Detection and Localization of Multiple Damages through Entropy in Information Theory

Ceravolo, Rosario; Civera, Marco; Lenticchia, Erica; Miraglia, Gaetano; Surace, Cecilia

doi:10.3390/app11135773

Cited by 27 publications

(14 citation statements)

References 46 publications

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“…For instance, in the two case studies mentioned earlier [10,11], the Shannon spectral entropy (SSE) was proven to be the most appropriate definition for masonry structures. Instead, for applications where mainly metallic components are utilised, the authors of [12,13] suggested the use of the Wiener entropy (WE) due to its higher sensibility to damage (in non-homogeneous materials, such as concrete or masonry, this higher sensibility to local inhomogeneities may cause false alarms).…”

Section: Motivations For Entropy-based Shmmentioning

confidence: 99%

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Instantaneous Spectral Entropy: An Application for the Online Monitoring of Multi-Storey Frame Structures

Civera

Surace

2022

Buildings

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Damage assessment techniques based on entropy measurements have been recently proposed for the structural health monitoring of civil structures and infrastructures. A quasi-real-time approach, based on the use of instantaneous spectral entropy (ISE) over an uninterrupted stream of data, is discussed here. The methodology is proposed for the detection of sudden damage-related structural changes (more specifically, linear stiffness reductions and nonlinear breathing cracks). The method operates by framing the continuous stream of vibration signals and comparing the single frames to a known baseline. The approach is also suitable for nonstationary signals originating from nonlinearly behaving structures. The procedure is validated on an experimental benchmark: a laboratory-scaled model of a three-storey single-span frame metallic structure. Three different definitions of entropy and six candidate time–frequency/time-scale transforms have been tested to find the optimal settings.

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Section: Motivations For Entropy-based Shmmentioning

confidence: 99%

“…The WE, being more sensitive to both structural and non-structural changes, was contrariwise suggested for metallic structures, with more uniform construction materials and fewer nonuniformities [12]. This allows us to take advantage of its major sensitivity with fewer false positives [12,13].…”

Section: Wiener Entropymentioning

confidence: 99%

Instantaneous Spectral Entropy: An Application for the Online Monitoring of Multi-Storey Frame Structures

Civera

Surace

2022

Buildings

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“…Spectral entropy has been applied for rotary machinery as well; e.g., for the performance degradation assessment of rolling element bearing by Ref [16]. However, for such application where mainly metallic components are utilised, the Wiener Entropy (WE) should be preferred due to its higher sensibility to damage, which is apter for more homogeneous construction materials [17,18]. Therefore, different entropy definitions may apply more or less conveniently to different construction materials.…”

Section: Entropy Measurements and Their Application For Shmmentioning

confidence: 99%

“…The WE, being more sensitive to both structural and nonstructural changes, was contrariwise suggested for metallic structures, with more uniform construction materials and fewer nonuniformities [17]. This allows taking advantage of its major sensitivity with fewer false positives [17,18].…”

Section: Wiener Entropymentioning

confidence: 99%

The Instantaneous Spectral Entropy for Real-time, Online Structural Health Monitoring.

Civera

Surace

2022

J. Phys.: Conf. Ser.

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Entropy measurements have been recently proposed for the damage assessment of civil structures and mechanical systems. Here, a quasi-real-time approach, based on Instantaneous Spectral Entropy (ISE), is proposed for the detection of sudden stiffness reduction, breathing cracks, and other kinds of structural changes. The method, validated on an experimental benchmark, is suitable for nonstationary signals originating from nonlinear structures as well.

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“…Due to the influence of complex operating conditions and environment, the collected vibration signals of the rolling bearing are usually non-stationary and non-linear [20]. Many non-linear dynamic methods, such as sample entropy [21], permutation entropy [22], fuzzy entropy [23], Rényi entropy [24], Wiener entropy [25], Instantantaneous Spectral Entropy [26], dispersion entropy [27], are proposed, which can reflect the non-linear properties of vibration signal and characterize equipment health status. Sample Entropy is slow in calculating long time series, poor in real-time performance, and prone to a sudden change in similarity measurement [28].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Diagnosis of Rolling Element Bearing Based on Refined Composite Multiscale Reverse Dispersion Entropy and Random Forest

Liu

Yang²,

et al. 2022

Sensors

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Rolling bearings are the vital components of large electromechanical equipment, thus it is of great significance to develop intelligent fault diagnoses for them to improve equipment operation reliability. In this paper, a fault diagnosis method based on refined composite multiscale reverse dispersion entropy (RCMRDE) and random forest is developed. Firstly, rolling bearing vibration signals are adaptively decomposed by variational mode decomposition (VMD), and then the RCMRDE values of 25 scales are calculated for original signal and each decomposed component as the initial feature set. Secondly, based on the joint mutual information maximization (JMIM) algorithm, the top 15 sensitive features are selected as a new feature set and feed into random forest model to identify bearing health status. Finally, to verify the effectiveness and superiority of the presented method, actual data acquisition and analysis are performed on the bearing fault diagnosis experimental platform. These results indicate that the presented method can precisely diagnose bearing fault types and damage degree, and the average identification accuracy rate is 97.33%. Compared with the refine composite multiscale dispersion entropy (RCMDE) and multiscale dispersion entropy (MDE), the fault diagnosis accuracy is improved by 2.67% and 8.67%, respectively. Furthermore, compared with the RCMRDE method without VMD decomposition, the fault diagnosis accuracy is improved by 3.67%. Research results prove that a better feature extraction technique is proposed, which can effectively overcome the deficiency of existing entropy and significantly enhance the ability of fault identification.

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Detection and Localization of Multiple Damages through Entropy in Information Theory

Cited by 27 publications

References 46 publications

Instantaneous Spectral Entropy: An Application for the Online Monitoring of Multi-Storey Frame Structures

Instantaneous Spectral Entropy: An Application for the Online Monitoring of Multi-Storey Frame Structures

The Instantaneous Spectral Entropy for Real-time, Online Structural Health Monitoring.

Intelligent Diagnosis of Rolling Element Bearing Based on Refined Composite Multiscale Reverse Dispersion Entropy and Random Forest

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