Estimation Of Remaining Fatigue Life Research Articles

Motion magnification-based nonlinear ultrasonic signal enhancement and its application to remaining fatigue life estimation of a steel padeye

In this study, a nonlinear ultrasonic signal enhancement method based on phase-based motion magnification was proposed. First, a two-dimensional (2D) video was constructed from a one-dimensional time-domain ultrasonic signal with an automatically selected pixel scale. The constructed 2D video was spatially decomposed into multiple sub-bands with different spatial scales. The subtle motions of the nonlinear responses of interest were enhanced by phase denoising and magnification in each sub-band. Then, the magnified 2D video was reconstructed by collapsing all the amplified sub-bands. Finally, a magnified ultrasonic signal was extracted from the magnified 2D video for further nonlinear ultrasonic analysis. The proposed method was validated with a group of synthetic signals at different noise levels, and applied in the estimation of the remaining fatigue life of a steel padeye with improved accuracy.

Integration of symbolic regression and domain knowledge for interpretable modeling of remaining fatigue life under multistep loading

This research work aims to explore the integration of data-driven symbolic regression (SR) and domain knowledge to model the remaining fatigue life under multistep loading. To this end, six classical semiempirical damage models are analyzed to distill reliable domain knowledge as the restrictions on the structures of SR formulas. Meanwhile, a total of 194 experimental results involving fifteen materials and structures as well as three kinds of loading spectrums are collected for data support. As a major contribution of this research work, a novel model without including fitting parameters is successfully discovered for remaining fatigue life estimation under two-step loading. This model can be interpreted in the framework of conventional damage models, and shows good extendibility to multistep loading through proper definitions of the damage indicator and the damage transition. Extensive model evaluations demonstrate that the discovered model is better than five existing damage models in terms of predictive accuracy and application scope, showing great applicability for remaining life estimation under multistep loading.

Health Monitoring of Automotive Suspensions: A LSTM Network Approach

In the automotive industry, one of the critical issues is to develop a health monitoring system for condition assessment and remaining fatigue life estimation of key load-bearing components including automotive suspension. However, considering the difficulty to obtain expert knowledge and nonlinear dynamics in large-scale sensory data, health monitoring of automotive suspension is a challenging work. With the development of deep learning based sequence models in recent years, a long short-term memory (LSTM) network has been proved to capture long-term dependencies in time-series prediction without additional expert knowledge. In this paper, a novel health monitoring system based on a LSTM network is proposed to estimate the remaining fatigue life of automotive suspension. Specifically, first durability tests under various driving cycles are implemented to obtain sequential sensory data provided by common sensors on a test car. Then, a LSTM-based load identification method is designed to predict dynamic stress histories based on the available sensory data. Finally, the damages and remaining fatigue life of the suspensions are estimated by each time step. The experimental results prove that our model can achieve a better performance compared with other representative models.

Design and Field Validation of a Low Power Wireless Sensor Node for Structural Health Monitoring

Smart monitoring systems are currently gaining more attention and are being employed in several technological areas. These devices are particularly appreciated in the structural field, where the collected data are used with purposes of real time alarm generation and remaining fatigue life estimation. Furthermore, monitoring systems allow one to take advantage of predictive maintenance logics that are nowadays essential tools for mechanical and civil structures. In this context, a smart wireless node has been designed and developed. The sensor node main tasks are to carry out accelerometric measurements, to process data on-board, and to send wirelessly synthetic information. A deep analysis of the design stage is carried out, both in terms of hardware and software development. A key role is played by energy harvesting integrated in the device, which represents a peculiar feature and it is thanks to this solution and to the adoption of low power components that the node is essentially autonomous from an energy point of view. Some prototypes have been assembled and tested in a laboratory in order to check the design features. Finally, a field test on a real structure under extreme weather conditions has been performed in order to assess the accuracy and reliability of the sensors.

