Search

Evaluating the degradation of hull and ship performance and exploring their degradation pathways is crucial for developing scientific and reasonable ship maintenance plans. This paper proposes a two-stage optimization (TSO) algorithm that combines the Genetic Algorithm (GA) and Long Short-Term Memory (LSTM) network, capable of simultaneously optimizing input features and model parameters to enhance the accuracy and generalization ability of speed prediction models. Additionally, a performance degradation assessment method based on speed loss is provided, aimed at evaluating the degradation of hull and propeller performance, as well as extracting the performance degradation paths. The results indicated that the proposed TSO-LSTM-GA algorithm significantly outperformed existing baseline models. Furthermore, the provided performance degradation assessment method demonstrated certain effectiveness on the target ship data, with a measured degradation rate of 0.00344 kn/d and a performance degradation of 9.569% over 478 days, corresponding to an annual speed loss of 1.257 kn. [ABSTRACT FROM AUTHOR]

To more accurately predict remaining useful life (RUL) and quantitatively evaluate the uncertainty of the predicted results, a performance degradation assessment framework based on semi-analytical solution of self-similar stable distribution process is proposed. The established performance degradation model based on adaptive fractional Lévy stable motion (AFLSM) is more flexible in revealing the long-range dependence, non-Gaussian, and heavy-tailed distribution properties of the incremental behavior. The corresponding stable distribution parameters are estimated through characteristic function method, and Hurst exponent is calculated based on the generalized Hurst exponent approach with narrower confidence interval. Aiming at the difficulties in solving the exact analytical solution and the excessive computation of the numerical solution in the whole process, based on Mellin-Stieltjes transform and direct integration, a semi-analytical solution of RUL distribution function is proposed, which can be readily implemented in practical equipment operations. The proposed performance degradation assessment framework is validated by the novel truck transmission dataset and the benchmark rolling bearing dataset. Experimental results indicate that the developed framework is more effective and superior than other state-of-the-art approaches in terms of RUL prediction. [ABSTRACT FROM AUTHOR]

Evaluating the degradation of hull and ship performance and exploring their degradation pathways is crucial for developing scientific and reasonable ship maintenance plans. This paper proposes a two-stage optimization (TSO) algorithm that combines the Genetic Algorithm (GA) and Long Short-Term Memory (LSTM) network, capable of simultaneously optimizing input features and model parameters to enhance the accuracy and generalization ability of speed prediction models. Additionally, a performance degradation assessment method based on speed loss is provided, aimed at evaluating the degradation of hull and propeller performance, as well as extracting the performance degradation paths. The results indicated that the proposed TSO-LSTM-GA algorithm significantly outperformed existing baseline models. Furthermore, the provided performance degradation assessment method demonstrated certain effectiveness on the target ship data, with a measured degradation rate of 0.00344 kn/d and a performance degradation of 9.569% over 478 days, corresponding to an annual speed loss of 1.257 kn.

Many traditional approaches for performance degradation assessment of rolling bearings, using sensor data, make assumptions about how they degrade or fault evolve. However, the sequential sensor data cannot be directly taken as input in the traditional models since the data always contain noise and change in length. To solve these problems, a convolutional neural network and deep long-short term memory (CNN-DLSTM) based architecture is proposed to obtain an unsupervised H-statistic for performance degradation assessment of rolling bearing using sensor time-series data. Firstly, a CNN is applied to extract local abstract features from raw sensor data. Secondly, a deep LSTM is explored to extract temporal features. CNN-DLSTM is trained to reconstruct the time-series sensor signal reflecting the health condition of rolling bearing. The D- and Q-statistic are used to compute H-statistic which is then used for performance degradation assessment. The proposed approach is evaluated on an experiment with rolling bearings and the results are presented on a public dataset of rolling bearing, verifying that the proposed approach outperforms several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In the existing rolling bearing performance degradation assessment methods, the input signal is usually mixed with a large amount of noise and is easily disturbed by the transfer path. The time information is usually ignored when the model processes the input signal, which affects the effect of bearing performance degradation assessment. To solve the above problems, an end-to-end performance degradation assessment model of railway axle box bearing based on a deep residual shrinkage network and a deep long short-term memory network (DRSN-LSTM) is proposed. The proposed model uses DRSN to extract local abstract features from the signal and denoises the signal to obtain the denoised feature vector, then uses deep LSTM to extract the time-series information of the signal. The healthy time-series signal of the rolling bearing is input into the DRSN-LSTM reconstruction model for training. Time-domain, frequency-domain, and time–frequency-domain features are extracted from the signal both before and after reconstruction to form a multi-domain features vector. The mean square error of the two feature vectors is used as the degradation indicator to implement the performance degradation assessment. Artificially induced defects and rolling bearings life accelerated fatigue test data verify that the proposed model is more sensitive to early failures than mathematical models, shallow networks or other deep learning models. The result is similar to the development trend of bearing failures. [ABSTRACT FROM AUTHOR]

