Online Health Monitoring Research Articles

An Improved Time-Domain Damage Detection Method for Railway Bridges Subjected to Unknown Moving Loads

There is considerable interest in structural health monitoring (SHM) and damage detection of bridges and considerable progress has been made in this field in recent years. However, several challenges such as sensitivity to low levels of damage and identification without the knowledge of the moving load remain and need to be precisely investigated by researchers. The current work addresses such challenges and proposes an efficient response sensitivity-based model updating procedure in time-domain for damage identification of railway bridges subjected to unknown moving loads. The bridge is modelled as an Euler-Bernoulli beam and the train is modelled as a set of sprung masses passing over the beam. Structural damage is considered as a reduction in the modulus of elasticity of the elements. Sensitivity analysis and Tikhonov regularization methods are adopted and used to solve the inverse problem of the model updating. To verify the efficiency of the model, two numerical models with multiple damage scenarios subjected to unknown moving loads are analyzed. In addition, the efficiency of the proposed method in the presence of measurement noise is also verified. Numerical results reveal that the proposed model-updating procedure simultaneously identifies structural damages as well as the unknown moving loads with an acceptable accuracy. The effect of critical parameters such as mass and speed of the moving vehicle on the accuracy of identification results is investigated as well. Based on the findings of this research, the proposed method can be adopted and applied to online and long-term health monitoring of real bridge structures.

Reconstruction of full-field complex deformed shapes of thin-walled special-section beam structures based on in situ strain measurement

This study proposed a method capable of reconstructing complex deformations of thin-walled special-section beam structures subjected to highly coupled loading cases, in terms of the combination of tension/compression, biaxial bending, and warping torsion. The complex beam deformation was decoupled, depending on axial strain measurement strategy on beam surface, and leads to reconstructed full-field displacements (deformed shapes) as the linear superposition of deformations subject to individual loading types. Full-filed strain/stress distributions can then be derived based on the reconstructed displacements. Particular efforts were focused on reconstructing beam deformation subject to warping torsion, where both rotations angles and warping displacements across the beam cross-section and along the beam length were identified precisely. As a proof-of-concept validation, the effectiveness of the method was examined using finite element analysis, where the deformed shape of a thin-walled hat-section beam under the coupling between uniaxial bending and warping torsion was reconstructed., Experiments were conducted subsequently to reconstruct deformation of an aluminum hat-section beam using distributed optical fiber sensors for the measurement of axial strains on the beam surface. The reconstructed full-field deformed shapes of the beam were compared with the three-dimensional displacement signals captured using a non-contact digital image correlation system. The effectiveness of the proposed methodology for complex deformation reconstruction is possible to be extended to a variety of thin-walled beam-type structures which are typical in civil and aerospace engineering, showing potential contributions in fields such as on-line structural health monitoring and active structural control.

Online jointly estimation of hysteretic structures using the combination of central difference Kalman filter and Robbins–Monro technique

Rapid assessment of structural safety and performance right after the occurrence of significant earthquake shaking is crucial for building owners and decision-makers to make informed risk management decisions. Hence, it is vital to develop online and pseudo-online health monitoring methods to quantify the health of the building right after significant earthquake shaking. Many Bayesian inference–based methods have been developed in the past which allow the users to estimate the unknown states and parameters. However, one of the most challenging part of the Bayesian inference–based methods is the determination of the parameter noise covariance matrix. It is especially difficult when the number of unknown states and parameters is large. In this study, an effective online joint estimation method for nonlinear hysteretic structures, with consideration of degradation and pinching phenomena, is proposed. Simultaneous estimation of states and parameters is conducted using the combination of a central difference Kalman filter as an effective estimator and the Robbins–Monro stochastic approximation technique as the parameters noise covariance matrix regulator. The proposed algorithm is implemented on three shear buildings with 36, 54, and 90 unknown states and parameters. To verify the performance of the system identification method, robust simulations with synthetic measurement noises and modeling errors were generated using the Monte Carlo random simulation method. The result shows the proposed method can be used to estimate the unknown parameters and states of highly nonlinear systems efficiently and effectively.

A Time-Frequency Based Approach for Acoustic Emission Assessment of Sliding Wear

The acoustic emission method is one of few contemporary non-destructive testing techniques enabling continuous on-line health monitoring and control of tribological systems. However, the existence of multiple “pseudo”-acoustic emission (AE) and noise sources during friction, and their random occurrence poses serious challenges for researchers and practitioners when extracting “useful” information from the upcoming AE signal. These challenges and numerous uncertainties in signal classification prevent the unequivocal interpretation of results and hinder wider uptake of the AE technique despite its apparent advantages. Currently, the signal recording and processing technologies are booming, and new applications are born on this support. Specific tribology applications, therefore, call for developing new and tuning existing approaches to the online AE monitoring and analysis. In the present work, we critically analyze, compare and summarize the results of the application of several filtering techniques and AE signal classifiers in model tribological sliding friction systems allowing for the simulation of predominant wear mechanisms. Several effective schemes of AE data processing were identified through extensive comparative studies. Guidelines were provided for practical application, including the online monitoring and control of the systems with friction, characterizing the severity and timing of damage, on-line evaluation of wear as sliding contact tests and instrumented acceleration of tribological testing and cost reduction.