Structural monitoring and remaining fatigue life estimation of typical welded crack details in the Manhattan Bridge

Manhattan Bridge is a suspension structure that connects Manhattan to Downtown Brooklyn. This bridge is over 110 years old and subjected to daily repeated axles of transit trains and other vehicular traffic. Visual inspections of the bridge revealed manifestation of many cracks in the welded details due to the out-of-plane forces at the connections between transit and floor beams. The research reported herein, pertains to the fatigue life evaluation and reliability assessment of typical cracked welded details. The bridge was instrumented with a real-time structural health monitoring system based on FBG sensors for remote monitoring of the cracked connections. Both deterministic and probabilistic approaches were employed for prediction of the remaining fatigue life of typical cracked details. For the same fatigue reliability index of 2.3, the deterministic and probability based methods yielded remaining fatigue life of 369 and 120 years, respectively, for the typical welded connections details of the bridge.

Utility analysis for SHM durations and service life extension of welds on steel bridge deck

Optimization of the duration of Structural Health Monitoring (SHM) campaigns is rarely performed. This article provides a utility-based solution to posteriorly determine: i) optimal monitoring durations and ii) the extension of the service life of the welds on a steel bridge deck. The approach is illustrated with a case study focusing on remaining fatigue life estimation of the welds on the orthotropic steel deck of the Great Belt Bridge, in Denmark. The identification of the optimal monitoring duration and the decision about extending the service life of the welds are modelled by maximizing the expected benefits and minimizing the structural risks. The results are a parametric analysis, mainly on the effect of the target probability, benefit, cost of failure, cost of rehabilitation, cost of monitoring and discount rate on the posterior utilities of monitoring strategies and the choice of service life considering the risk variability and the costs and benefits models. The results show that the decision on short-term monitoring, i.e., 1 week every six months, is overall the most valued SHM strategy. In addition, it is found that the target probability is the most sensitive parameter affecting the optimal SHM durations and service life extension of the welds.

A Fatigue Life Prediction Method for the Drive System of Wind Turbine Using Internet of Things

The wind turbine drive system is one of the key components in converting wind energy into electrical energy. The life prediction of drive system is very important for the maintenance of wind turbine. With increasing capacity, the wind turbine system has become more complicated. Consequently, for the life prediction of drive system, it is necessary to consider the problems of multi-information fusion of big data, quantification of time-varying dynamic loads, and analysis of multiple-damage coupling. In order to solve the above challenges, the fatigue life analysis and evaluation method considering the interaction of coupled multiple damages are proposed in this study. The hierarchical Bayesian theory with fault physics technology is introduced to deal with the uncertainty of wind turbine drive system. Then, a time-varying performance analysis model is established based on the multiple-damage coupling competition failure mechanism. Moreover, the Internet of Things (IoT) technology is introduced and combined with the proposed model. Through the data collection by IoT, the time-stress curve of drive system can be obtained. A case study about the remaining fatigue life estimation of drive system is utilized to illustrate the effectiveness of the proposed method.

Online fatigue crack prognosis using nonlinear ultrasonic modulation

This article presents an online remaining fatigue life estimation (prognosis) technique using nonlinear ultrasonic modulation. When two ultrasonic inputs at two distinct frequencies are introduced to a structure with a fatigue crack, modulation components at the sum and difference of input frequencies are generated owing to crack opening and closing. First, a fatigue index is defined for prognosis and extracted from nonlinear ultrasonic modulation measurement. Subsequently, it is shown that the fatigue index is expressed as a simple power function of loading cycles applied to a target structure based on the Nazarov–Sutin and Paris–Erdogan theories. Then, the real-time remaining fatigue life estimation method is proposed by fitting the power function to online data obtained from the target structure. The performance of the proposed prognosis technique is evaluated using either notched or welded aluminum (6061-T6) plate specimens.

Data-driven fatigue crack quantification and prognosis using nonlinear ultrasonic modulation

Abstract In this study, an online monitoring technique for continuous fatigue crack quantification and remaining fatigue life estimation is developed for plate-like structures using nonlinear ultrasonic modulation and an artificial neural network (ANN). First, multiple aluminum plates of different thicknesses are subjected to cyclic loading tests at a constant amplitude, and the ultrasonic responses are obtained from the piezoelectric transducers attached to each specimen. Second, an ANN is constructed by defining (1) the specimen thickness; the elapsed fatigue cycles; and two features extracted from the ultrasonic responses, namely, the cumulative increase and decrease in the nonlinear beta parameter, as inputs and (2) the crack length and remaining fatigue life as outputs. Then, the architecture and learning parameter of the ANN are optimized using the data obtained from the specimen tests. Finally, the performance of the trained ANN is examined using the blind test data obtained from additional specimens. The results of the blind tests indicate that the proposed technique can estimate the crack length and remaining fatigue life with a maximum error of 2 mm and 3 k cycles, respectively, for the tested aluminum plates. The uniqueness of this technique lies in (1) the fatigue crack quantification and remaining fatigue life estimation using nonlinear ultrasonic modulation and (2) the data-driven crack quantification and prognosis using an ANN for online monitoring.

