Model-Based Fault Detection Method for Coil Burnout in Solenoid Valves Subjected to Dynamic Thermal Loading

Jo, Soo-Ho; Seo, Boseong; Oh, Hyunseok; Youn, Byeng D.; Lee, Dong Ki

doi:10.1109/access.2020.2986537

Cited by 16 publications

(10 citation statements)

References 27 publications

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“…In future works, two topics are under consideration as follows. First, it should be stated that lots of mechanical systems (e.g., railway [68]) with which a PnC-based PEH system may be equipped are exposed to dynamic thermal loading (e.g., external temperature environment and Joule heating during operation). Since the mechanical strain and stress in both the host plate and piezoelectric materials can change due to the temperature effects, not only the absolute output electric power [69], but also the broadband characteristics in the PnC-inspired PEH system can be affected by the thermal loading.…”

Section: Discussionmentioning

confidence: 99%

A Phononic Crystal with Differently Configured Double Defects for Broadband Elastic Wave Energy Localization and Harvesting

Youn

2021

Crystals

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Several previous studies have been dedicated to incorporating double defect modes of a phononic crystal (PnC) into piezoelectric energy harvesting (PEH) systems to broaden the bandwidth. However, these prior studies are limited to examining an identical configuration of the double defects. Therefore, this paper aims to propose a new design concept for PnCs that examines differently configured double defects for broadband elastic wave energy localization and harvesting. For example, a square-pillar-type unit cell is considered and a defect is considered to be a structure where one piezoelectric patch is bonded to a host square lattice in the absence of a pillar. When the double defects introduced in a PnC are sufficiently distant from each other to implement decoupling behaviors, each defect oscillates like a single independent defect. Here, by differentiating the geometric dimensions of two piezoelectric patches, the defects’ dissimilar equivalent inertia and stiffness contribute to individually manipulating defect bands that correspond to each defect. Hence, with adequately designed piezoelectric patches that consider both the piezoelectric effects on shift patterns of defect bands and the characteristics for the output electric power obtained from a single-defect case, we can successfully localize and harvest the elastic wave energy transferred in broadband frequencies.

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Section: Discussionmentioning

confidence: 99%

A Phononic Crystal with Differently Configured Double Defects for Broadband Elastic Wave Energy Localization and Harvesting

Youn

2021

Crystals

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“…In order to ensure the accuracy of the voltage and current amplitudes in the data window, the timing of the calibration moment of the traveling wave head needs to be identified in time. Under normal conditions, it is more difficult to match the waveform in the fault area of a critical transmission line, and it is more intuitive to use the numerical magnitude to discern the abnormal area of the traveling wave [7][8][9]. Once a fault limit condition has occurred, however, the fault resistance will then rise and the numerical judgment will not be very accurate.…”

Section: 𝐺 𝜇 𝑅 ∑ 𝜇mentioning

confidence: 99%

Research on fault detection of critical transmission lines based on multi-source data fusion algorithm

Wang

Shang

Zheng

et al. 2023

Eighth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2022)

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Key transmission line fault detection methods have the problem of large detection errors, and design a key transmission line fault detection method based on multi-source data fusion algorithm. The component symmetry between the forward and inverse traveling waves is used to discriminate the traveling wave abnormal region, combine the time window, pinpoint the information of the time field to the daily shift data, construct a tripping warning model based on the multi-source data fusion algorithm, and optimize the critical transmission line fault detection mode. Experimental results:The mean error values of the key transmission line fault detection method in the paper, compared with three other key transmission line fault detection methods, are: 0.059, 0.113, 0.120 and 0.115 respectively, proving that the use of the fault detection method is improved when combined with the multi-source data fusion algorithm.

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“…Previous work without UQ is now described. Jo et al [19] propose a modelbased fault detection method for SVs taken from real braking systems of urban railway vehicles. Guo et al [20] use a data-driven fault diagnosis method for SVs using multi-kernel support vector machines.…”

Section: B Solenoid Valvesmentioning

confidence: 99%

Bayesian Convolutional Neural Networks for Remaining Useful Life Prognostics of Solenoid Valves With Uncertainty Estimations

Mazaev

Crevecoeur

Hoecke

2021

IEEE Trans. Ind. Inf.

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Solenoid valves are essential components of industrial systems and therefore widely used. As they suffer from high failure rates in the field, fault prognosis of these assets plays a major role for improving their maintenance and reliability. In this work, Bayesian convolutional neural networks are used to predict the remaining useful life (RUL) of solenoid valves, by training them on the valve's current signatures. Predictive performance is further improved upon by using salient physical features obtained from an electromechanical model as the network's training input. Results show that our designed network architecture produces well-calibrated uncertainty estimations of the RUL predictive distributions, which is an important concern in prognostic decision-making. I. INTRODUCTIONT HE purpose of Prognostics and Health Management (PHM) is to enable optimal maintenance strategies, as to prevent machine failure, extend the lifetime of machines and reduce operational costs. This is achieved by detecting incipient faults, fault isolation, identification of different fault types (fault diagnostics) and fault prognosis. These techniques typically imply the analysis of healthy and/or faulty conditions indicated by process measurements.Two main approaches exist for estimating the remaining useful life (RUL). Physics-based approaches rely on physical domain knowledge, which describe normal operation and physical degradation laws. Data-driven approaches are based on condition monitoring data, which are used for constructing statistical or machine learning models [1]. The term "hybrid approaches" is commonly used for approaches that combine physics-based and data-driven techniques.Deep learning (DL) techniques are an important subcategory of data-driven approaches. Features are learned automatically at multiple levels of feature representations, which allows DL to learn complex relations mapping the input to the output directly. This contrasts DL with feature-based approaches such as decision tree ensembles or Gaussian process regression, which rely on the construction of features [2]. DL is T.

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Model-Based Fault Detection Method for Coil Burnout in Solenoid Valves Subjected to Dynamic Thermal Loading

Cited by 16 publications

References 27 publications

A Phononic Crystal with Differently Configured Double Defects for Broadband Elastic Wave Energy Localization and Harvesting

A Phononic Crystal with Differently Configured Double Defects for Broadband Elastic Wave Energy Localization and Harvesting

Research on fault detection of critical transmission lines based on multi-source data fusion algorithm

Bayesian Convolutional Neural Networks for Remaining Useful Life Prognostics of Solenoid Valves With Uncertainty Estimations

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