In order to solve the problems of subjectivity in the extraction of traditional degradation features and incomplete degradation information contained in a single sensor signal, a performance degradation assessment and abnormal health status detection method based on information fusion for the quayside crane lifting gearbox is proposed. Firstly, the correlation between the vibration and temperature of the gearbox is analyzed; secondly, the Convolutional Neural Network (CNN) and entropy degradation features from the full fault cycle vibration and temperature data of the lifting gearbox are extracted respectively; then, the final degradation indicators of the vibration and temperature data are obtained, respectively, through feature optimization, and the fusion degradation indicator is obtained by combining the two indicators; finally, the performance degradation assessment and abnormal health status detection of the gearbox are carried out. The effectiveness and superiority of the proposed method in the performance degradation evaluation of the gearbox are verified by comparison, and the proposed method can identify the initial degradation point of the gearbox earlier than the method based on the single vibration degradation index and the method based on the fusion of the traditional vibration degradation feature and the temperature entropy degradation feature. [ABSTRACT FROM AUTHOR]

During the gear hobbing machine process, the hob performance degradation assessment is significant for optimizing the tool changing frequency and improving machining efficiency. It is challenging to recognize hob wear state through vibration analysis and signal processing. Especially in the condition of intense vibration and noise interference, extracting signal features that reflect the wear state is challenging work. This paper proposes a feature extraction method called Cyclic Statistical Energy (CSE) to obtain the hob wear characteristics by tracking the sensitive frequency band. For the method, the vibration signal model of hob spindle is established first based on the vibration-generation mechanism of the machining process. Then, an index E is proposed to track the wear resonance frequency band of the vibration signal. Furthermore, the cyclic statistical order is analyzed and discussed. The hob performance degradation assessment can be realized by analyzing the energy index E with time variation. The experimental setup has been designed, and the two tests have been conducted in the production line: (1) impact hammer test and (2) hob whole life cutting test. The impact hammer test is set to obtain the modal parameters of the hob. Based on this, the whole life cutting test is designed and realized by which the vibration data of whole life with different wear states can be acquired. The proposed method CSE is successfully applied into online industry experiments test. The results show that the proposed method can give a more accurate performance degradation assessment curve for tool condition monitoring compared with traditional methods such as Wavelet Packet Decomposition (WPD). [ABSTRACT FROM AUTHOR]

In light of the problems of a single vibration feature containing limited information on the degradation of rolling bearings, the redundant information in high-dimensional feature sets inaccurately reflecting the reliability of rolling bearings in service, and assessments of the degradation performance being disturbed by outliers and false fluctuations in the signal, this study proposes a method of assessing rolling bearings' performance in terms of degradation using adaptive sensitive feature selection and multi-strategy optimized support vector data description (SVDD). First, a high-dimensional feature set of vibration signals from rolling bearings was extracted. Second, a method combining the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and K-medoids was used to comprehensively evaluate the features with multiple evaluation indicators and to adaptively select better degradation features to construct the sensitive feature set. Next, multi-strategy optimization of the SVDD model was carried out by introducing the autocorrelation kernel regression (AAKR) and a multi-kernel function to improve the ability of the evaluation model to overcome outliers and false fluctuations. Through validation, it could be seen that the method in this study uses samples of rolling bearings in the healthy early stage to establish the evaluation model, which can adaptively determine the starting point of the bearing's degradation. The stability and accuracy of the model were effectively improved. [ABSTRACT FROM AUTHOR]

The evidence reasoning (ER) rule has been widely used in various fields to deal with both quantitative and qualitative information with uncertainty. However, when analyzing dynamic systems, the importance of various indicators frequently changes with time and working conditions, such as performance degradation assessment of complex electromechanical systems, and the weights of the traditional evidence reasoning rules cannot be appropriately adjusted. To solve this problem, this paper proposes an adaptive evidence reasoning (AER) rule that can adjust weights according to different times and working conditions. The AER rule has two unique features: adaptive weight operation under time division and adaptive weight operation under working-condition division, which are used to solve the problem of dynamic weight adjustment under different times and working conditions. The CMA-ES algorithm is used to optimize the model parameters. Two case studies of performance degradation assessment are established to prove the advantage of the AER rule: a computer numerical control experiment and a simulation experiment of turbofan aeroengine. The results verify the effectiveness and practicability of the proposed method., Competing Interests: Declaration of Competing Interest The authors declare that they have no Declaration of Interest Statement. We declare that we do not have any competing financial interests or personal relationships., (Copyright © 2024 ISA. Published by Elsevier Ltd. All rights reserved.)

Signal de-noising is one of challenging tasks in the slewing bearing performance degradation assessment due to the strong background noise and early weak fault characteristics. Adaptive digital driven methods like local mean decomposition (LMD) and robust local mean decomposition (RLMD) are a promising approach to implement signal-noise separation. However, the mode mixing problem limits its practical applications. Based on noise-assisted approach, complete ensemble robust local mean decomposition with adaptive noise (CERLMDAN) is used to solve this problem. Then, statistic detection of kernel principle component analysis (KPCA) is employed to select fault components for reconstruction and de-noising. After that, square prediction error (SPE) is utilized for performance degradation assessment model establishment. In the decomposition process of CERLMDAN, moth-flame optimization (MFO) is proposed to optimize the noise amplitude and ensemble trials for the improvement of signal decomposition ability. Meanwhile, a comparison is conducted between ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), LMD, RLMD, CERLMDAN and MFO-CERLMDAN. The effectiveness of the proposed method is validated using numerical as well as experimental signals obtained through a slewing bearing highly accelerated life test. The results illustrate that MFO-CERLMDAN gets better result in signal de-noising, and SPE based on MFO-CERLMDAN-KPCA can assess the performance degradation of slewing bearing effectively.

