Exploration of the possibility of acoustic emission technique in detection and diagnosis of bubble formation and collapse in valves

The application of acoustic emission (AE) technique in detection and monitoring of bubble formation and collapse in valves are presented in this review. The generation of AE signals and the basic compositions of AE detection system are briefly explained. The applications of AE technique in valves are focused on condition monitoring and detection bubble formation (bubble cavitation), and leakage of water through valves. All results prove that the AE technique works well for detection and diagnosis of failures during valves

State of the art in structural health monitoring of offshore and marine structures

This paper deals with state of the art in structural health monitoring (SHM) methods in offshore and marine structures. Most SHM methods have been developed for onshore infrastructures. Few studies are available to implement SHM technologies in offshore and marine structures. This paper aims to fill this gap and highlight the challenges in implementing SHM methods in offshore and marine structures. The present work categorises the available techniques for establishing SHM models in oil rigs, offshore wind turbine structures, subsea systems, vessels, pipelines and so on. Additionally, the capabilities of proposed ideas in recent publications are classified into three main categories: model-based methods, vibration-based methods and digital twin methods. Recently developed novel signal processing and machine learning algorithms are reviewed and their abilities are discussed. Developed methods in vision-based and population-based approaches are also presented and discussed. The aim of this paper is to provide guidelines for selecting and establishing SHM in offshore and marine structures.publishedVersio

Fault Management in DC Microgrids:A Review of Challenges, Countermeasures, and Future Research Trends

The significant benefits of DC microgrids have instigated extensive efforts to be an alternative network as compared to conventional AC power networks. Although their deployment is ever-growing, multiple challenges still occurred for the protection of DC microgrids to efficiently design, control, and operate the system for the islanded mode and grid-tied mode. Therefore, there are extensive research activities underway to tackle these issues. The challenge arises from the sudden exponential increase in DC fault current, which must be extinguished in the absence of the naturally occurring zero crossings, potentially leading to sustained arcs. This paper presents cut-age and state-of-the-art issues concerning the fault management of DC microgrids. It provides an account of research in areas related to fault management of DC microgrids, including fault detection, location, identification, isolation, and reconfiguration. In each area, a comprehensive review has been carried out to identify the fault management of DC microgrids. Finally, future trends and challenges regarding fault management in DC-microgrids are also discussed

A Wavelet Packet Based Sifting Process and Its Application for Structural Health Monitoring

In this work an innovative wavelet packet based sifting process for signal decomposition has been developed and its application for health monitoring of time-varying structures is presented. With the proposed sifting process, a signal can be decomposed into its mono-frequency components by examining the energy content in the wavelet packet components of a signal, and imposing certain decomposition criteria. The method is illustrated for simulation data of a linear three degree-of-freedom spring-mass-damper system and the results are compared with those obtained using the empirical mode decomposition (EMD) method. Both methods provide good approximations, as compared with the exact solution for modal responses from a conventional modal analysis. Incorporated with the classical Hilbert transform, the proposed sifting process may be effectively used for structural health monitoring by monitoring instantaneous modal parameters of the structure for both, cases of abrupt structural stiffness loss and progressive stiffness degradation. The effectiveness of this method for practical application is evaluated by applying the methodology for experimental data and the results obtained matched with the field observations. The proposed methodology has shown better results in a comparison study which is done to evaluate performance of the proposed approach with other available SHM techniques, namely EMD technique and Continuous Wavelet Transform (CWT) method, for cases characterized by different damage scenarios and noise conditions

Hierarchical k-nearest neighbours classification and binary differential evolution for fault diagnostics of automotive bearings operating under variable conditions

International audienc

Wind Power Forecasting Methods Based on Deep Learning: A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics

Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review

From MDPI via Jisc Publications RouterHistory: accepted 2021-09-14, pub-electronic 2021-09-20Publication status: PublishedModern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms

Identification of time-varying cable forces based on parameter optimization variational mode decomposition.

