Shaft Crack Detection Using Statistical Analysis

2009 ◽  
Author(s):  
Zhuang Li ◽  
Lei Jin ◽  
Ning Zhang ◽  
Yang Zhou
Keyword(s):  
Statistical Analysis ◽  
Damage Detection ◽  
Crack Detection ◽  
Damage Identification ◽  
Vibration Data ◽  
Rotating Systems ◽  
Detection And Diagnosis ◽  
The Relationship ◽  
Cracked Shafts ◽  
Damage Types

Cracks and voids are common defects in rotating systems and are a precursor to fatigue-induced failure. The application of statistical analysis, as a tool for damage identification and health monitoring in rotating machinery, is investigated. Experimental vibration data were collected for a set of health and cracked shafts. Formal statistical models have been proposed to describe the relationship between the vibration signals and the existence of damage. Damage detection and diagnosis are implemented based on statistical estimation and hypothesis testing. Such a statistical model provides a screening technique to detect other damage types. As a result, the proposed methods can improve the power of damage detection.

scholarly journals Automatic Concrete Damage Recognition Using Multi-Level Attention Convolutional Neural Network

Materials ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 5549
Author(s):  
Hyun Kyu Shin ◽  
Yong Han Ahn ◽  
Sang Hyo Lee ◽  
Ha Young Kim
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Convolutional Neural Network ◽  
Damage Identification ◽  
Concrete Surface ◽  
Superior Performance ◽  
Detection Accuracy ◽  
Damage Recognition ◽  
Proposed Model ◽  
Damage Types

There has been an increase in the deterioration of buildings and infrastructure in dense urban regions, and several defects in the structures are being exposed. To ensure the effective diagnosis of building conditions, vision-based automatic damage recognition techniques have been developed. However, conventional image processing techniques have some limitations in real-world situations owing to their manual feature extraction approach. To overcome these limitations, a convolutional neural network-based image recognition technique was adopted in this study, and a convolution-based concrete multi-damage recognition neural network (CMDnet) was developed. The image datasets consisted of 1981 types of concrete surface damages, including surface cracks, rebar exposure and delamination, as well as intact. Furthermore, it was experimentally demonstrated that the proposed model could accurately classify the damage types. The results obtained in this study reveal that the proposed model can recognize the different damage types from digital images of the surfaces of concrete structures. The trained CMDnet demonstrated a damage-detection accuracy of 98.9%. Moreover, the proposed model could be applied in automatic damage detection networks to achieve superior performance with regard to concrete surface damage detection and recognition, as well as accelerating efficient damage identification during the diagnosis of deteriorating structures used in civil engineering applications.

scholarly journals Damage identification using inverse methods

2006 ◽  
Vol 365 (1851) ◽  
pp. 393-410 ◽  
Author(s):  
Michael I Friswell
Keyword(s):  
Inverse Problems ◽  
Damage Detection ◽  
Environmental Effects ◽  
Health Monitoring ◽  
Damage Identification ◽  
Inverse Methods ◽  
Damage Localization ◽  
Uncertain Parameters ◽  
Vibration Data ◽  
Modelling Error

This paper gives an overview of the use of inverse methods in damage detection and location, using measured vibration data. Inverse problems require the use of a model and the identification of uncertain parameters of this model. Damage is often local in nature and although the effect of the loss of stiffness may require only a small number of parameters, the lack of knowledge of the location means that a large number of candidate parameters must be included. This paper discusses a number of problems that exist with this approach to health monitoring, including modelling error, environmental effects, damage localization and regularization.