The evaluation of rolling bearing performance degradation has important implications for the prediction and health management (PHM) of rotating equipment. A method for evaluation of rolling bearing performance degradation based on comprehensive index reduction and support vector data description (SVDD) is proposed in this study. Firstly, the improved variational mode decomposition (VMD) method was used to decompose vibration signals, and the defect frequency amplitude ratio index which is sensitive to early faults is extracted. Secondly, a comprehensive feature index set of rolling bearings is constructed by combining traditional time-domain and time–frequency-domain indexes, and the main features are extracted by the dimensionality reduction algorithm of locally linear embedding (LLE). Finally, the SVDD evaluation model was utilized to characterize and evaluate the rolling bearing lifetime degradation process using the distance from the test sample to the trained hypersphere center. Results showed that the proposed comprehensive degradation index can accurately detect the occurrence of early weak fault stage of rolling bearings and objectively reveal the performance degradation process of rolling bearings. [ABSTRACT FROM AUTHOR]

In order to solve the problems of subjectivity in the extraction of traditional degradation features and incomplete degradation information contained in a single sensor signal, a performance degradation assessment and abnormal health status detection method based on information fusion for the quayside crane lifting gearbox is proposed. Firstly, the correlation between the vibration and temperature of the gearbox is analyzed; secondly, the Convolutional Neural Network (CNN) and entropy degradation features from the full fault cycle vibration and temperature data of the lifting gearbox are extracted respectively; then, the final degradation indicators of the vibration and temperature data are obtained, respectively, through feature optimization, and the fusion degradation indicator is obtained by combining the two indicators; finally, the performance degradation assessment and abnormal health status detection of the gearbox are carried out. The effectiveness and superiority of the proposed method in the performance degradation evaluation of the gearbox are verified by comparison, and the proposed method can identify the initial degradation point of the gearbox earlier than the method based on the single vibration degradation index and the method based on the fusion of the traditional vibration degradation feature and the temperature entropy degradation feature.

In the existing rolling bearing performance degradation assessment methods, the input signal is usually mixed with a large amount of noise and is easily disturbed by the transfer path. The time information is usually ignored when the model processes the input signal, which affects the effect of bearing performance degradation assessment. To solve the above problems, an end-to-end performance degradation assessment model of railway axle box bearing based on a deep residual shrinkage network and a deep long short-term memory network (DRSN-LSTM) is proposed. The proposed model uses DRSN to extract local abstract features from the signal and denoises the signal to obtain the denoised feature vector, then uses deep LSTM to extract the time-series information of the signal. The healthy time-series signal of the rolling bearing is input into the DRSN-LSTM reconstruction model for training. Time-domain, frequency-domain, and time–frequency-domain features are extracted from the signal both before and after reconstruction to form a multi-domain features vector. The mean square error of the two feature vectors is used as the degradation indicator to implement the performance degradation assessment. Artificially induced defects and rolling bearings life accelerated fatigue test data verify that the proposed model is more sensitive to early failures than mathematical models, shallow networks or other deep learning models. The result is similar to the development trend of bearing failures.

Aiming at the problems of unclear early fault characteristics and difficult extraction of rolling bearings, a new nonlinear dynamic analysis method called refined composite multi-scale amplitude-aware permutation entropy (RCMAAPE) is introduced in this paper. Firstly, RCMAAPE is used to extract features from the bearing life data, and Chebyshev’s inequality is used to establish a health threshold to evaluate the performance degradation state. Secondly, RCMAAPE is also used for bearing fault diagnosis. Both experimental results prove that RCMAAPE could extract fault characteristics effectively. RCMAAPE can accurately reflect the degradation trend of bearing in the whole life process, and is especially sensitive to the early failure of the bearing. RCMAAPE is also able to effectively identify the states of bearing faults. Especially after selecting the features, RCMAAPE only needs a small number of features to effectively identify the different states of the bearing and the recognition accuracy is up to 100%. Compared with the existing methods, the proposed method can extract fault features more effectively, has higher computational efficiency and obvious advantages.

In light of the problems of a single vibration feature containing limited information on the degradation of rolling bearings, the redundant information in high-dimensional feature sets inaccurately reflecting the reliability of rolling bearings in service, and assessments of the degradation performance being disturbed by outliers and false fluctuations in the signal, this study proposes a method of assessing rolling bearings’ performance in terms of degradation using adaptive sensitive feature selection and multi-strategy optimized support vector data description (SVDD). First, a high-dimensional feature set of vibration signals from rolling bearings was extracted. Second, a method combining the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and K-medoids was used to comprehensively evaluate the features with multiple evaluation indicators and to adaptively select better degradation features to construct the sensitive feature set. Next, multi-strategy optimization of the SVDD model was carried out by introducing the autocorrelation kernel regression (AAKR) and a multi-kernel function to improve the ability of the evaluation model to overcome outliers and false fluctuations. Through validation, it could be seen that the method in this study uses samples of rolling bearings in the healthy early stage to establish the evaluation model, which can adaptively determine the starting point of the bearing’s degradation. The stability and accuracy of the model were effectively improved.

