Search

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]

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]

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 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 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]

Exploring an effective assessment index is significant for performance degradation assessment, which has been proposed to realize equipments’ near-zero downtime and maximum productivity. In this paper, the Kolmogorov-Smirnov test based on an autoregressive model is proposed to assess the performance degradation of rolling bearings. Accelerated life test (in Hangzhou Bearing Test and Research Center) of rolling bearings was performed to collect vibration data over a whole lifetime (normal-fault-failure). The result shows that the Kolmogorov-Smirnov test method can obviously detect incipient weak defects and can reflect performance degradation process well. In particular, it can detect abnormal stages earlier before the bearing steps into failure, which is significant in condition maintenance and prognosis. [ABSTRACT FROM PUBLISHER]