Structural health monitoring of cylindrical bodies under impulsive hydrodynamic loading by distributed FBG strain measurements

Various mechanical, ocean, aerospace and civil engineering problems involve solid bodies impacting the water surface and often result in complex coupled dynamics, characterized by impulsive loading conditions, high amplitude vibrations and large local deformations. Monitoring in such problems for purposes such as remaining fatigue life estimation and real time damage detection is a technical and scientific challenge of primary concern in this context. Open issues include the need for developing distributed sensing systems able to operate at very high acquisition frequencies, to be utilized to study rapidly varying strain fields, with high resolution and very low noise, while scientific challenges mostly relate to the definition of appropriate signal processing and modeling tools enabling the extraction of useful information from distributed sensing signals.Building on previous work by some of the authors, we propose an enhanced method for real time deformed shape reconstruction using distributed FBG strain measurements in curved bodies subjected to impulsive loading and we establish a new framework for applying this method for structural health monitoring purposes, as the main focus of the work. Experiments are carried out on a cylinder impacting the water at various speeds, proving improved performance in displacement reconstruction of the enhanced method compared to its previous version. A numerical study is then carried out considering the same physical problem with different delamination damages affecting the body. The potential for detecting, localizing and quantifying this damage using the reconstruction algorithm is thoroughly investigated. Overall, the results presented in the paper show the potential of distributed FBG strain measurements for real time structural health monitoring of curved bodies under impulsive hydrodynamic loading, defining damage sensitive features in terms of strain or displacement reconstruction errors at selected locations along the structure.

Remaining fatigue life estimation of corroded bridge members

ABSTRACTThis paper presents a method to estimate remaining fatigue lives of railway bridge members subjected to time‐dependent corrosion. The method addresses effects of material loss due to general (uniform) corrosion and fatigue strength degradation of material due to corrosive environment. The method mainly consists of stress history, which is obtained by considering the effect of time‐dependent loss of material, full‐range S–N curve, which represents the corrosive environment, and sequential law, which takes the loading sequence effect more precisely in to account than Miner's rule. Initially, nonlinear behaviour of material loss over time (i.e. time‐dependent growth of corrosion wastage) is discussed, and hence, necessary formulae to calculate time‐dependent cross‐sectional properties are comprehensively presented. Then finite element analysis‐based procedure is clearly proposed to predict stress histories of corroded members. A technique is introduced to obtain the full‐range S–N curve for the corroded structural detail. The concept of sequential law is summarized with the algorithm, and then the proposed method is applied to predict the remaining fatigue lives of the corroded members of a railway bridge. The predicted remaining lives were compared with the previous method‐based estimations, and comparisons reveal the range of 16–47% reduction of fatigue lives of critical members when time‐dependent corrosion is taken into account. Also, the results reveal that the corroded members of smaller cross‐sectional area are most vulnerable for fatigue damage. Finally, significance of the proposed method is confirmed.

Nondestructive Estimation of Remaining Fatigue Life: Thermography Technique

A technique for the estimation of remaining fatigue life (RFL) of metallic specimens when subjected to cyclic load is presented. The procedure includes evaluation of thermal response of the metal and its correlation with the fatigue life. Results are presented for a series of fully reversed fatigue tests with Stainless Steel 304L at room temperature. It is demonstrated that the slope of temperature rise as a function of time is a good candidate for estimation of RFL. Once the temperature slope is measured, the remaining useful life of the specimen can be effectively estimated.