Detection of Leukemia Using Image Processing

2018 ◽  
Vol 8 (4) ◽  
pp. 61
Author(s):  
A. Sivasangari ◽  
G. Sasikumar
Keyword(s):  
Image Processing ◽  
Statistical Analysis ◽  
Early Detection ◽  
Visual Inspection ◽  
Causes Of Death ◽  
Cure Rate ◽  
Blood Disorders ◽  
Detection And Diagnosis ◽  
The Moment ◽  
Early Detection And Diagnosis

Leukemia   disease   is one   of    the   leading   causes   of death   among   human. Its  cure  rate and  prognosis   depends   mainly   on  the  early  detection   and  diagnosis  of   the  disease. At  the  moment, identification  of  blood  disorders  is  through   visual  inspection  of  microscopic  images  by  examining  changes  like  texture, geometry, colour  and   statistical  analysis  of  images . This  project  aims  to  preliminary  of  developing  a  detection  of  leukemia  types  using   microscopic  blood  sample using MATLAB. Images  are  used  as  they  are  cheap  and  do  not  expensive  for testing  and  lab  equipment.

scholarly journals BMI-1 Expression Heterogeneity in Endometriosis-Related and Non-Endometriotic Ovarian Carcinoma

2021 ◽  
Vol 22 (11) ◽  
pp. 6082
Author(s):  
Ludmila Lozneanu ◽  
Raluca Anca Balan ◽  
Ioana Păvăleanu ◽  
Simona Eliza Giuşcă ◽  
Irina-Draga Căruntu ◽  
...  
Keyword(s):  
Stem Cells ◽  
Statistical Analysis ◽  
Ovarian Carcinoma ◽  
Clinicopathological Characteristics ◽  
Cell Phenotype ◽  
Epithelial Tumor ◽  
Normal Organ ◽  
Ovarian Carcinogenesis ◽  
Cellular Signaling Pathway ◽  
The Relationship

BMI-1 is a key component of stem cells, which are essential for normal organ development and cell phenotype maintenance. BMI-1 expression is deregulated in cancer, resulting in the alteration of chromatin and gene transcription repression. The cellular signaling pathway that governs BMI-1 action in the ovarian carcinogenesis sequences is incompletely deciphered. In this study, we set out to analyze the immunohistochemical (IHC) BMI-1 expression in two different groups: endometriosis-related ovarian carcinoma (EOC) and non-endometriotic ovarian carcinoma (NEOC), aiming to identify the differences in its tissue profile. Methods: BMI-1 IHC expression has been individually quantified in epithelial and in stromal components by using adapted scores systems. Statistical analysis was performed to analyze the relationship between BMI-1 epithelial and stromal profile in each group and between groups and its correlation with classical clinicopathological characteristics. Results: BMI-1 expression in epithelial tumor cells was mostly low or negative in the EOC group, and predominantly positive in the NEOC group. Moreover, the stromal BMI-1 expression was variable in the EOC group, whereas in the NEOC group, stromal BMI-1 expression was mainly strong. We noted statistically significant differences between the epithelial and stromal BMI-1 profiles in each group and between the two ovarian carcinoma (OC) groups. Conclusions: Our study provides solid evidence for a different BMI-1 expression in EOC and NEOC, corresponding to the differences in their etiopathogeny. The reported differences in the BMI-1 expression of EOC and NEOC need to be further validated in a larger and homogenous cohort of study.

Sparse damage detection via the elastic net method using modal data

2021 ◽  
pp. 147592172110219
Author(s):  
Rongrong Hou ◽  
Xiaoyou Wang ◽  
Yong Xia
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Damage Identification ◽  
Measurement Data ◽  
Elastic Net ◽  
Sensitivity Matrix ◽  
Regularization Technique ◽  
Modal Data ◽  
Regularization Techniques ◽  
Net Method

The l1 regularization technique has been developed for damage detection by utilizing the sparsity feature of structural damage. However, the sensitivity matrix in the damage identification exhibits a strong correlation structure, which does not suffice the independency criteria of the l1 regularization technique. This study employs the elastic net method to solve the problem by combining the l1 and l2 regularization techniques. Moreover, the proposed method enables the grouped structural damage being identified simultaneously, whereas the l1 regularization cannot. A numerical cantilever beam and an experimental three-story frame are utilized to demonstrate the effectiveness of the proposed method. The results showed that the proposed method is able to accurately locate and quantify the single and multiple damages, even when the number of measurement data is much less than the number of elements. In particular, the present elastic net technique can detect the grouped damaged elements accurately, whilst the l1 regularization method cannot.

