Wavelet packet energy–based damage identification of wood utility poles using support vector machine multi-classifier and evidence theory

Yu, Yang; Dackermann, Ulrike; Liu, Haotian; Niederleithinger, Ernst

doi:10.1177/1475921718798622

Cited by 45 publications

(20 citation statements)

References 37 publications

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“…However, most state-of-the-art methods use the wavelet packet-based energy index [ 33 , 34 , 35 ] for various applications like monitoring timber connection [ 36 ], railway axles [ 37 ] or wood utility poles [ 38 ] and for fibre-reinforced laminated composites [ 39 , 40 ]. In addition, Song et al describes a damage index representing the proportion of transmission energy losses caused by damages based on continuous wavelet transform (CWT), which has been successfully applied to bridge bent-caps as an indicator of the health state [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

Monitoring the Structural Health of Glass Fibre-Reinforced Hybrid Laminates Using Novel Piezoceramic Film

Schmidt

Graf

Decker

et al. 2020

Sensors

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This work investigates a new generation structural health monitoring (SHM) system for fibre metal laminates (FML) based on an embedded thermoplastic film with compounded piezoceramics, termed piezo-active fibre metal laminate (PFML). The PFML is manufactured using near-series processes and its potential as a passive SHM system is being investigated. A commercial Polyvinylidene fluoride (PVDF) sensor film is used for comparative evaluation of the sensor signals. Furthermore, thermoset and thermoplastic-based FML are equipped with the sensor films and evaluated. For this purpose, static and dynamic three-point bending tests are carried out and the data are recorded. The data obtained from the sensors and the testing machine are compared with the type and time of damage by means of intelligent signal processing. By using a smart sensor system, further investigations are planned which the differentiation between various failure modes, e.g., delamination or fibre breakage.

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Section: Methodsmentioning

confidence: 99%

Monitoring the Structural Health of Glass Fibre-Reinforced Hybrid Laminates Using Novel Piezoceramic Film

Schmidt

Graf

Decker

et al. 2020

Sensors

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“…The WPEI defined by Equation (3) has been applied to evaluating the structural health condition in concrete structures [56] and other applications [57][58][59][60]. In this study, the total energy of received signal by S2 can be characterized through the WPEI.…”

Section: Wavelet-packet-based Energy Indexmentioning

confidence: 99%

Detecting Debonding between Steel Beam and Reinforcing CFRP Plate Using Active Sensing with Removable PZT-Based Transducers

Jiang

Deng

et al. 2019

Sensors

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Carbon fiber reinforced polymer (CFRP) plates are widely used to retrofit or reinforce steel structures, and the debonding damage between the steel structure and the CFRP plate is a typical failure in strengthening steel structures. This paper proposes a new approach to detecting debonding between a steel beam and a reinforcing CFRP plate by using removable lead zirconate titanate (PZT)-based transducers and active sensing. The removable PZT-based transducers are used to implement the active sensing approach, in which one transducer, as an actuator, is used to generate stress wave, and another transducer, as a sensor, is used to detect the stress wave that propagates across the bonding between the steel beam and the reinforcing CFRP plate. The bonding condition significantly influences the received sensor signal, and a wavelet-packet-based energy index (WPEI) is used to quantify the energy of the received signal to evaluate the severity of debonding between the steel beam and the reinforcing CFRP plate. To validate the proposed approach, experimental studies were performed, and two removable PZT-based transducers were designed and fabricated to detect the debonding between a steel beam and the reinforcing CRFP plate. The experimental results demonstrate the feasibility of the proposed method in detecting the debonding between a steel beam and the reinforcing CFRP plate using removable PZT-based transducers.

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“…PCA is remarkable in information compression and elimination of data correlation [18]. Yang et al [19] applied WPT combined with PCA to extract the feature to identify the health condition of wood utility poles. Using WPT to decompose the vibration signal, Sudhir et al [20] developed bearing damage index (BDI) from the decomposed signal to select the useful signal from the originally recorded signal and PCA was employed to select significant features as the input of dendogram support vector machine (DSVM) classifier to identify the faults of induction motor bearing.…”

Section: Introductionmentioning

confidence: 99%

Insulation Fault Diagnosis of Disconnecting Switches Based on Wavelet Packet Transform and PCA-IPSO-SVM of Electric Fields

Zhang

Kong

2020

IEEE Access

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Previous studies have shown that switching operations of gas insulated substations (GIS) can generate transient radiation fields outside the enclosure, namely switching transient electric fields (STEF). The waveform features of STEF can reflect the functioning performance of the switch. To monitor online the working states of disconnecting switches (DS), in this paper, we built an experimental platform to simulate their typical faulty types. Then, under different faulty status, a non-invasive three-dimensional (3D) electric field measurement system was applied to obtain STEF produced by DS. It is difficult for conventional methods to establish an accurate fault-diagnosis model, so we presented a novel method to identify the condition of DS. This innovative approach is based on feature extraction and machine learning and combined signal analysis to classify different defect types of DS. Measured STEF signals were analyzed by the wavelet packet transform(WPT) method in the time-frequency domain, which was transformed to the multi-dimensional feature matrix. The principal component analysis (PCA) algorithm was employed to reduce the dimensionality of the obtained feature matrix, which was also compared to other feature extraction algorithms. In addition, a support vector machine (SVM) with an improved particle swarm optimization (IPSO) algorithm was designed to achieve a PCA-IPSO-SVM model which can be used for signal recognition. The proposed IPSO technique can improve the convergence performance of the PSO through the dynamic adjustment of inertia weight and learning factors. Results show that the proposed fault diagnosis method based on WPT and PCA-IPSO-SVM can effectively identify the insulation faulty signals in STEF. INDEX TERMS Gas insulated substation (GIS); disconnecting switch (DS); switching transient electric field (STEF); wavelet packet transform(WPT); principal component analysis (PCA); support vector machine (SV M); improved particle swarm optimization (IPSO)

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Wavelet packet energy–based damage identification of wood utility poles using support vector machine multi-classifier and evidence theory

Cited by 45 publications

References 37 publications

Monitoring the Structural Health of Glass Fibre-Reinforced Hybrid Laminates Using Novel Piezoceramic Film

Monitoring the Structural Health of Glass Fibre-Reinforced Hybrid Laminates Using Novel Piezoceramic Film

Detecting Debonding between Steel Beam and Reinforcing CFRP Plate Using Active Sensing with Removable PZT-Based Transducers

Insulation Fault Diagnosis of Disconnecting Switches Based on Wavelet Packet Transform and PCA-IPSO-SVM of Electric Fields

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