An Acoustic Emission Technique for Monitoring the Liquefied Natural Gas Cargo Tank

Increase in the market of supersized LNG (Liquefied Natural Gas) vessel, with doubled walled cargo tanks, has led to concerns regarding their safe operations. If both the primary and secondary wall of the cargo tank fails simultaneously, the hull of the vessel can be exposed to the LNG’s. This has the potential to cause brittle failure of the hull structure. This research presents a new Acoustic Emission (AE) technique that can be implemented to monitor the structural condition of the primary wall in the LNG cargo tank. The presented technique is able to provide information regarding critical damage so that appropriate maintenance can be carried out to avoid catastrophic failure. Acoustic Emission (AE) is a passive Non-Destructive Testing (NDT) technique, employed to identify critical damage in structures before failure can occur. Currently, AE monitoring is carried out by calculating the features of the waveform received by the AE sensor. User defined settings (i.e. timing and threshold) in the AE data acquisition system significantly affects many traditional AE features such as count, energy, centroid frequency, rise-time and duration. In AE monitoring, AE features are strongly related to the damage sources. Therefore, AE features, calculated due to inaccurate user defined acquisition settings can result in inaccurately classified damage sources. The new AE technique presented in this study is based on an AE feature of the waveform, which is independent of some user defined parameter (i.e. timing and threshold) used in the AE data acquisition system, unlike many traditional AE features. The presented AE feature is referred to as AE entropy in this research and is a measure of randomness in the waveform calculated using quadratic Renyi’s entropy. The effectiveness of AE entropy is evaluated by comparing it and traditional AE features under ideal conditions for a range of varying acquisition settings. Unlike the traditional feature, the AE entropy showed no variance with the acquisition settings and was effective in identifying different waveform shapes. The AE entropy was validated through fatigue and tensile tests on coupon specimens of austenitic stainless steel (material of the primary wall). The result suggested that AE entropy is effective in identifying the critical damages in austenitic stainless steel, irrespective of the data acquisition settings. Since AE entropy reduces the human involvement with the data acquisition system and can identify damages, it has the potential to be implemented in the commercial AE data acquisition system

Parametric probabilistic approach for cumulative fatigue damage using double linear damage rule considering limited data

International audienceThis work proposes a parametric probabilistic approach to model damage accumulation using the double linear damage rule (DLDR) considering the existence of limited experimental fatigue data. A probabilistic version of DLDR is developed in which the joint distribution of the knee-point coordinates is obtained as a function of the joint distribution of the DLDR model input parameters. Considering information extracted from experiments containing a limited number of data points, an uncertainty quantification framework based on the Maximum Entropy Principle and Monte Carlo simulations is proposed to determine the distribution of fatigue life. The proposed approach is validated using fatigue life experiments available in the literature

Nonlinear Dynamical Systems for Theory And Research In Ergonomics

Nonlinear dynamical systems (NDS) theory offers new constructs, methods and explanations for phenomena that have in turn produced new paradigms of thinking within several disciplines of the behavioural sciences. This article explores the recent developments of NDS as a paradigm in ergonomics. The exposition includes its basic axioms, the primary constructs from elementary dynamics and so-called complexity theory, an overview of its methods, and growing areas of application within ergonomics. The applications considered here include: psychophysics, iconic displays, control theory, cognitive workload and fatigue, occupational accidents, resilience of systems, team coordination and synchronisation in systems. Although these applications make use of different subsets of NDS constructs, several of them share the general principles of the complex adaptive system

Energy Dissipation and Entropy Generation During the Fatigue Degradation: Application to Health Monitoring of Composites

In this dissertation, an experimental approach for characterizing energy dissipation and degradation evolution in a woven Epoxy/Glass (G10/FR4) laminate subjected to fully reversed bending fatigue test is presented. Infrared thermography and acoustic emission are utilized to characterize the degradation progression. The results show similar evolutionary response indicating the presence of three degradation stages. The effect of the surface cooling on the fatigue life of the laminates is investigated both experimentally and analytically. The results show that the life of the laminate is highly dependent on the temperature and that surface cooling can significantly increase the fatigue life of the laminate. The signatures of acoustic emission (AE) response emanating from laminates are studied. The distribution of the cumulative AE amplitude is described by a power law. Examination of the evolution of the probability density function (PDF) of the AE energy (counts) reveals two scaling zones wherein the transition from the low energy (count) to high energy (count) regime is identified. The low-energy phase represents very low damage state of the laminate characterized by a power law. The AE energy release and counts follow the statistics and power laws that do not depend on the operational conditions. A fatigue damage detection method for the laminates based on the cumulative information entropy is reported. The cumulative entropy demonstrates a persistent trend of nonlinear damage evolution typically observed in the experimental measures of the damage in composite materials. In this dissertation, a continuum formulation for irreversible energy dissipation that accounts for generated acoustic emissions during the loading of the materials is also developed. The evolution of the dissipative energy for AL6061 specimens is experimentally measured as the material is degraded. A statistically similar behavior is observed in different forms of the dissipated energy as the material degrade. Finally, a damage detection method for detection of wear in thrust ball bearings coated with molybdenum disulphide (MoS2) is presented. It employs an energy feature obtained from time-frequency representation of the vibration signal. Extensive experimental studies are conducted to verify the efficacy of the proposed method for fault diagnosis of MoS2 coating