scholarly journals Noni fruit’s water spot appearance on the second day of harvest: a trade-off between resistance and energy

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Tian Wu ◽  
Danyan Hu ◽  
Qingfen Wang
Keyword(s):  
Statistical Analysis ◽  
Molecular Level ◽  
Morinda Citrifolia ◽  
Transcriptome Data ◽  
Trade Off ◽  
Kegg Pathways ◽  
Available Energy ◽  
Climacteric Fruit ◽  
The Relationship ◽  
Insight Into

Abstract Background Noni (Morinda citrifolia Linn.) is a tropical tree that bears climacteric fruit. Previous observations and research have shown that the second day (2 d) after harvest is the most important demarcation point when the fruit has the same appearance as the freshly picked fruit (0 d); however, they are beginning to become water spot appearance. We performed a conjoint analysis of metabolome and transcriptome data for noni fruit of 0 d and 2 d to reveal what happened to the fruit at the molecular level. Genes and metabolites were annotated to KEGG pathways and the co-annotated KEGG pathways were used as a statistical analysis. Results We found 25 pathways that were significantly altered at both metabolic and transcriptional levels, including a total of 285 differentially expressed genes (DEGs) and 11 differential metabolites through an integrative analysis of transcriptomics and metabolomics. The energy metabolism and pathways originating from phenylalanine were disturbed the most. The upregulated resistance metabolites and genes implied the increase of resistance and energy consumption in the postharvest noni fruit. Most genes involved in glycolysis were downregulated, further limiting the available energy. This lack of energy led noni fruit to water spot appearance, a prelude to softening. The metabolites and genes related to the resistance and energy interacted and restricted each other to keep noni fruit seemingly hard within two days after harvest, but actually the softening was already unstoppable. Conclusions This study provides a new insight into the relationship between the metabolites and genes of noni fruit, as well as a foundation for further clarification of the post-ripening mechanism in noni fruit.

scholarly journals Development of a Novel Damage Detection Framework for Truss Railway Bridges Using Operational Acceleration and Strain Response

Vibration ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 422-445
Author(s):  
Md Riasat Azim ◽  
Mustafa Gül
Keyword(s):  
Damage Detection ◽  
Damage Identification ◽  
Numerical Study ◽  
Damage Index ◽  
Strain Analysis ◽  
Time History ◽  
Principal Component ◽  
Element Model ◽  
Railway Bridges ◽  
Truss Bridge

Railway bridges are an integral part of any railway communication network. As more and more railway bridges are showing signs of deterioration due to various natural and artificial causes, it is becoming increasingly imperative to develop effective health monitoring strategies specifically tailored to railway bridges. This paper presents a new damage detection framework for element level damage identification, for railway truss bridges, that combines the analysis of acceleration and strain responses. For this research, operational acceleration and strain time-history responses are obtained in response to the passage of trains. The acceleration response is analyzed through a sensor-clustering-based time-series analysis method and damage features are investigated in terms of structural nodes from the truss bridge. The strain data is analyzed through principal component analysis and provides information on damage from instrumented truss elements. A new damage index is developed by formulating a strategy to combine the damage features obtained individually from both acceleration and strain analysis. The proposed method is validated through a numerical study by utilizing a finite element model of a railway truss bridge. It is shown that while both methods individually can provide information on damage location, and severity, the new framework helps to provide substantially improved damage localization and can overcome the limitations of individual analysis.