The conventional signal processing based methods are difficult to achieve satisfactory results for rolling element bearings (REBs)' weak fault due to the serious influence of interference signal. Intelligent classification technology and the arising popular monitoring technology-performance evaluation assessment (PDA) are the research hotspots of fault diagnosis of REB in recent years, which could resolve the above problem to some extent. Especially the latter could reflect the operating status of the equipment more comprehensively. Effective feature extraction basing on signal processing methods and intelligent algorithm are the two key aspects for the above two technologies which will determine their effectiveness to great extent. Multifractal detrended fluctuation analyses (MDFA) is an effective non-stationary signal processing method which could reveal the multifractality buried in nonlinear and nonstationary vibration signals of REB, and continuous hidden markov model (CHMM) is a mature intelligent algorithm with solid theoretical basis and rich mathematical structure. So a diagnosis method basing on combination of MDFA with CHMM is proposed in the paper, and it could deal with both fault classification and PDA tasks for the diagnosis of REBs. Effectiveness of the proposed method is validated by two different diagnosis experiments, one for fault classification and another for lifecycle performance evaluation of REBs. Compared to state-of-the-art peer methods, the proposed method has the best performance when dealing with fault diagnosis tasks for REBs. [ABSTRACT FROM AUTHOR]

Performance degradation assessment (PDA) is of great significance to ensure safety and availability of mechanical equipment. As an important issue of PDA, the robustness of the trained model directly affects the assessment efficiency and restricts its application in practice. This paper proposes a robust modeling approach based on Student's t-hidden Markov model (Student's t-HMM) and nuisance attribute projection (NAP). NAP can remove nuisance attributes caused by individual differences from the feature space. Student's t-HMM utilizes the finite Student's t-mixture models (SMMs) to describe the observation emission densities associated with each hidden state, which can be more tolerant towards outliers than conventional HMMs. Based on these two techniques, the proposed method is supposed to be more robust and can assess the performance degradation process of new objects based on data of tested objects. The superiority of the proposed approach is evaluated using the vibration data from the accelerated life tests of bearings and the public XJTU-SY Bearing Datasets. The results demonstrate the robustness and effectiveness of the proposed approach for PDA modeling of rolling element bearings.

The evaluation of rolling bearing performance degradation has important implications for the prediction and health management (PHM) of rotating equipment. A method for evaluation of rolling bearing performance degradation based on comprehensive index reduction and support vector data description (SVDD) is proposed in this study. Firstly, the improved variational mode decomposition (VMD) method was used to decompose vibration signals, and the defect frequency amplitude ratio index which is sensitive to early faults is extracted. Secondly, a comprehensive feature index set of rolling bearings is constructed by combining traditional time-domain and time–frequency-domain indexes, and the main features are extracted by the dimensionality reduction algorithm of locally linear embedding (LLE). Finally, the SVDD evaluation model was utilized to characterize and evaluate the rolling bearing lifetime degradation process using the distance from the test sample to the trained hypersphere center. Results showed that the proposed comprehensive degradation index can accurately detect the occurrence of early weak fault stage of rolling bearings and objectively reveal the performance degradation process of rolling bearings.

Bearing performance degradation assessment is essential to avoid abrupt machinery breakdown. However, background noise, outliers, and other interferences in the monitoring data may restrict the accuracy and stability of bearing performance degradation assessment in practical applications. In this study, a bearing performance degradation assessment method based on the topological representation and hidden Markov model is proposed. To construct a robust and representative feature space, the topological representations, specifically, topological meshes of the original features are obtained by self-organizing map, which can represent the general structure of the original feature space and eliminate outliers and other interferences. Then, the weight vectors of topological meshes are used as degradation features. Finally, the hidden Markov model is adopted as the assessment model to evaluate the bearing performance degradation tendency and detect the initial degradation effectively. To validate the effectiveness and superiority of the proposed method, two experimental datasets are analyzed. Compared with peer methods, the performance indicator curve of the proposed method presents a more smooth and accurate degradation tendency than comparative methods. Moreover, initial degradation can be identified accurately. [ABSTRACT FROM AUTHOR]