Evaluation of Reliability-based Fatigue Strain Data Analysis for an Automobile Suspension Under Various Road Condition

This work aimed to analyse fatigue-based reliability for automobile suspension on the basis of the strain load signal from an automobile under operating conditions. Fatigue life was used to ensure the aging of the component, and it was suitable for use for longer than the standard age given. The damage behaviour patterns for each retained edited signal from 100% to 85% were used to predict the fatigue durability of the suspension with a sampling frequency of 500 Hz for various road conditions. The extended global statistics were computed to determine the behaviour of the signal. Accelerated durability analysis was used to remove the low-amplitude cycles, which contributed minimally toward the total damage, by considering the effects of mean stresses. The reliability assessment, hazard rate function and mean time-to-failure (MTTF) based on the retention signal were predicted through fatigue strain data analysis. Changes were observed from a range of below 15% and above 60% of the length of the actual original signals due to the low amplitude. Extended global statistics showed scale parameter of 75 and 94 with an MTTF of 1.25×103 and 1.27×103 cycles. The retention signal loads provide an accurate signal editing technique for predicting fatigue life with good reliability characteristic understanding for the suspension part

Condition Monitoring of Slow Speed Rotating Machinery Using Acoustic Emission Technology

Slow speed rotating machines are the mainstay of several industrial applications worldwide. They can be found in paper and steel mills, rotating biological contractors, wind turbines etc. Operational experience of such machinery has not only revealed the early design problems but has also presented opportunities for further significant improvements in the technology and economics of the machines. Slow speed rotating machinery maintenance, mostly related to bearings, shafts and gearbox problems, represents the cause of extended outages. Rotating machinery components such as gearboxes, shafts and bearings degrade slowly with operating time. Such a slow degradation process can be identified if a robust on-line monitoring and predictive maintenance technology is used to detect impending problems and allow repairs to be scheduled. To keep machines functioning at optimal levels, failure detection of such vital components is important as any mechanical degradation or wear, if is not impeded in time, will often progress to more serious damage affecting the operational performance of the machine. This requires far more costly repairs than simply replacing a part. Over the last few years there have been many developments in the use of Acoustic Emission (AE) technology and its analysis for monitoring the condition of rotating machinery whilst in operation, particularly on slow speed rotating machinery. Unlike conventional technologies such as thermography, oil analysis, strain measurements and vibration, AE has been introduced due to its increased sensitivity in detecting the earliest stages of loss of mechanical integrity. This programme of research involves laboratory tests for monitoring slow speed rotating machinery components (shafts and bearings) using AE technology. To implement this objective, two test rigs have been designed to assess the capability of AE as an effective tool for detection of incipient defects within low speed machine components (e.g. shafts and bearings). The focus of the experimental work will be on the initiation and growth of natural defects. Further, this research work investigates the source characterizations of AE signals associated with such bearings whilst in operation. It is also hoped that at the end of this research program, a reliable on-line monitoring scheme used for slow speed rotating machinery components can be developed

Adaptive Multiscale Weighted Permutation Entropy for Rolling Bearing Fault Diagnosis

© 2020 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.Bearing vibration signals contain non-linear and non-stationary features due to instantaneous variations in the operation of rotating machinery. It is important to characterize and analyze the complexity change of the bearing vibration signals so that bearing health conditions can be accurately identified. Entropy measures are non-linear indicators that are applicable to the time series complexity analysis for machine fault diagnosis. In this paper, an improved entropy measure, termed Adaptive Multiscale Weighted Permutation Entropy (AMWPE), is proposed. Then, a new rolling bearing fault diagnosis method is developed based on the AMWPE and multi-class SVM. For comparison, experimental bearing data are analyzed using the AMWPE, compared with the conventional entropy measures, where a multi-class SVM is adopted for fault type classification. Moreover, the robustness of different entropy measures is further studied for the analysis of noisy signals with various Signal-to-Noise Ratios (SNRs). The experimental results have demonstrated the effectiveness of the proposed method in fault diagnosis of rolling bearing under different fault types, severity degrees, and SNR levels.Peer reviewedFinal Accepted Versio