Structural Appraisal-based Different Approach to Estimate the Remaining Fatigue Life of Railway Bridges

The current fatigue life estimation methods of railway bridges are generally based on combinations of measured strain histories, Miner’s rule, and railway code provided fatigue curve. Even though the past measured strain histories are available for major bridges, most of the old bridges do not have past strain measurements. Furthermore, in case of existing railway bridges where the detailed loading history is known, Miner’s rule might provide incorrect results because of its omission of load sequence effect. These reasons hinder the usage of current methods to estimate the realistic remaining fatigue life of most of the existing railway bridges. Therefore, a new method is presented in this article to estimate remaining fatigue life of riveted railway bridges. The method mainly consists of predicted stress histories, recently developed sequential law and fully known Wöhler curve. Here, it is essential to use the fully known Wöhler curve as the related fatigue curve. Hence, the technique, which utilizes transfer of the partially known Wöhler curve to fully known curve, is also discussed in this article. Further, this method describes a reasonably accurate procedure to obtain the past stress histories from present day measured strains. Initially, the article describes the proposed method for remaining fatigue life estimation. Then the sequential law and associated fully known S-N curve determination procedure are verified by comparing theoretical fatigue life, damage behavior with real fatigue life and damage behavior of few materials. Finally, the remaining fatigue life of an existing railway bridge is estimated. Hence, validity and merits of the proposed method is confirmed by comparing the results with previous method-based fatigue lives.

The ANN Method of Obtaining Fatigue Load Spectrum of Overhead Traveling Crane

For the realization of remaining fatigue life estimation, prevention of catastrophic accidents and safety of the cranes in service, it is necessary to obtain the actual load spectrum of overhead traveling crane. Due to the limitation of condition in field experiment, it is difficult to implement a large number of actual Load Spectrum experiments. So, the present paper will focus on overhead traveling cranes for general purpose and conduct data investigation. In a certain period，the numbers of work cycles corresponding to different lifting loads for different rated lifting capacity are collected. Based on Levenberg-Marquardt Back Propagation(LMBP) Artificial Neural Network(ANN), the equivalent load spectrum, which is equivalent to the actual load spectrum, is acquired. The example demonstrates: It is feasible to establish the equivalent load spectrum for some types of crane using ANN. Meanwhile, the equivalent load spectrums of the typical cranes in this type could be obtained and predicted through the trained network. It has provided the reliable data for the reliability analysis and fatigue life estimation of the crane. In the approach to establish the crane load spectrum, an exploring and initiating study work have been done.

An Accurate Life Estimation Method for Existing Railway Bridges

The paper proposes an accurate methodology to estimate remaining fatigue lives of riveted railway bridges. The proposed method mainly consists of measured stress histories, recently developed sequential law and fully known Wohler curve. Here, it is essential to use the fully known Wohler curve as the related fatigue curve. Therefore the technique, which utilizes to transfer the partially known Wohler curve to fully known curve, is also discussed under this paper. Since most of the bridges do not have past strain measurements, this method describes reasonably accurate procedure to obtain the past stress histories from present day measured stress histograms. Initially paper describes the proposed method for remaining fatigue life estimation. Secondly remaining fatigue life of an existing railway bridge is estimated by performing a case study. Case study describes the details of the considered railway bridge and the appraisals related to condition evaluation, FE analysis, material testing, experimental static and dynamic load testing. Then the remaining fatigue lives of each critical components of the bridge are obtained. Hence validity and merits of proposed method is confirmed by comparing the results with previous method-based fatigue lives.

Application of new damage indicator-based sequential law for remaining fatigue life estimation of railway bridges

Miner’s rule is generally accepted as the fatigue criteria for life estimation of railway bridges. Similarly, it has always been acknowledged as a simplification that is easy to use in design where detailed loading history is unknown. But in the case of existing railway bridges where the detailed loading history is known, Miner’s rule might provide incorrect results because of its omission of load sequence effect. Recently, a new damage indicator-based sequential law has been proposed to capture the load sequence effect more precisely. However, application of this sequential law to estimate the remaining fatigue lives of existing railway bridges has not been properly studied. Therefore, the major objective of this paper is to estimate the remaining fatigue life of a railway bridge using the sequential law and hence introduces a new approach to estimate remaining fatigue life of riveted railway bridges. This approach is specially based on combination of real stress histories, sequential law and fully known Wöhler curve. The practice of sequential law to obtain the remaining fatigue lives due to both uniaxial and multiaxial stresses are described by estimating remaining life of a considered railway bridge. Further, the obtained fatigue lives are compared with Miner’s estimations. Hence it could be concluded that it is advisable to apply the sequential law for assessment of remaining fatigue lives of riveted railway bridges, where the detailed stress histories are known.