Structural Health Monitoring of Rotating Machines in Manufacturing Processes by Vibration Methods

2014 ◽  
Vol 1036 ◽  
pp. 642-647 ◽  
Author(s):  
Rafał Burdzik ◽  
Łukasz Konieczny ◽  
Piotr Folęga
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Vibration Signal ◽  
Technical Condition ◽  
Operating Conditions ◽  
Vibration Monitoring ◽  
Manufacturing Processes ◽  
Diagnostic Systems ◽  
Structural Health ◽  
Run Up

The paper presents results of the active diagnostics experiments on influence of fatigue metal damage of the inner race of bearing and unbalance of rotating masses on vibration generated by the machine. Analysis of vibration related phenomena is a solution commonly applied in Structural Health Monitoring (SHM) systems. The application of vibroacoustics methods for technical condition monitoring has been developed in the past years in many systems of manufacturing processes. Vibroacoustic methods, based on the analysis of vibration or acoustic signals perceived as residual processes of non-invasive nature, is becoming more and more important in this respect. The scope of its application as well as the applicability of methods in numerous diagnostic systems also results from the capabilities of advanced methods of signal analysis and identification of numerous characteristics of technical condition. One of the most common operation damages are caused by rolling bearings wear. The scope of research contains tests on bearing damage and the unbalance of disc. The wear processes and unbalance are closely related to the vibration levels (arising from the mass loss of plastic deformation, and the fatigue damage). The research was conducted on special research test bench for vibration monitoring for rotating machinery. Structural health monitoring of machinery has to be conducted in different states and working conditions of the manufacturing system. Thus for simulating of different operating conditions the experiments have been conducted during run up of the machine which consist the transient states of working and during work on constant rotational speed of the power generate engine. For the identification of the symptoms of the machinery and equipments health monitoring the vibration signal have been analysed in time domain and frequency transformation as well. The performed signals are not stationary. Thus it is better to observe the signal simultaneously in time and frequency domains. For this purpose the spectrograms were determined. Spectrograms computes the short-time Fourier transform of a signal by default divided into segments. During the transformation the Hamming window and noverlap were used. For the comparison of the vibration of good and damage bearings signals registered for different frequencies have been presented in form of spectrograms and RMS distributions.

scholarly journals A Review of Recent Distributed Optical Fiber Sensors Applications for Civil Engineering Structural Health Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1818
Author(s):  
Mattia Francesco Bado ◽  
Joan R. Casas
Keyword(s):  
Structural Health Monitoring ◽  
Optical Fiber ◽  
Health Monitoring ◽  
Optical Fiber Sensors ◽  
Vibration Monitoring ◽  
Fiber Sensors ◽  
Structural Health ◽  
Comprehensive Collection ◽  
Monitoring Tools ◽  
Distributed Optical Fiber

The present work is a comprehensive collection of recently published research articles on Structural Health Monitoring (SHM) campaigns performed by means of Distributed Optical Fiber Sensors (DOFS). The latter are cutting-edge strain, temperature and vibration monitoring tools with a large potential pool, namely their minimal intrusiveness, accuracy, ease of deployment and more. Its most state-of-the-art feature, though, is the ability to perform measurements with very small spatial resolutions (as small as 0.63 mm). This review article intends to introduce, inform and advise the readers on various DOFS deployment methodologies for the assessment of the residual ability of a structure to continue serving its intended purpose. By collecting in a single place these recent efforts, advancements and findings, the authors intend to contribute to the goal of collective growth towards an efficient SHM. The current work is structured in a manner that allows for the single consultation of any specific DOFS application field, i.e., laboratory experimentation, the built environment (bridges, buildings, roads, etc.), geotechnical constructions, tunnels, pipelines and wind turbines. Beforehand, a brief section was constructed around the recent progress on the study of the strain transfer mechanisms occurring in the multi-layered sensing system inherent to any DOFS deployment (different kinds of fiber claddings, coatings and bonding adhesives). Finally, a section is also dedicated to ideas and concepts for those novel DOFS applications which may very well represent the future of SHM.