Structural Damage Assessment Using Output-Only Measurement: Localization and Quantification

2013 ◽  
Author(s):  
Chin-Hsiung Loh ◽  
Min-Hsuan Tseng ◽  
Shu-Hsien Chao
Keyword(s):  
Damage Detection ◽  
Damage Assessment ◽  
Structural Damage ◽  
Damage Identification ◽  
Model Updating ◽  
Null Space ◽  
Singular Spectrum Analysis ◽  
Damage Index ◽  
Computation Efficiency ◽  
Output Only

One of the important issues to conduct the damage detection of a structure using vibration-based damage detection (VBDD) is not only to detect the damage but also to locate and quantify the damage. In this paper a systematic way of damage assessment, including identification of damage location and damage quantification, is proposed by using output-only measurement. Four level of damage identification algorithms are proposed. First, to identify the damage occurrence, null-space and subspace damage index are used. The eigenvalue difference ratio is also discussed for detecting the damage. Second, to locate the damage, the change of mode shape slope ratio and the prediction error from response using singular spectrum analysis are used. Finally, to quantify the damage the RSSI-COV algorithm is used to identify the change of dynamic characteristics together with the model updating technique, the loss of stiffness can be identified. Experimental data collected from the bridge foundation scouring in hydraulic lab was used to demonstrate the applicability of the proposed methods. The computation efficiency of each method is also discussed so as to accommodate the online damage detection.

Civil engineers’ personal values/demographics linkage in project team building

2017 ◽  
Vol 24 (6) ◽  
pp. 1337-1349 ◽  
Author(s):  
Atilla Damci ◽  
David Arditi ◽  
Gul Polat
Keyword(s):  
Statistical Analysis ◽  
Work Experience ◽  
Personal Values ◽  
Team Building ◽  
Project Teams ◽  
Team Leaders ◽  
Content Type ◽  
First Time ◽  
The Relationship ◽  
Civil Engineers

Purpose The purpose of this paper is to explore the relationship between civil engineers’ demographics (e.g. age, marital status, education, work experience) and their personal values. The objective was to predict civil engineers’ personal values based on their demographics. Design/methodology/approach A questionnaire survey was administered to civil engineers to collect data on their demographics and their personal values. Statistical analysis was performed to verify whether a significant statistical relationship exists between civil engineers’ demographics and their personal values. Findings The most important and the least important personal values were identified for civil engineers. Statistical analysis indicated that civil engineers’ values do vary based on their demographics. Research limitations/implications The results of this study cannot be generalized, because individuals’ personal values and demographics are, by definition, local. Location and culture may affect the personal values of civil engineers. Practical implications Team leaders normally have access to information about the demographics of the engineers they employ; based on the results of this study, they should be able to predict their personal values, and to make more informed decisions when appointing them to particular positions on project teams. Originality/value The research presented in this paper, establishes for the first time, that a linkage exists between civil engineers’ personal values and their demographics, and makes it easier for team leaders to make assignment decisions.

INSTAGRAM SEBAGAI MEDIA EDUKASI PENINGKATAN PENGETAHUAN KESEHATAN GIGI DAN MULUT

2021 ◽  
Vol 8 (2) ◽  
pp. 51-56
Author(s):  
Ratna Sari Dyah ◽  
Lies Elina
Keyword(s):  
Comparative Analysis ◽  
Statistical Analysis ◽  
Dental Health ◽  
Sampling Technique ◽  
Online Media ◽  
Hygiene Behavior ◽  
Increasing Knowledge ◽  
Quota Sampling ◽  
The Relationship ◽  
Oral Hygiene Behavior

Knowledge of how to maintain proper dental health will greatly affect the incidence of dental caries, brushing and rinsing teeth - gargling is one of the behaviors to maintain oral hygiene. behavior based on correct knowledge will last longer than behavior that is not based on knowledge, an effort to increase knowledge is through health education. Online media is one of the educational media to increase knowledge of dental and oral health. The type of research in this study is a comparative comparative analysis or "causal-comparative". Quota sampling technique sampling, the research location was conducted in SMA N 3 Bandar Lampung ..as many as 100 people. The research variable was the online media instagram in increasing knowledge of caries. Statistical analysis used the T-test to see the relationship between Instagram in increasing knowledge. The results showed instagram can that there was a role for online media education in increasing knowledge of cavities.

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