Remaining useful life estimation of ball bearings by means of monotonic score calibration

Carino, Jesus A.; Zurita, Daniel; Delgado, M.; Ortega, Juan Antonio; Romero-Troncoso, René de Jesús

doi:10.1109/icit.2015.7125351

Cited by 20 publications

(15 citation statements)

References 12 publications

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“…Consequently, linearity correlation does not seem like a rational metric. Spearman coefficient is accordingly introduced to measure the nonlinearity correlation as the HIs are generally the nonlinear 40,41 . Apparently, it is a more reasonable index to depict the HIs and degradation.…”

Section: The Evaluation To the Methods Of Anomaly Calculationmentioning

confidence: 99%

Anomaly Prediction: Successive Analysis for Healthy State Based on the Expansion to Remaining Useful Life

Yin

Liu

2021

Preprint

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The prediction of remaining useful life is the key to realize the condition-based maintenance. However, Existing studies on remaining useful life cannot effectively cope with the scenarios with extremely high requirement to stability. In this paper, the conception of anomaly prediction is introduced to solve the problem by expanding the remaining useful life to healthy stage to describe and predict the equipment condition at this stage. To obtain more reasonable health indicator, inverse number is firstly introduced into the feature selection. On this basis, a novel method is put forward, which consists of center optimization, boundary adjustment and regressive analysis, in order to realize the anomaly prediction and fills the gap of the absence to remaining life research at healthy stage. Two validations are subsequently implemented with the open dataset of XJTU-SY. The experimental results and comparison analysis demonstrate the feasibility and the superiority of proposed method, respectively. The dissection to the remaining useful life based anomaly prediction indicates that the related research is essential and of great significant to the condition-based maintenance.

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Section: The Evaluation To the Methods Of Anomaly Calculationmentioning

confidence: 99%

Anomaly Prediction: Successive Analysis for Healthy State Based on the Expansion to Remaining Useful Life

Yin

Liu

2021

Preprint

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“…In this paper, we shall also refer to those metrics as monotonicity since a correlation between a HI and time induces that the metric is mono-tonic (since time is monotonic in a time series). Spearman's rank correlation was used in Lei et al (2016) and Carino et al (2015) to account for non-linear relationships between the HI and time instead of linear relationships in the conventional Pearson's correlation.…”

Section: Existing Relevance Metricsmentioning

confidence: 99%

Unsupervised Minimum Redundancy Maximum Relevance Feature Selection for Predictive Maintenance

Hamaide

Glineur

2021

IJPHM

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Identifying and selecting optimal prognostic health indicators in the context of predictive maintenance is essential to obtain a good model and make accurate predictions. Several metrics have been proposed in the past decade to quantify the relevance of those prognostic parameters. Other works have used the well-known minimum redundancy maximum relevance (mRMR) algorithm to select features that are both relevant and non-redundant. However, the relevance criterion is based on labelled machine malfunctions which are not always available in real life scenarios. In this paper, we develop a prognostic mRMR feature selection, an adaptation of the conventional mRMR algorithm, to a situation where class labels are a priori unknown, which we call unsupervised feature selection. In addition, this paper proposes new metrics for computing the relevance and compares different methods to estimate redundancy between features. We show that using unsupervised feature selection as well as adapting relevance metrics with the dynamic time warping algorithm help increase the effectiveness of the selection of health indicators for a rotating machine case study.

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“…According to the mechanism of bearing health degradation, an HI with monotonicity over time is critical to machine PDA. In general, spearman coefficient (SC) [27] was applied to evaluate the monotonic relationship between two stochastic sequences. is study employs this coefficient to assess the monotonicity of the indicator over the operating time.…”

Section: Monotonicity Evaluation For Feature Clustering Chartmentioning

confidence: 99%

Feature Clustering Analysis Using Reference Model towards Rolling Bearing Performance Degradation Assessment

Ding

Wang

Huang

et al. 2020

Shock and Vibration

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e health monitoring and management have been accepted in modern industrial machinery for an intelligent industrial production. To timely and reliably assess the bearing performance degradation, a novel health monitoring method called feature clustering analysis (FCA) has been proposed in this study. Along with the working time going, this new monitored chart picked by FCA aims to describe the feature clustering distribution transition by a series of reference models. First, the data provided by the reference state (healthy data) and the one from the monitor state (monitor data) are fused together to construct a reference model, which is to explore the active role of healthy status and activate the difference between healthy status and unhealthy status. Manifold learning is later implemented to mine the discriminated features for good class-separable clustering measure. In this manner, heterogeneous information hidden in this reference model will appear once degradation happened. Finally, a clustering quantification factor, named as feature clustering indicator (FCI), is calculated to assess distribution evolution and migration of the monitor status as compared to the consistent healthy status. Furthermore, a single Gaussian model (SGM) based on these FCIs is used to provide a smooth estimate of the healthy condition level. e corresponding negative log likelihood probability (NLLP) and the fault occurrence alarm are developed for an accurate and reliable FCC. And it can well depict a comprehensibility of the real bearing performance degradation process for its whole life. Meanwhile, as compared to other health profiles based on the classical health indicators, the proposed FCC has provided a much more accurate degradation level and rather monotonic profile. e experimental results show the potential in machine health performance degradation assessment.

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Remaining useful life estimation of ball bearings by means of monotonic score calibration

Cited by 20 publications

References 12 publications

Anomaly Prediction: Successive Analysis for Healthy State Based on the Expansion to Remaining Useful Life

Anomaly Prediction: Successive Analysis for Healthy State Based on the Expansion to Remaining Useful Life

Unsupervised Minimum Redundancy Maximum Relevance Feature Selection for Predictive Maintenance

Feature Clustering Analysis Using Reference Model towards Rolling Bearing Performance Degradation Assessment

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