Robust performance degradation assessment methods for enhanced rolling element bearing prognostics

Qiu, Hai; Lee, Jay; Lin, Jing; Yu, Gang

doi:10.1016/j.aei.2004.08.001

Cited by 375 publications

(193 citation statements)

References 23 publications

(26 reference statements)

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“…They compared the supervised k-nearest neighbor (k-NN) algorithm and unsupervised SOM and found similar performance but k-NN was easier to use. Qui et al 24 applied a wavelet filter and SOM for performance assessment of bearing rolling element. The wavelet filter was used for signal denoising and enhancing of weak signatures and SOM for assessment of bearing degradation utilizing time domain features.…”

Section: Jammu and Danaimentioning

confidence: 99%

Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques

Omenzetter¹,

Lautour²

2010

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2010

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Developed for studying long, periodic records of various measured quantities, time series analysis methods are inherently suited and offer interesting possibilities for Structural Health Monitoring (SHM) applications. However, their use in SHM can still be regarded as an emerging application and deserves more studies. In this research, Autoregressive (AR) models were used to fit experimental acceleration time histories from two experimental structural systems, a 3-storey bookshelf-type laboratory structure and the ASCE Phase II SHM Benchmark Structure, in healthy and several damaged states. The coefficients of the AR models were chosen as damage sensitive features. Preliminary visual inspection of the large, multidimensional sets of AR coefficients to check the presence of clusters corresponding to different damage severities was achieved using Sammon mapping -an efficient nonlinear data compression technique. Systematic classification of damage into states based on the analysis of the AR coefficients was achieved using two supervised classification techniques: Nearest Neighbor Classification (NNC) and Learning Vector Quantization (LVQ), and one unsupervised technique: Self-organizing Maps (SOM). This paper discusses the performance of AR coefficients as damage sensitive features and compares the efficiency of the three classification techniques using experimental data.

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Section: Jammu and Danaimentioning

confidence: 99%

Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques

Omenzetter¹,

Lautour²

2010

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2010

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“…To perform this degradation, a process of normalization is conducted to transform values of features into a common scale and group them as input set for feature-fusion. SOM neural network is employed to combine the input set into a single out indicator which is minimum quantization error (MQE) (Qiu et al, 2003), as shown in Fig. 18.…”

Section: Machine Health Assessment and Failure Threshold Determinationmentioning

confidence: 99%

An intelligent condition-based maintenance platform for rotating machinery

Tran

Yang

2012

Expert Systems with Applications

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Maintenance is of necessity for sustaining machinery availability and reliability in order to ensure productivity, product quality, on-time delivery, and safe working environment. The costly maintenance strategies such as corrective maintenance and scheduled maintenance have been progressively replaced by superior maintenance strategies in which condition-based maintenance (CBM) is one of the delegates. This strategy commonly consists of sequent modules such as data acquisition, signal processing, feature extraction and feature selection, condition monitoring, etc. However, approaches in literature which have been developed for each module and implemented for different applications are standalone instead of a comprehensive system. Furthermore, these approaches have been demonstrated in a laboratory environment without any industrial validations. For these reasons, an intelligent algorithm based CBM platform is proposed in this paper to be applied for rotating machinery easily and effectively. Subsequently, two case-studies are presented in order to evaluate the effectiveness of this platform in industrial applications.

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“…PHM generally combines condition monitoring, fault diagnostics, fault prognostics, and decision support [3]. Remaining useful life (RUL) prediction aims at assessing the performance degradation of equipment and detecting the impending failure [4]. Therefore, accurate RUL prediction is regarded as one of the most central components in performing PHM, since necessary maintenance actions are implemented based on the RUL prediction result [5].…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction of Bearing with Vibration Signals Based on a Novel Indicator

Qiu

2017

Shock and Vibration

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Aiming at reducing the production downtime and maintenance cost, prognostics and health management (PHM) of rotating machinery often includes the remaining useful life (RUL) prediction of bearings. In this paper, a method combining the generalized Weibull failure rate function (WFRF) and radial basis function (RBF) neural network is developed to deal with the RUL prediction of bearings. A novel indicator, namely, the power value on the sensitive frequency band (SFB), is proposed to track bearing degradation process. Generalized WFRF is used to fit the degradation indicator series to reduce the effect of noise and avoid areas of fluctuation in the time domain. RBF neural network is employed to predict the RUL of bearings with times and fitted power values at present and previous inspections as input. Meanwhile, the life percentage is selected as output. The performance of the proposed method is validated by an accelerated bearing run-to-failure experiment, and the results demonstrate the advantage of this method in achieving more accurate RUL prediction.

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Robust performance degradation assessment methods for enhanced rolling element bearing prognostics

Cited by 375 publications

References 23 publications

Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques

Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques

An intelligent condition-based maintenance platform for rotating machinery

Remaining Useful Life Prediction of Bearing with Vibration Signals Based on a Novel Indicator

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