The aircraft environmental control system (AECS) plays a vital role in the normal operation of an airplane; thus, performance degradation of the AECS may result in failure and even catastrophic consequences. Because performance degradation is inevitable, performance degradation assessment is critical to provide information for appropriate maintenance actions. This paper proposed an integrated framework for performance degradation assessment of AECS by introducing the visual cognition theory into the specific method. First, an observer is used to generate a residual error, which contains degradation information of the AECS. Second, the generated residual error is transformed into a two-dimensional image based on a permutation method. Third, inspired by the multichannel characteristic (MCC), the transformed image is decomposed into several subbands based on the nonsubsampled contourlet transform (NCST) for multiscale and multidirectional feature extraction, thus capturing the precise characteristics of degradation. Then, inspired by the manifold sensing characteristic (MSC), the manifold space is established based on diffusion maps (DM) to reveal the intrinsic evolutionary law of health degradation. Finally, the geodesic distance is employed to represent the deviation between the current state and health baseline. As a health indicator, the confidence value (CV), which is derived from the geodesic distance, is capable of representing the degradation trend of the AECS. The whole process of degradation can be tracked in real time based on the proposed method. An AECS simulation model was established in MATLAB/Simulink, and two typical faults were introduced. The results based on simulation data demonstrate that the proposed assessment method can effectively reflect the degradation process of the AECS and the constructed health indicator is reasonable. [ABSTRACT FROM AUTHOR]

As key components in a rotating machinery system, bearings affect the safety of the entire mechanical system. Hence, early-stage monitor of bearing degradation is critical to avoid abrupt mechanical system failure. In this paper, a novel bearing performance assessment model is constructed based on ensemble empirical mode decomposition (EEMD) and affinity propagation (AP) clustering. Unlike most clustering methods, AP clustering, which automatically finds the center of all available clusters, can determine the bearing degradation status without an experience-based selection of the number of degradation states. The original bearing vibration signal is first decomposed by EEMD and its degradation fault features are extracted from the singular-value decomposition of intrinsic mode functions. Then, the degradation features are selected as the input of AP clustering to find the cluster centers of different bearing health statuses: “normal”, “slight”, and “severe”. Last, a health evaluation indicator, referred to as the confidence value, which is obtained from the dissimilarity between actual samples and the various cluster centers, is used to evaluate the bearing health status. To prove the superiority of the approach, the proposed model is compared to various popular clustering methods, including, k-means, k-medoids, fuzzy c-means, Gustafson-Kessel, and Gath-Geva, and commonly used time-domain indicators such as root mean square and kurtosis. The experimental results show that the proposed method outperforms the above time-domain indicators and clustering methods in monitoring early-stage degradation, without presetting the number of clusters.

Gearboxes are critical transmission components in the drivetrain of wind turbine, which have a dominant failure rate and the highest downtime loss in all wind turbine subsystems. However, load variations of wind turbine gearbox are far from smooth and usually nondeterministic, which result in inconsistent data distributions. To solve the problem, a novel performance degradation assessment and prognosis method based on maximum mean discrepancy is proposed to test the difference between data distributions and extract the characteristics of multi-source working conditions data. Besides, the increase in sensors will bring more difficulties to establish prediction models in real-world scenarios due to different installation locations. In view of this, a transfer learning strategy called joint distribution adaptation is utilized to adapt data distribution between multi-sensor signals. Nevertheless, the presence of background noise of wind turbine signals restricts the applicability of these algorithms in practice. To further reduce the distribution difference, a novel criterion is proposed to evaluate and measure the data distribution difference between known and tested working conditions based on the witness function of maximum mean discrepancy. The application and superiority of proposed methodology are validated using a wind turbine gearbox life-cycle test data set. Meanwhile, model comparison and cross-verification are conducted between conventional and proposed prediction models. The results indicate that the proposed method has a better performance in performance degradation assessment for wind turbine gearbox. [ABSTRACT FROM AUTHOR]

Estimating the remaining useful life (RUL) of components is a crucial task to enhance reliability, safety, productivity, and to reduce maintenance cost. In general, predicting the RUL of a component includes constructing a health indicator ( H I ) to infer the current condition of the component, and modelling the degradation process in order to estimate the future behavior. Although many signal processing and data-driven methods have been proposed to construct the H I , most of the existing methods are based on manual feature extraction techniques and require the prior knowledge of experts, or rely on a large amount of failure data. Therefore, in this study, a new data-driven method based on the convolutional autoencoder (CAE) is presented to construct the H I . For this purpose, the continuous wavelet transform (CWT) technique was used to convert the raw acquired vibrational signals into a two-dimensional image; then, the CAE model was trained by the healthy operation dataset. Finally, the Mahalanobis distance (MD) between the healthy and failure stages was measured as the H I . The proposed method was tested on a benchmark bearing dataset and compared with several other traditional H I construction models. Experimental results indicate that the constructed H I exhibited a monotonically increasing degradation trend and had good performance in terms of detecting incipient faults. [ABSTRACT FROM AUTHOR]

According to the characteristics of the constant deceleration braking system of hoist, a variable step-size fruit fly optimization algorithm–complex Gaussian wavelet support vector data description method is proposed in this article to evaluate the performance of the brake system. This method takes the pressure–time curve of safety braking as the characteristic data and extracts the candidate characteristic parameters from it. After the comprehensive evaluation of the characteristic parameters based on correlation, monotonicity, and predictability, the characteristic parameters are selected to evaluate the overall performance of the brake system. The proposed variable step-size fruit fly optimization algorithm–complex Gaussian wavelet support vector data description model avoids the situation where the classical Drosophila optimization algorithm is easy to fall into the local optimum. The reliability of the proposed method is verified by the simulation data, and the practicability of the proposed method is verified by the test-bed data. The performance evaluation model of variable step-size fruit fly optimization algorithm–complex Gaussian wavelet support vector data description is constructed, which realizes the quantitative evaluation of the brake system health state and provides technical support for the condition-based maintenance and intelligent maintenance of the hoist brake system. [ABSTRACT FROM AUTHOR]