Acceleration data quality assessment for bridge structural health monitoring via statistical and deep-learning approach

2021 ◽  
Author(s):  
Huaqiang Zhong ◽  
Limin Sun ◽  
José Turmo ◽  
Ye Xia
Keyword(s):  
Structural Health Monitoring ◽  
Data Quality ◽  
Health Monitoring ◽  
Structural Damage ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Operating Conditions ◽  
Monitoring Data ◽  
Statistical Characteristics ◽  
Structural Health

<p>In recent years, the safety and comfort problems of bridges are not uncommon, and the operating conditions of in-service bridges have received widespread attention. Many large-span key bridges have installed structural health monitoring systems and collected massive amounts of data. Monitoring data is the basis of structural damage identification and performance evaluation, and it is of great significance to analyze and evaluate its quality. This paper takes the acceleration monitoring data of the main girder and arch rib of a long-span arch bridge as the research object, analyzes and summarizes the statistical characteristics of the data, summarizes 6 abnormal data conditions, and proposes a data quality evaluation method of convolutional neural network. This paper conducts frequency statistics on the acceleration vibration amplitude of the bridge in December 2018 in hours. In order to highlight the end effect of frequency statistics, the whole is amplified and used as network input for training and data quality evaluation. The results are good. It provides another new method for structural monitoring data quality evaluation and abnormal data elimination.</p>

Propagative broad learning for nonparametric modeling of ambient effects on structural health indicators

2020 ◽  
pp. 147592172091692 ◽  
Author(s):  
Sin-Chi Kuok ◽  
Ka-Veng Yuen ◽  
Stephen Roberts ◽  
Mark A Girolami
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Health Indicators ◽  
Operating Conditions ◽  
Learning Approach ◽  
Nonparametric Modeling ◽  
Structural Health ◽  
Model Free ◽  
Free Data

In this article, a novel propagative broad learning approach is proposed for nonparametric modeling of the ambient effects on structural health indicators. Structural health indicators interpret the structural health condition of the underlying dynamical system. Long-term structural health monitoring on in-service civil engineering infrastructures has demonstrated that commonly used structural health indicators, such as modal frequencies, depend on the ambient conditions. Therefore, it is crucial to detrend the ambient effects on the structural health indicators for reliable judgment on the variation of structural integrity. However, two major challenging problems are encountered. First, it is not trivial to formulate an appropriate parametric expression for the complicated relationship between the operating conditions and the structural health indicators. Second, since continuous data stream is generated during long-term structural health monitoring, it is required to handle the growing data efficiently. The proposed propagative broad learning provides an effective tool to address these problems. In particular, it is a model-free data-driven machine learning approach for nonparametric modeling of the ambient-influenced structural health indicators. Moreover, the learning network can be updated and reconfigured incrementally to adapt newly available data as well as network architecture modifications. The proposed approach is applied to develop the ambient-influenced structural health indicator model based on the measurements of 3-year full-scale continuous monitoring on a reinforced concrete building.

Optimal Sensor Configuration for Fatigue Life Prediction in Structural Applications

2019 ◽  
Author(s):  
Mohamed Khalil ◽  
Ioannis Kouroudis ◽  
Roland Wüchner ◽  
Kai-Uwe Bletzinger
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Estimation Error ◽  
Operating Conditions ◽  
Added Value ◽  
Sensor Data ◽  
Damage Prediction ◽  
Structural Health ◽  
Structural Applications ◽  
Prediction Approach

Abstract Structural health monitoring is spreading widely across engineering domains. Its added value is not restricted to observing structural behavior, but crosses over to enabling the assessment of structural integrity under varying operating conditions. Damage prognosis is one vital demand from structural health monitoring solutions. Many methods have been developed to update damage predictions based on sensor data, nonetheless the selection and positioning of sensors to alleviate the prediction errors remains a question under investigation. In this work, an optimal sensor placement method is proposed for fatigue damage prediction in structures. An optimization problem is formulated to minimize the a-posteriori damage estimation error based on a Kalman filter. The derivation of the objective function is presented, along with a discussion of algorithm-related issues. Finally, the mentioned damage prediction approach is applied to two structures to verify the adequacy of the sensor configurations proposed by the method.

scholarly journals Comparative Study of Fiber Bragg Gratings and Fiber Polarimetric Sensors for Structural Health Monitoring of Carbon Composites

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Massimo Olivero ◽  
Guido Perrone ◽  
Alberto Vallan ◽  
Daniele Tosi
Keyword(s):  
Structural Health Monitoring ◽  
Comparative Study ◽  
Health Monitoring ◽  
Smart Materials ◽  
Vibration Monitoring ◽  
Carbon Fiber Composites ◽  
Test Plate ◽  
Structural Health ◽  
Dynamic Excitation ◽  
Cheap Alternative

A comparative study is presented between Bragg grating (FBG) and polarimetric sensors (PS), two of the most promising fiber optic sensing techniques for the structural health monitoring of smart materials based on carbon fiber composites. The paper describes the realization of a test plate equipped with both types of sensors and reports the characterization under static and dynamic conditions, highlighting pros and cons of both technologies. The FBG setup achieves 1.15 ± 0.0016 pm/kg static load response and reproduces dynamic excitation with 0.1% frequency uncertainty; the PS system exhibits a sensitivity of 1.74 ± 0.001 mV/kg and reproduces dynamic excitation with 0.5% frequency uncertainty. It is shown that the PS technology is a good and cheap alternative to FBG for vibration-monitoring of small structures at high frequency.

