A new method for the estimation of bearing health state and remaining useful life based on the moving average cross-correlation of power spectral density

Abstract

In the broad framework of condition-based maintenance, the final objective of bearing condition monitoring is to evaluate the health state and to estimate the remaining useful life of the bearings. The latter is a particularly challenging task, considering that remaining useful life is inextricably linked to a projection of what will happen in the future. Often, health indices, whose reliability relies on their effectiveness and consistency, are used for bearing condition monitoring. Most of the existing health indices pursue the property of monotonicity, but generally there are no obvious boundaries between the different health states of the bearings. Hence, it is quite difficult to give an objective and independent estimation of the health states, especially in real applications under different operating conditions and in the presence of noise. Furthermore, it is also difficult to set the failure threshold for a given health index when it is employed in different applications. In this paper, a new health index called 'MAC 2 PSD' is proposed, based on the moving-average cross-correlation (MAC 2) of the power spectral density (PSD) of the vibration signals. An interesting property of MAC 2 PSD is its capability to track the health condition and to discriminate clearly between the different health states. As shown in the paper, the MAC 2 PSD can also be used to estimate the remaining useful life by using its values during the defect-propagation phase. The effectiveness of MAC 2 PSD is shown by means of two different cases of bearing run-to-failure experimental data, from two different test rigs. Additionally, the capability to avoid false positives is evaluated by means of bearing vibration data measured on a locomotive in commercial service.

Definition of Damage Indices for Railway Axle Bearings: Results of Long-Lasting Tests

“…It is interesting to observe in Figure 8 that SES based indices present a non-mon trend close to the end of the test campaign. The change of the trend of these kind of i has been already observed and described in detail in other papers by the same a [27] and by other authors [35,36] and is typically related to the wear evolution in element bearings. In this paper, for the sake of brevity, a full discussion is not pres but the trend change occurs in the phases of "defect initiation" and "defect propag during the lifetime of the bearing.…”

“…Then, a condition monitoring system for motor, gearbox and axlebox has been designed and installed by the authors on a regional locomotive, which is still in commercial service [23,24]. The data collected during these years have boosted the development of reliable diagnostic [25,26] and even prognostic tools [27] for bearings employed in railway applications. The authors' aim in this paper is at contributing to the diagnostics of RWABs and at defining some suitable indices, which can be used, in a condition monitoring approach, to evaluate the condition of the bearing, following on the research started in [28].…”

Remaining Useful Life Estimation through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent Variables

“…ese can extract abstract information and features from massive datasets while building and discovering complex functional and temporal relationships from the data [9]. Deep learning approaches have been implemented in a great variety of systems for prognostics purposes, such as lithium-ion batteries state of health (SOH) and state of charge (SOC) estimation, [10][11][12][13], RUL estimation in rolling bearings [14][15][16], and turbofan engines [17][18][19][20].…”

Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization

“…Generally, once the rolling bearings fail, it is accompanied by vibration and sound. Therefore, using appropriate technology to process the collected vibration or acoustic signals could well detect potential failures [ 5 , 6 , 7 ]. Feature extraction is a committed step in identifying rolling bearing faults, but the vibration signals present nonlinear and nonstationary characteristics resulting in a limitation in the ability of feature extraction.…”