Dimensionality is a decent term utilized for the dimension-related issues of feature vectors for the condition assessment of bearings. It has been a challenging task while dealing with the characteristic information conservation of the sampled data. Feature vectors with higher dimensions provide an accurate description of the condition, while lower dimension vectors are easy to be classified. These conditions make the dimensionality of feature space a big challenge in the performance evaluation of critical machine parts like bearings. This paper presents a judicious and wise application of a linear approach, locality sensitive discriminant analysis along with the rational use of local mean decomposition in performance degradation assessment. The combination effectively solves the problems of insufficient training samples and large dimensionality of fault features, which imparts excessive noise and causes loss of competent hidden characteristics. In such cases, local structures of feature space are more crucial than the global one. These obstacles can be answered by imparting locality sensitive discriminant analysis as an initial operation. Locality sensitive discriminant analysis is a linear dimensionality reduction tool, which explores the precise projections that amplify the margin between data points and prepare a conservation aid to preserve faulty information of bearings. The steps are followed to achieve the same: decomposition of vibration signal into product functions; calculation of fault features; third, higher dimensionality of the features is reduced with the implementation of locality sensitive discriminant analysis and some prime features are selected with the proposed criterion; the reduced features are further clustered and a trained model of assessment is prepared. Finally, health indicator is calculated from the trained model and test features. The proposed technique is verified on two bearing datasets. The superiority of the technique has been envisaged by comparing the method with different similar assessment methods i.e. time domain features, linear discriminant analysis, and principal component analysis. [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.

• Random matrix theory is introduced into degradation assessment of rolling bearing. • A quantitative indicators for evaluating degradation of rolling bearing is given. • The rolling bearing monitoring data is modeled directly by random matrix. Aiming at problems of feature extraction of monitoring data may lose useful information and different feature extraction methods have great influence on the results in rolling bearing performance degradation assessment. A method which is based on random matrix theory to assess the degradation of rolling bearing is proposed to improve production safety. Firstly, the observation matrix was constructed by the raw data of rolling bearing health monitoring, and the random matrix model of rolling bearing operating states was established by the steps of normalization, singular matrix singular value decomposition and standardization. Then, three performance degradation indicators, the average spectral radius, the maximum eigenvalue, and the number of random points in the inner ring are proposed by constructing the linear eigenvalue statistic of the random matrix, combined with the single ring curve of the bearing operating states, to describe the fluctuation discipline of the random matrix elements and the variation law of the characteristic root distribution, thus characterize the statistical random characteristics of the operating states of the rolling bearing as a whole. Finally, the method was applied by the rolling bearing life test vibration data. And four different operation states of the bearing are selected to build the random matrix model and analysis. The analysis results are calculated by single ring curves and quantitative indicators of different states and proved that the method proposed in this paper can effectively identify different degradation states and realize safety states of rolling bearing, meanwhile, it can quantitatively assess the degree of performance degradation. [ABSTRACT FROM AUTHOR]

The performance degradation assessment of ball bearings is of great importance to increase the efficiency and the reliability of rotating mechanical systems. The large dimensionality of feature space introduces a lot of noise and buries the potential information about faults hidden in the feature data. This paper proposes a novel health assessment method facilitated with two compatible methods, namely curvilinear component analysis and self-organizing map network. The novelty lies in the implementation of a vector quantization approach for the sub-manifolds in the feature space and to extract the fault signatures through nonlinear mapping technique. Curvilinear component analysis is a nonlinear mapping tool that can effectively represent the average manifold of the highly folded information and further preserves the local topology of the data. To answer the complications and to accomplish reliability and accuracy in bearing performance degradation assessment, the work is carried out with following steps; first, ensemble empirical mode decomposition is used to decompose the vibration signals into useful intrinsic mode functions; second, two fault features i.e. singular values and energy entropies are extracted from the envelopes of the intrinsic mode function signals; third, the extracted feature vectors under healthy conditions, further reduced with curvilinear component analysis are used to train the self-organizing map model; finally, the reduced test feature vectors are supplied to the trained self-organizing map and the confidence value is obtained. The effectiveness of the proposed technique is validated on three run-to-failure test signals with the different type of defects. The results indicate that the proposed technique detects the weak degradation earlier than the widely used indicators such as root mean square, kurtosis, self-organizing map-based minimum quantization error, and minimum quantization error-based on the principal component analysis. [ABSTRACT FROM AUTHOR]