Effects of environmental and operational variability on structural health monitoring

2006 ◽  
Vol 365 (1851) ◽  
pp. 539-560 ◽  
Author(s):  
Hoon Sohn
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Changes ◽  
Vibration Signal ◽  
Data Normalization ◽  
Operational Conditions ◽  
Structural Health ◽  
Structural Deterioration ◽  
Damage Data ◽  
Environmental Variations

Stated in its most basic form, the objective of structural health monitoring is to ascertain if damage is present or not based on measured dynamic or static characteristics of a system to be monitored. In reality, structures are subject to changing environmental and operational conditions that affect measured signals, and these ambient variations of the system can often mask subtle changes in the system's vibration signal caused by damage. Data normalization is a procedure to normalize datasets, so that signal changes caused by operational and environmental variations of the system can be separated from structural changes of interest, such as structural deterioration or degradation. This paper first reviews the effects of environmental and operational variations on real structures as reported in the literature. Then, this paper presents research progresses that have been made in the area of data normalization.

scholarly journals Techniques for Processing Experimental Data for Structural Health Monitoring of Bridges

2022 ◽  
Vol 19 (4) ◽  
pp. 22-33
Author(s):  
N. M. Kvashnin ◽  
I. S. Bondar ◽  
M. Ya. Kvashnin
Keyword(s):  
Structural Health Monitoring ◽  
Bearing Capacity ◽  
Health Monitoring ◽  
Strain State ◽  
Technical Condition ◽  
Strain Gauges ◽  
Vertical Load ◽  
Stress Strain ◽  
Artificial Structures ◽  
Structural Health

Reliability of transport artificial structures and transit of trains at sanctioned speed should be provided with the necessary and sufficient load-bearing capacity, strength, rigidity, and stability of engineering structures.The objective of this work was to substantiate the possibility of using well-known methods for controlling the stress-strain state of structures using automated systems of structural health monitoring of bridge spans.It is extremely important regarding operation of transport artificial structures designed according to the standards of the first half of the 20th century.Under these conditions, the experimental determination of the stress-strain state of bearing structures of bridges becomes the most important component of the task of a comprehensive assessment of physical wear and tear as well as of operational reliability of the structures. Monitoring the structural health and technical condition of bridges and planning of timely measures aimed at the repair, strengthening or reconstruction of spans will extend their service life and ensure safety during operation.Maximum permissible deflections of spans under a movable temporary vertical load have been revealed since to ensure smooth movement of vehicles it is necessary to control horizontal longitudinal and transverse displacements of the top of the bridge piers, as well as vertical settlements.The paper suggests methods of interpreting data measured by inclinometers and electric strain gauges, tensiometers to use them in an automated system for monitoring the structural health of railway bridges. The method of strain measurement is described in detail in the proposed options for installing resistance strain gauges on structures to measure tensile-compression stresses and longitudinal forces due to a temporary vertical load.Monitoring the technical condition of bridge structures, using the methods for measuring deflections and deformations proposed by the authors in this article, will make it possible to assess the change in bearing capacity of the structure over the entire period of operation. The study used regulations and experience of the Russian Federation and the Republic of Kazakhstan. 

Vibration-response-only statistical time series structural health monitoring methods: A comprehensive assessment via a scale jacket structure

2019 ◽  
Vol 19 (3) ◽  
pp. 736-750 ◽  
Author(s):  
Nikos A Spanos ◽  
John S Sakellariou ◽  
Spilios D Fassois
Keyword(s):  
Time Series ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Vibration Signal ◽  
Small Scale ◽  
Laser Vibrometer ◽  
Monitoring Methods ◽  
Structural Health ◽  
Statistical Time ◽  
Prominent Response

Random-vibration-based statistical time series structural health monitoring methods utilize small-scale, compact, and data-based, time series stochastic representations of the structural dynamics for damage diagnosis. In this study, a comprehensive and critical assessment of the diagnostic performance of five prominent response-only methods is presented based on incipient, ‘minor’ to ‘mild’, damages on a lab-scale wind turbine jacket structure. Statistically reliable damage detection and identification results are obtained via a ‘rotation’ procedure resulting into thousands of test cases, with the performance analysed in terms of receiver operating characteristic curves and confusion matrices. The results indicate not only challenging of the methods’ capabilities, but also the achievement of good to excellent performance for the ‘minor’ to ‘mild’ damages, respectively, with the model parameter–based method offering the best performance. In addition, the use of a vibration signal measured via a laser vibrometer leads to slightly improved detection performance over that obtained via a classical accelerometer.

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