Differential evolution-based multi-objective optimization for the definition of a health indicator for fault diagnostics and prognostics

Baraldi, Piero; Bonfanti, G.; Zio, Enrico

doi:10.1016/j.ymssp.2017.09.013

Cited by 94 publications

(53 citation statements)

References 66 publications

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“…Therefore, feature ranking is applied to identify descriptive features that effectively can track battery degradation. According to [29] there are three common metrics that are broadly applicable for prognostic feature ranking. Monotonicity based on Spearman's rank correlation coefficient, prognosability and trendability are the suitable criteria to define the prognostic features.…”

Section: Prognostic Feature Rankingmentioning

confidence: 99%

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Ensemble Gradient Boosted Tree for SoH Estimation Based on Diagnostic Features

et al. 2020

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The success of electric vehicles (EVs) depends principally on their energy storage system. Lithium-ion batteries currently feature the ideal properties to fulfil the wide range of prerequisites specific to electric vehicles. Meanwhile, the precise estimation of batteries’ state of health (SoH) should be available to provide the optimal performance of EVs. This study attempts to propose a precise, real-time method to estimate lithium-ion state of health when it operates in a realistic driving condition in the presence of dynamic stress factors. To this end, a real-life driving profile was simulated based on highly dynamic worldwide harmonized light vehicle test cycle load profiles. Afterward, various features will be extracted from voltage data and they will be scored based on prognostic metrics to select diagnostic features which can conveniently identify battery degradation. Lastly, an ensemble learning model was developed to capture the correlation of diagnostic features and battery’s state of health (SoH). The result illustrates that the proposed method has the potential to estimate the SoH of battery cells aged under a distinct depth of discharge and current profile with a maximum error of 1%. This confirms the robustness of the developed approach. The proposed method has the capability of implementing in battery management systems due to many reasons; firstly, it is tested and validated based on the data which are equal to the real-life driving operation of an electric vehicle. Secondly, it has high accuracy and precision, and a low computational cost. Finally, it can estimate the SoH of battery cells with different aging patterns.

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Section: Prognostic Feature Rankingmentioning

confidence: 99%

“…As the battery degrades a descriptive feature expresses higher trendability. The following equation demonstrates the trendability metric [29].…”

Section: Prognostic Feature Rankingmentioning

confidence: 99%

Ensemble Gradient Boosted Tree for SoH Estimation Based on Diagnostic Features

et al. 2020

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“…For instance, there is no real /close remedy to deal with uncertainty, which prevails as the core aspect in the area of multi-objective problem domains. In [4] the main purpose of this research is to present a model that is able to extract health indicators HI's. The aim is to keep track of various signals during operation.…”

Section: Multi-objective Optimization Techniquesmentioning

confidence: 99%

Beyond the Horizon: A Meticulous Analysis of Clinical Decision-Making Practices

Raja¹,

Asghar²

2020

IJACSA

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Clinical advancements are one of the major outcomes of the technological phase shift of data sciences. The significance of information technology in medical sciences by utilizing the Clinical Decision Support System (CDSS) has opened the spillways of exponentially improved predictive models. Utilizing the latest norms of classification algorithms on clinical data are widely incorporated for prognostic assessments. Medical experts have to make decisions that are crucial in nature and if the research can develop a mechanism that assists them in evolving solid reasoning, infer the knowledge and clearly express their clinical decision by justifying their assertions made, it will be a win-win situation. However, this field of science is still an unknown world for clinicians despite the fact that the enormous amount of medical data cannot be exploited to its maximum without invoking the technological support. The objective of this research is to introduce the clinicians and policymakers of the medical domain with the renowned computer-based methodologies employed to construct a clinical decision support system. We expect that gaining the technical insight into the medical domain by the stakeholders will ensure commissioning the accurate and effective CDSS for improved healthcare delivery.

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“…The coefficients of the linear combination were obtained by using a genetic algorithm to assess the capability of a health indicator. Baraldi et al [ 24 ] considered various signal extraction techniques, including statistical indicators, empirical mode decomposition (EMD), wavelet transform and Fourier transform to define a health indicator as the residual of the AAKR model for fault diagnostics and prognostics.…”

Section: Introductionmentioning

confidence: 99%

EEMD-Based Steady-State Indexes and Their Applications to Condition Monitoring and Fault Diagnosis of Railway Axle Bearings

Cai

Wang

Fan

et al. 2018

Sensors

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Railway axle bearings are one of the most important components used in vehicles and their failures probably result in unexpected accidents and economic losses. To realize a condition monitoring and fault diagnosis scheme of railway axle bearings, three dimensionless steadiness indexes in a time domain, a frequency domain, and a shape domain are respectively proposed to measure the steady states of bearing vibration signals. Firstly, vibration data collected from some designed experiments are pre-processed by using ensemble empirical mode decomposition (EEMD). Then, the coefficient of variation is introduced to construct two steady-state indexes from pre-processed vibration data in a time domain and a frequency domain, respectively. A shape function is used to construct a steady-state index in a shape domain. At last, to distinguish normal and abnormal bearing health states, some guideline thresholds are proposed. Further, to identify axle bearings with outer race defects, a pin roller defect, a cage defect, and coupling defects, the boundaries of all steadiness indexes are experimentally established. Experimental results showed that the proposed condition monitoring and fault diagnosis scheme is effective in identifying different bearing health conditions.

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Differential evolution-based multi-objective optimization for the definition of a health indicator for fault diagnostics and prognostics

Cited by 94 publications

References 66 publications

Ensemble Gradient Boosted Tree for SoH Estimation Based on Diagnostic Features

Ensemble Gradient Boosted Tree for SoH Estimation Based on Diagnostic Features

Beyond the Horizon: A Meticulous Analysis of Clinical Decision-Making Practices

EEMD-Based Steady-State Indexes and Their Applications to Condition Monitoring and Fault Diagnosis of Railway Axle Bearings

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