Identification of rock bolt quality based on improved probabilistic neural network

Di, Weiguo; Wang, Mingming; Xiaoyun, Sun; Kang, Fengning; Xing, Hui; Zheng, Haiqing; Bian, Jıanpeng

doi:10.1504/ijmic.2018.094206

Cited by 3 publications

(1 citation statement)

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“…The authors there suggest that reading such a pattern can be successfully used to assess the state of the anchoring. A very interesting method was presented in [24]. This method is based on the analysis of the wavelet spectrum from ultrasonic testing of an anchor using a neural network.…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive Acoustical Rock Bolt Testing System with Intelligent Filtering in Salt Mine ‘Wieliczka’

et al. 2021

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This article presents the application of a self-excited acoustic SAS system for non-destructive testing (NDT) for roof-bolt housings in laboratory and real mine conditions. The proposed system with a filtering mechanism was applied to the J64-27 composite anchors. The conducted tests allowed successful confirmation of the usefulness of the system in the detection of rod defects, damage of the mechanism coupling the anchor to the rock mass and testing of the stress state of the anchor itself. The proposed filtering system allowed eliminating the effect of jump change of frequency in the limit cycle of self-excited system. The proposed method is a novel solution for safety diagnostics of bolt housings in mining applications.

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Section: Introductionmentioning

confidence: 99%

Non-Destructive Acoustical Rock Bolt Testing System with Intelligent Filtering in Salt Mine ‘Wieliczka’

et al. 2021

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Effect of bias magnetic field on transducer efficiency in electromagnetic ultrasonic non-destructive testing of Rock bolt

Dong,

Ma,

Jiang

et al. 2024

Sensors and Actuators A: Physical

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Energy Management Strategy for Plug-In Hybrid Electric Vehicles Based on Driving Condition Recognition: A Review

Liu

Yan

2022

Electronics

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Appropriate energy management strategies (EMSs) have been selected for plug-in hybrid electric vehicles (PHEVs) based on driving-condition recognition (DCR) according to the acquired driving information, so as to minimize the target parameters. With online control and offline optimization, the strategy is suitable for real-time applications and is of great significance to repetitive routes, owing to its simplicity and ease of implementation. This paper aims to identity the DCR-based EMSs, develop efficient EMSs, and invite researchers involved in this field to discover new solutions. This paper presents a comprehensive analysis of EMSs based on DCR in terms of principles, the scope of application as well as their advantages and disadvantages, and provides a thorough survey of the latest progress in EMSs. We concluded that DCR-based EMSs show an improvement in energy saving and the pollutant-discharging effect.

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Identification of rock bolt quality based on improved probabilistic neural network

Cited by 3 publications

References 0 publications

Non-Destructive Acoustical Rock Bolt Testing System with Intelligent Filtering in Salt Mine ‘Wieliczka’

Non-Destructive Acoustical Rock Bolt Testing System with Intelligent Filtering in Salt Mine ‘Wieliczka’

Effect of bias magnetic field on transducer efficiency in electromagnetic ultrasonic non-destructive testing of Rock bolt

Energy Management Strategy for Plug-In Hybrid Electric Vehicles Based on Driving Condition Recognition: A Review

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