing. And after discussing the influence of outliers in training set, a robust strategy is proposed. [Copyright &y& Elsevier]

Abstract: Bearing performance degradation assessment is one of the most important techniques in proactive maintenance aiming to realize equipment''s near-zero downtime and maximum productivity. In this paper, we propose a new robust method for it based on improved wavelet packet decomposition (IWPD) and support vector data description (SVDD). A health index is designed based on general distance. Node energies of IWPD are used to compose feature vectors. Based on feature vectors extracted from normal signals, a SVDD model fitting a tight hypersphere around them is trained, the general distance of test data to this hypersphere is used as the health index. Research results of its application in a bearing accelerated life test show that this health index can reflect effectively bearing performance degradation comparing with many other parameters. [Copyright &y& Elsevier]