• A novel method for signal de-noising based on complete ensemble empirical mode decomposition with adaptive noise and kernel principal component analysis (CEEMDAN-KPCA) is proposed. • The fusion of multi-sensor is established based on KPCA and Hotelling T 2 statistics. • The continuous T 2(C − T 2) is utilized to build a health evaluation model. • The remaining useful life (RUL) prediction model based on extreme learning machine optimized by fruit fly of algorithm (ELM-FOA) is proposed. • The effectiveness of the proposed method is validated on simulated and experimental vibration data. Gearbox is a critical transmission component in the drivetrain of wind turbine having a dominant failure rate and a highest downtime loss among all wind turbine subsystems. Ensemble empirical mode decomposition and principal component analysis have been extensively investigated for signal decomposition and fault feature extraction from the signals of a wind turbine gearbox. However, presence of background noise in wind turbine signals restricts the applicability of these algorithms in real scenarios. To solve this problem, a novel performance degradation assessment method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and kernel principal component analysis (KPCA) was proposed to de-noise and fuse vibration signals. A comparison is conducted between CEEMDAN-KPCA and other five cross combinations. After feature extraction, extreme learning machine (ELM) optimized by fruit fly of algorithm (FOA) is employed to predict the remaining use life (RUL) of wind turbine gearbox. To avoid the tedious hand-crafting hidden nodes, a dynamic adjustment strategy is presented for the improvement of search efficiency. The effectiveness of the proposed method is validated using simulated and experimental vibration signals. The results illustrate that the CEEMDAN-KPCA gets better result in signal de-noising. Compared with different swarm intelligence algorithms, the RUL prediction rates of wind turbine gearbox are improved by using the FOA-ELM prediction model with multiple parameters. [ABSTRACT FROM AUTHOR]

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]

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Estimating the remaining useful life (RUL) of components is a crucial task to enhance reliability, safety, productivity, and to reduce maintenance cost. In general, predicting the RUL of a component includes constructing a health indicator (HI) to infer the current condition of the component, and modelling the degradation process in order to estimate the future behavior. Although many signal processing and data-driven methods have been proposed to construct the HI, most of the existing methods are based on manual feature extraction techniques and require the prior knowledge of experts, or rely on a large amount of failure data. Therefore, in this study, a new data-driven method based on the convolutional autoencoder (CAE) is presented to construct the HI. For this purpose, the continuous wavelet transform (CWT) technique was used to convert the raw acquired vibrational signals into a two-dimensional image; then, the CAE model was trained by the healthy operation dataset. Finally, the Mahalanobis distance (MD) between the healthy and failure stages was measured as the HI. The proposed method was tested on a benchmark bearing dataset and compared with several other traditional HI construction models. Experimental results indicate that the constructed HI exhibited a monotonically increasing degradation trend and had good performance in terms of detecting incipient faults.

The rolling bearing performance degradation assessment method is proposed by the joint use of wavelet packet Tsallis entropy（ WPTE） and fuzzy C- means（ FCM）. Firstly,the WPTEs of normal data and final failure data are extracted followed by the evaluation model of performance degradation being built by using FCM. Then,the degree of tested data belonging to the final failure state is employed as an index to evaluate the level of bearing performance degradation in a quantitative way. Meanwhile,a threshold is set to alert incipient faults. Experimental analysis shows that the proposed index of bearing performance degradation is able to detect bearing fault in its early stage and has stronger robustness to the noise in comparison with the assessment method based on wavelet packet entropy and FCM. In addition,the proposed index has a value located in [0,1]and a reasonable interpretation to the evaluated bearing health condition.

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]

Exploring effective indicators is significant for the assessment of the bearing performance degradation, which is crucial to realize the condition-based maintenance. In this paper, the cross-fuzzy entropy is introduced and is used to measure the similarity of patterns between normal signals and tested signals of the rolling element bearings, and the degree of similarity is used as an indicator of the bearing performance degradation. The original cross-fuzzy entropy focuses on the local characteristics of the signal and neglects its global trend. However, the global characteristics and global trends of bearing vibration signals may vary as the bearings degrade gradually. Therefore, a change has been made in the implementation of the original cross-fuzzy entropy algorithm to overcome this limitation and the modified cross-fuzzy entropy is more suitable for reflecting the whole degradation process of rolling element bearings. The experimental results demonstrate that the modified cross-fuzzy entropy can assess the bearing performance degradation process over their whole life time clearly and effectively. [ABSTRACT FROM AUTHOR]

Rolling element bearings are critical components of rotating machines since the failure of rolling element bearings may cease the functioning of the entire equipment. The damages observed due to bearing failures are expeditious in nature and hence the need to develop an effective prognostic methodology becomes a requisite to prevent the sudden machinery breakdown. The performance degradation assessment and accurate determination of remaining useful life are the two key issues in prognostics of rolling element bearings. This paper proposes a degradation indicator based on selforganising map for the performance degradation assessment of bearings and later support vector regression is utilised to estimate the remaining useful life of bearings. The time-domain and frequency domain features extracted from the raw bearing vibration signals are supplied to the self-organising map classifier to achieve the degradation index termed as self-organising map-minimum quantisation error evolution in the paper. For estimating the remaining useful life of bearings, first the central trend of minimum quantisation error is extracted to achieve the feature vector defined as bearing health index in this work. The bearing health index is then used as input and the life percentage of the bearing is set to output in order to build the support vector regression prediction model for remaining useful life estimation of bearings. The proposed method is validated on the vibration signatures collected in a bearing test rig. The results show that the advocated method can efficiently track the evolution of deterioration and predict the remaining useful life of bearings. [ABSTRACT FROM AUTHOR]

