Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators

Wang, Dong; Tsui, Kwok‐Leung; Miao, Qiang

doi:10.1109/access.2017.2774261

Cited by 361 publications

(190 citation statements)

References 129 publications

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“…By use of intermittent operation, their sensor node is made with a minimum of components, leading to small size and cost without introducing maintenance requirements or sacrificing lifetime. Other potentially suitable applications for ICSs include predictive maintenance on industrial equipment via vibrational and acoustical monitoring [35]; energy for the system could be supplied by a piezoelectric vibration harvester.…”

Section: (A) Conditions For Intermittent Operationmentioning

confidence: 99%

Energy-driven computing

Sliper

Cetinkaya

Weddell

et al. 2019

Phil. Trans. R. Soc. A.

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For decades, the design of untethered devices has been focused on delivering a fixed quality of service with minimum power consumption, to enable battery-powered devices with reasonably long deployment lifetime. However, to realize the promised tens of billions of connected devices in the Internet of Things, computers must operate autonomously and harvest ambient energy to avoid the cost and maintenance requirements imposed by mains- or battery-powered operation. But harvested power typically fluctuates, often unpredictably, and with large temporal and spatial variability. Energy-driven computers are designed to treat energy-availability as a first-class citizen, in order to gracefully adapt to the dynamics of energy harvesting. They may sleep through periods of no energy, endure periods of scarce energy, and capitalize on periods of ample energy. In this paper, we describe the promise and limitations of energy-driven computing, with an emphasis on intermittent operation. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.

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Section: (A) Conditions For Intermittent Operationmentioning

confidence: 99%

Energy-driven computing

Sliper

Cetinkaya

Weddell

et al. 2019

Phil. Trans. R. Soc. A.

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“…In this process, the traditional algorithm mainly finds a set of features with high contribution rate. Some authors [26][27][28] defined different feature types based on the contribution of features to degradation information (DI). In this case, feature correlation is a measure of the degradation-stage-related information.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction

Tian

Wang

2020

Applied Sciences

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The prediction of the remaining life of a bearing plays a vital role in reducing the accident-related maintenance costs of machinery and in improving the reliability of machinery and equipment. To predict bearing remaining useful life (RUL), the abilities of statistical characteristics to reflect the bearing degradation state differ, and the single prediction model has low generalization ability and a poor prediction effect. An ensemble robust prediction method is proposed here to predict bearing RUL based on the construction of a bearing degradation indicator set: the initial bearing degradation indicator subsets were constructed using the Fast Correlation-Based Filter with Approximate Markov Blankets (FCBF-AMB) and Maximal Information Coefficient (MIC) selection methods. Through the cross-operation of the obtained subsets, we obtained a set of robust degradation indicators. These selected degradation indicators were fed into the long short-term memory (LSTM) neural network prediction model enhanced by the AdaBoost algorithm. We found through calculation that the average prediction accuracy of the proposed method is 91.40%, 92.04%, and 93.25% at 2100, 2250, and 2400 rpm, respectively. Compared with other methods, the proposed method improves the prediction accuracy by 1.8% to 14.87% at most. Therefore, the method proposed in this paper is more accurate than the other methods in terms of RUL prediction.

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“…e rolling bearing is a key part of rotating machines, whose performance determines the operation efficiency and reliability of the whole mechanical equipment [1], such as aircraft engines, high-speed trains, water-pumps, electric fans, and so on. When the rolling bearing is in early failure, the fault information is very weak.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced VMD with the Guidance of Envelope Negentropy Spectrum for Bearing Fault Diagnosis

et al. 2020

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Currently, study on the relevant methods of variational mode decomposition (VMD) is mainly focused on the selection of the number of decomposed modes and the bandwidth parameter using various optimization algorithms. Most of these methods utilize the genetic-like algorithms to quantitatively analyze these parameters, which increase the additional initial parameters and inevitably the computational burden due to ignoring the inherent characteristics of the VMD. From the perspective to locate the initial center frequency (ICF) during the VMD decomposition process, we propose an enhanced VMD with the guidance of envelope negentropy spectrum for bearing fault diagnosis, thus effectively avoiding the drawbacks of the current VMD-based algorithms. First, the ICF is coarsely located by envelope negentropy spectrum (ENS) and the fault-related modes are fast extracted by incorporating the ICF into the VMD. Then, the fault-related modes are adaptively optimized by adjusting the bandwidth parameters. Lastly, in order to identify fault-related features, the Hilbert envelope demodulation technique is used to analyze the optimal mode obtained by the proposed method. Analysis results of simulated and experimental data indicate that the proposed method is effective to extract the weak faulty characteristics of bearings and has advantage over some advanced methods. Moreover, a discussion on the extension of the proposed method is put forward to identify multicomponents for broadening its applied scope.

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Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators

Cited by 361 publications

References 129 publications

Energy-driven computing

Energy-driven computing

An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction

An Enhanced VMD with the Guidance of Envelope Negentropy Spectrum for Bearing Fault Diagnosis

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