Accumulated fatigue damage is one of the main causes of damage and destruction of actual bridge structures. for cable bridges, the cable is the key force component. the cumulative fatigue damage of the cable seriously threatens the safety of the bridge structure. the traditional cable force test based on the vibration method can only identify the average cable force of a bridge cable over a period of time. however, due to vehicle load and environmental factors, the cable force of the bridge cable is time-varying. time-varying cable force is the main cause of fatigue damage, and it is also the basis for the safety assessment of cable limit state and the evaluation of cumulative fatigue damage. to this end, this paper studies the identification method of bridge cable time-varying cable force based on variational mode decomposition. the main research contents of this article include: based on the time-frequency analysis method of variational mode decomposition, a new method for identifying time-varying cable forces is proposed. the time-frequency analysis method of variational modal decomposition is a new development method in the field of current signal processing. its principle is to obtain a limited number of imf and extract the instantaneous frequency of the time-varying system by performing hilbert transform on the obtained imf. firstly, according to the time-frequency analysis method of variational modal decomposition, the time-varying modal frequency is identified from the measured cable acceleration. then, the bridge cable is simplified into an ideal tension string, and the cable force is identified based on the relationship between the cable force and frequency established by the classic string vibration theory. the frequency-doubling relationship of the vibration of the cable is used to reduce the optimization variables of the method, improve the calculation efficiency, reduce the influence of noise on the different instantaneous frequencies of the cable, and improve the accuracy of frequency identification of time-varying modal. finally, the time-varying modal frequency of the identified cable is substituted into the cable force formula to obtain the time-varying cable force of the cable. in the practical application of variational modal decomposition, the choice of penalty factors and the number of components has a great influence on the final signal decomposition results. in order to automatically determine the best parameter combination, the particle swarm optimization algorithm is used to search for these two influencing parameters in parallel . simulation signal and engineering signal processing results show that the proposed method can achieve identification of time-varying frequency. for the end effect of the instantaneous frequency curve, the signal extension method is used. reduce the error of the instantaneous frequency at the end point. design and build a tilting cable vibration test platform, and compare the test results with the calculation results to further verify the correctness of the method in this paper. the results show that the cable force error is about ± 5.0%

In-situ health monitoring for wind turbine blade using acoustic wireless sensor networks at low sampling rates

PhD ThesisThe development of in-situ structural health monitoring (SHM) techniques represents a challenge for offshore wind turbines (OWTs) in order to reduce the cost of the operation and maintenance (O&M) of safety-critical components and systems. This thesis propos- es an in-situ wireless SHM system based on acoustic emission (AE) techniques. The proposed wireless system of AE sensor networks is not without its own challenges amongst which are requirements of high sampling rates, limitations in the communication bandwidth, memory space, and power resources. This work is part of the HEMOW- FP7 Project, ‘The Health Monitoring of Offshore Wind Farms’. The present study investigates solutions relevant to the abovementioned challenges. Two related topics have been considered: to implement a novel in-situ wireless SHM technique for wind turbine blades (WTBs); and to develop an appropriate signal pro- cessing algorithm to detect, localise, and classify different AE events. The major contri- butions of this study can be summarised as follows: 1) investigating the possibility of employing low sampling rates lower than the Nyquist rate in the data acquisition opera- tion and content-based feature (envelope and time-frequency data analysis) for data analysis; 2) proposing techniques to overcome drawbacks associated with lowering sampling rates, such as information loss and low spatial resolution; 3) showing that the time-frequency domain is an effective domain for analysing the aliased signals, and an envelope-based wavelet transform cross-correlation algorithm, developed in the course of this study, can enhance the estimation accuracy of wireless acoustic source localisa- tion; 4) investigating the implementation of a novel in-situ wireless SHM technique with field deployment on the WTB structure, and developing a constraint model and approaches for localisation of AE sources and environmental monitoring respectively. Finally, the system has been experimentally evaluated with the consideration of the lo- calisation and classification of different AE events as well as changes of environmental conditions. The study concludes that the in-situ wireless SHM platform developed in the course of this research represents a promising technique for reliable SHM for OWTBs in which solutions for major challenges, e.g., employing low sampling rates lower than the Nyquist rate in the acquisition operation and resource constraints of WSNs in terms of communication bandwidth and memory space are presente