Showing papers on "Bearing (mechanical) published in 2018"

Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network

Abstract: Bearing fault diagnosis is of significance to enhance the reliability and security of electric locomotive. In this paper, a novel convolutional deep belief network (CDBN) is proposed for bearing fault diagnosis. First, an auto-encoder is used to compress data and reduce the dimension. Second, a novel CDBN is constructed with Gaussian visible units to learn the representative features. Third, exponential moving average is employed to improve the performance of the constructed deep model. The proposed method is applied to analyze experimental signals collected from electric locomotive bearings. The results show that the proposed method is more effective than the traditional methods and standard deep learning methods.

A Novel Fault Diagnosis Method Based on Integrating Empirical Wavelet Transform and Fuzzy Entropy for Motor Bearing

Abstract: Motor bearing is subjected to the joint effects of much more loads, transmissions, and shocks that cause bearing fault and machinery breakdown. A vibration signal analysis method is the most popular technique that is used to monitor and diagnose the fault of motor bearing. However, the application of the vibration signal analysis method for motor bearing is very limited in engineering practice. In this paper, on the basis of comparing fault feature extraction by using empirical wavelet transform (EWT) and Hilbert transform with the theoretical calculation, a new motor bearing fault diagnosis method based on integrating EWT, fuzzy entropy, and support vector machine (SVM) called EWTFSFD is proposed. In the proposed method, a novel signal processing method called EWT is used to decompose vibration signal into multiple components in order to extract a series of amplitude modulated–frequency modulated (AM-FM) components with supporting Fourier spectrum under an orthogonal basis. Then, fuzzy entropy is utilized to measure the complexity of vibration signal, reflect the complexity changes of intrinsic oscillation, and compute the fuzzy entropy values of AM-FM components, which are regarded as the inputs of the SVM model to train and construct an SVM classifier for fulfilling fault pattern recognition. Finally, the effectiveness of the proposed method is validated by using the simulated signal and real motor bearing vibration signals. The experiment results show that the EWT outperforms empirical mode decomposition for decomposing the signal into multiple components, and the proposed EWTFSFD method can accurately and effectively achieve the fault diagnosis of motor bearing.

Nonconvex Sparse Regularization and Convex Optimization for Bearing Fault Diagnosis

Abstract: Vibration monitoring is one of the most effective ways for bearing fault diagnosis, and a challenge is how to accurately estimate bearing fault signals from noisy vibration signals. In this paper, a nonconvex sparse regularization method for bearing fault diagnosis is proposed based on the generalized minimax-concave (GMC) penalty, which maintains the convexity of the sparsity-regularized least squares cost function, and thus the global minimum can be solved by convex optimization algorithms. Furthermore, we introduce a k-sparsity strategy for the adaptive selection of the regularization parameter. The main advantage over conventional filtering methods is that GMC can better preserve the bearing fault signal while reducing the interference of noise and other components; thus, it can significantly improve the estimation accuracy of the bearing fault signal. A simulation study and two run-to-failure experiments verify the effectiveness of GMC in the diagnosis of localized faults in rolling bearings, and the comparison studies show that GMC provides more accurate estimation results than L1-norm regularization and spectral kurtosis.

A Hybrid Prognostics Technique for Rolling Element Bearings Using Adaptive Predictive Models

Abstract: Rolling element bearings cause the largest number of failures in induction motors. Predicting an impending failure and estimating the remaining useful life (RUL) of a bearing is essential for scheduling maintenance and avoiding abrupt shutdowns of critical systems. This paper presents a hybrid technique for bearing prognostics that utilizes regression-based adaptive predictive models to learn the evolving trend in a bearing's health indicator. These models are then used to project forward in time and estimate the RUL of a bearing. The proposed algorithm addresses some key issues in existing methods for bearing health prognosis that affect their prognostic performance, specifically determining the time to start prediction (TSP), handling random fluctuations in a bearing's health indicator, and setting a dynamic failure threshold. The proposed algorithm is validated on publicly available bearing prognostics data from the Center for Intelligent Maintenance Systems. Experimental results show that the proposed approach is effective in determining an accurate TSP and failure threshold, as well as handling random fluctuations. Moreover, this approach achieves excellent prognostic performance and estimates the RUL of bearings within the specified error bounds, even at points very close to the TSP, where traditional methods yield relatively poor RUL estimates.

Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review

Abstract: A rolling bearing is an essential component of a rotating mechanical transmission system Its performance and quality directly affects the life and reliability of machinery Bearings’ performance and reliability need high requirements because of a more complex and poor working conditions of bearings A bearing with high reliability reduces equipment operation accidents and equipment maintenance costs and achieves condition-based maintenance First in this paper, the development of technology of the main individual physical condition monitoring and fault diagnosis of rolling bearings are introduced, then the fault diagnosis technology of multi-sensors information fusion is introduced, and finally, the advantages, disadvantages, and trends developed in the future of the detection main individual physics technology and multi-sensors information fusion technology are summarized This paper is expected to provide the necessary basis for the follow-up study of the fault diagnosis of rolling bearings and a foundational knowledge for researchers about rolling bearings

Rolling Bearing Fault Diagnosis Using Modified LFDA and EMD With Sensitive Feature Selection

Abstract: In order to improve the accuracy of bearings fault diagnosis, one of the most crucial components of rotating machinery, a novel features extraction procedure incorporating an improved features dimensionality reduction method is proposed. In the first step, using the empirical mode decomposition method, the original statistical characteristics were calculated from intrinsic mode functions of the vibration signal. Due to information redundancy of the original statistical characteristics, this paper presents a novel features extraction method that combines K-means method and standard deviation to select the most sensitive characteristics. Furthermore, a modified features dimensionality reduction method is proposed, to realize the low-dimensional representations for high-dimensional feature space. Finally, the performance of the fault diagnosis model is evaluated by vibration signals with 12 bearing fault conditions, which are provided by Bearing Data Center of Case Western Reserve University. Experiment results show that the proposed fault diagnosis model can serve as an effective and adaptive bearing fault diagnosis system.

A Comprehensive Study on Shaft Voltages and Bearing Currents in Rotating Machines

Abstract: This paper puts into perspective the state-of-the art knowledge on bearing currents and diagnosis tools, which rely on shaft voltages and bearing currents phenomena. Today, shaft signals measurements are progressively included in predictive health monitoring of large turbo and hydro generators. In addition, bearing fault diagnosis through shaft signals have recently gained some attention to improve reliability and estimation of the remaining useful life in electrical drives. The main objective of this paper is to provide diagnosticians with a broad overview of the current trends in fault diagnosis through shaft signals. Therefore, the study presents a critical review of the measurements methods, advantages, and disadvantages of fault diagnosis methods, and suggests future possible enhancements for bearing health monitoring through shaft signals.

A Novel Bearing Multi-Fault Diagnosis Approach Based on Weighted Permutation Entropy and an Improved SVM Ensemble Classifier.

Abstract: Timely and accurate state detection and fault diagnosis of rolling element bearings are very critical to ensuring the reliability of rotating machinery. This paper proposes a novel method of rolling bearing fault diagnosis based on a combination of ensemble empirical mode decomposition (EEMD), weighted permutation entropy (WPE) and an improved support vector machine (SVM) ensemble classifier. A hybrid voting (HV) strategy that combines SVM-based classifiers and cloud similarity measurement (CSM) was employed to improve the classification accuracy. First, the WPE value of the bearing vibration signal was calculated to detect the fault. Secondly, if a bearing fault occurred, the vibration signal was decomposed into a set of intrinsic mode functions (IMFs) by EEMD. The WPE values of the first several IMFs were calculated to form the fault feature vectors. Then, the SVM ensemble classifier was composed of binary SVM and the HV strategy to identify the bearing multi-fault types. Finally, the proposed model was fully evaluated by experiments and comparative studies. The results demonstrate that the proposed method can effectively detect bearing faults and maintain a high accuracy rate of fault recognition when a small number of training samples are available.

The Routine Mineralogical Analysis of Clay-Bearing Samples

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Health Degradation Monitoring and Early Fault Diagnosis of a Rolling Bearing Based on CEEMDAN and Improved MMSE

Abstract: Rolling bearings play a crucial role in rotary machinery systems, and their operating state affects the entire mechanical system. In most cases, the fault of a rolling bearing can only be identified when it has developed to a certain degree. At that moment, there is already not much time for maintenance, and could cause serious damage to the entire mechanical system. This paper proposes a novel approach to health degradation monitoring and early fault diagnosis of rolling bearings based on a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved multivariate multiscale sample entropy (MMSE). The smoothed coarse graining process was proposed to improve the conventional MMSE. Numerical simulation results indicate that CEEMDAN can alleviate the mode mixing problem and enable accurate intrinsic mode functions (IMFs), and improved MMSE can reflect intrinsic dynamic characteristics of the rolling bearing more accurately. During application studies, rolling bearing signals are decomposed by CEEMDAN to obtain IMFs. Then improved MMSE values of effective IMFs are computed to accomplish health degradation monitoring of rolling bearings, aiming at identifying the early weak fault phase. Afterwards, CEEMDAN is performed to extract the fault characteristic frequency during the early weak fault phase. The experimental results indicate the proposed method can obtain a better performance than other techniques in objective analysis, which demonstrates the effectiveness of the proposed method in practical application. The theoretical derivations, numerical simulations, and application studies all confirmed that the proposed health degradation monitoring and early fault diagnosis approach is promising in the field of prognostic and fault diagnosis of rolling bearings.