• Two impulse-driven measures for signal processing optimization and HI construction. • Adaptive signal processing based on improved morphological and bandpass filters. • Practical health indicator based on sensitive features and HI performance criteria. • A hybrid model integrating two types of degradation models for accurate prediction. • Stable and accurate bearing prognostics, particularly long-term RUL predictions. Data-driven predictions for bearing remaining useful lifetime (RUL) exhibit the potential in maintaining safety and reliability in industry. However, it is still a challenge to construct an accurate health indicator (HI) and RUL prediction model, particularly for long-term predictions. In this study, a novel approach is developed for bearing prognosis. First, an improved morphological filter and an adaptive bandpass filter are designed to accurately identify resonant frequency bands of bearings and extract weak impulses from noisy vibrations. Two impulse-driven measures, namely fault frequency amplitude (FFA) and its ratio, are newly defined to optimize parameters for signal processing. FFA is also selected as a sensitive feature for assessing bearing degradation. Second, a practical HI is designed based on multi-domain features and feature selection. The HI generates a smooth and monotonic degradation trend while maintaining sensitivity towards incipient defects. Finally, a hybrid model is constructed based on two popular degradation models to improve prediction accuracy. The results, obtained for wheelset bearings and two open-source bearings, demonstrate that the proposed measures and processing methods can adaptively extract repetitive impulses. Furthermore, the constructed HI and hybrid model perform more stable and accurate RUL predictions for different bearings and prediction steps. [ABSTRACT FROM AUTHOR]

Performance degradation assessment of rolling element bearings is vital for the reliable and cost-efficient operation and maintenance of rotating machines, especially for the implementation of condition-based maintenance (CBM). For robust degradation assessment of rolling element bearings, uncertainties such as those induced from usage variations or sensor errors must be taken into account. This paper presents an information exergy index for bearing performance degradation assessment that combines singular value decomposition (SVD) and the information exergy method. Information exergy integrates condition monitoring information of multiple instants and multiple sensors, and thus performance degradation assessment uncertainties are reduced and robust degradation assessment results can be obtained using the proposed index. The effectiveness and robustness of the proposed information exergy index are validated through experimental case studies.

Linear Brownian motion with constant drift is widely used in remaining useful life predictions because its first hitting time follows the inverse Gaussian distribution. State space modelling of linear Brownian motion was proposed to make the drift coefficient adaptive and incorporate on-line measurements into the first hitting time distribution. Here, the drift coefficient followed the Gaussian distribution, and it was iteratively estimated by using Kalman filtering once a new measurement was available. Then, to model nonlinear degradation, linear Brownian motion with adaptive drift was extended to nonlinear Brownian motion with adaptive drift. However, in previous studies, an underlying assumption used in the state space modelling was that in the update phase of Kalman filtering, the predicted drift coefficient at the current time exactly equalled the posterior drift coefficient estimated at the previous time, which caused a contradiction with the predicted drift coefficient evolution driven by an additive Gaussian process noise. In this paper, to alleviate such an underlying assumption, a new state space model is constructed. As a result, in the update phase of Kalman filtering, the predicted drift coefficient at the current time evolves from the posterior drift coefficient at the previous time. Moreover, the optimal Kalman filtering gain for iteratively estimating the posterior drift coefficient at any time is mathematically derived. A discussion that theoretically explains the main reasons why the constructed state space model can result in high remaining useful life prediction accuracies is provided. Finally, the proposed state space model and its associated Kalman filtering gain are applied to battery prognostics. [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]

Bearings are one of the most frequently used components in the rotatory machinery, so the performance degradation assessment of bearings plays an important role in the prognostics and health management of systems. Hidden Markov model (HMM) is a widely applied data-driven model used for bearing performance degradation assessment and has many successful applications. A normal HMM needs to be trained in advance, which has close relationship with the evaluation system. However, the trained HMM is quite influenced by many issues, such as the data integrity and the feature space. In this paper, an intelligent bearing performance degradation assessment method based on HMM and nuisance attribute projection (NAP) is proposed. The proposed method can combine the information from the experimental data and the real-time data effectively and assess the performance since the beginning of the monitoring. The effectiveness of the proposed method is verified through an accelerated life test of rolling element bearings. [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]

As a key component in rotating machinery, the operating reliability of bearing influences the performance and service life of the equipment directly. In order to describe bearing performance degradation (BPD) process effectively, an assessment approach combining extension and ensemble empirical mode decomposition (EEMD) was proposed. First, the extension was utilized to construct the matter-element of bearing operating state, and the energy moment of intrinsic mode functions (IMFs) was used as characteristic parameter of the matter-element. Then, to determine classical domains of characteristic parameters, the mathematical statistics method was adopted. Finally, the BPD was analyzed qualitatively and quantitatively according to the comprehensive correlation degree of bearing current operating state related to its healthy state. The analytic results of bearing test-rig show that the proposed method indicates the incipient fault approximately occuring in the 81st hour, and the method also quantitatively presents the degree of BPD. By contrast, the BPD assessment based on time-domain features extraction method could not achieve the above two results effectively. [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]