In-situ health monitoring of IGBT modules of an on-line medium-voltage inverter system using for industrial Internet of Things

Requirements of the Internet of things for the network includes the ability to monitor the equipment and devices. Nowadays, the reliability of a power electronics converter has raised concerns of both academia and industry. In particular, power semiconductor devices are continuously exposed to excessive stress while being designed with high power handling capability and are considered as the most fragile component in power converters suffering from a high failure rate. Aiming to find an effective monitoring method which is also helpful for the Internet of Things and improve the reliability of a three-level neutral-point-clamped power inverter, an in-situ health monitoring method is proposed by harnessing the inverter operational characteristics and degradation sensitive electrical parameters to address the IGBT wire bonding faults. The zero voltage state provides an inherent redundant feature that allows for a power switch to be diagnosed during its normal operation in a neutral-point-clamped power inverter. The proposed prognostic approach obtains both the wire bonding failure features and junction temperature from the terminals of an IGBT module, which is regarded as non-invasive on-line health monitoring. The system performance can be affected by the designated testing point and testing window, which is discussed and experimentally validated. The proposed technique allows unhealthy wire bonding in IGBT modules online monitoring during the operational period of the inverter. And the proposed in-situ health monitoring of IGBT modules can be used for the industrial Internet of things.

Fault diagnosis of high-speed train suspension systems using multiscale permutation entropy and linear local tangent space alignment

Online health monitoring of railway vehicle suspension systems is of critical importance to guarantee train running safety. The currently reported works on vehicle suspension health monitoring mainly adopt model-based approaches. However, detailed parameters of the vehicle suspension systems, usually, are complicated to be acquired. Moreover, an accurate model cannot be easily obtained due to the nonlinearities of vehicle components and the complexity of suspension systems. Considering the limitations of the model-based approaches, a data-driven method, which combines multiscale permutation entropy and linear local tangent space alignment (MPE-LLTSA), is proposed to diagnose the faults of vehicle suspension systems. To demonstrate the effectiveness and advantages of this method, an MBS model of the China CRH3 train is built to generate the bogie frame’s lateral acceleration containing various secondary suspension faults, and the simulated signals are then introduced to evaluate the MPE-LLTSA method. The evaluation results show that the proposed data-driven approach can accurately identify different types of suspension faults. Finally, the MPE-LLTSA method is further validated using the tracking data of the CRH3 train running on a high-speed railway line in China, and the test results show that the proposed method has the potential to be applied to the field of railway engineering.

Using orthotropic viscoelastic representative elements for C1 -continuous zigzag dynamic response assessment of sandwich FG circular plates with unevenly damaged adhesive layers

In the present analysis, dynamic responses of sandwich plates with uniformly or nonuniformly damaged adhesive layers are compared with those of the perfectly bonded plates. In this regard, a direction-dependent (orthotropic) Kelvin–Voigt viscoelastic representative element whose coefficients vary in the radial direction is employed to model the irregular/damaged adhesive layer. It is the first time that the effects of the nonuniform stiffness/viscoelasticity distribution of the adhesive layer on the dynamic behaviors are evaluated, especially for circular plates. The core or face sheets may be made from homogenous or heterogeneous materials. To extract accurate results, a C1 -continuous zigzag plate theory with interlaminar transverse stress continuity is employed. The resulting governing equations are extracted based on the principle of minimum total potential energy and solved by a Kantorovich-type power series solution in conjunction with the fourth-order Runge-Kutta numerical time integration scheme. Results reveal that the influence of the uniformly and unevenly damaged layer on the dynamic responses of the damaged sandwich plates is especially remarkable when rigid layers are utilized. Moreover, online health monitoring based on the deviation of the dynamic amplitude with respect to a predefined target is more efficient than using the indirect changes in the natural frequencies and phase shifts.

An intelligent diagnostic and prognostic framework for large-scale rotating machinery in the presence of scarce failure data

In this work, a novel diagnostic and prognostic framework is proposed to detect faults and predict remaining service life of large-scale rotating machinery in the presence of scarce failure data. In the proposed framework, a canonical variate residuals–based diagnostic method is developed to facilitate remaining service life prediction by continuously implementing detection of the prediction start time. A novel two-step prognostic feature exploring approach that involves fault identification, feature extraction, feature selection and multi-feature fusion is put forward. Most existing prognostic methods lack a fault-identification module to automatically identify the fault root-cause variables required in the subsequent prognostic analysis and decision-making process. The proposed prognostic feature exploring method overcomes this challenge by introducing a canonical variate residuals–based fault-identification method. With this method, the most representative degradation features are extracted from only the fault root-cause variables, thereby facilitating machinery prognostics by ensuring accurate estimates. Its effectiveness is demonstrated for slow involving faults in two case studies of an operational industrial centrifugal pump. Moreover, an enhanced grey model approach is developed for remaining useful life prediction. In particular, the empirical Bayesian algorithm is employed to improve the traditional grey forecasting model in terms of quantifying the uncertainty of remaining service life in a probabilistic form and improving its prediction accuracy. To demonstrate the superiority of empirical Bayesian–grey model, existing prognostic methods such as grey model, particle filter–grey model and empirical Bayesian–exponential regression are also utilized to realize machinery remaining service life prediction, and the results are compared with that of the proposed method. The achieved predictive accuracy shows that the proposed approach outperforms its counterparts and is highly applicable in fault prognostics of industrial rotating machinery. The use of in-service data in a practical scenario shows that the proposed prognostic approach is a promising tool for online health monitoring.

Adapting Approximate Entropy as a Health Indicator of Rotating Machinery for Estimation of Remaining Useful Life

Despite significant attention to online health monitoring and prognostics of bearings, many common health indicators are not sensitive to early stages of degradation. This research investigates the use of approximate entropy (ApEn), previously developed for fault diagnostics, as a health indicator for prognostics. ApEn quantifies the regularity of a signal; as bearings degrade, the frequency content of vibration signals changes and affects the ApEn as the vibration becomes more chaotic. Early results suggest ApEn supports earlier degradation detection and more predictable progression from fault to failure. This research focuses on optimizing parameters of the ApEn calculation to provide guidance across a variety of bearing types, sizes, and geometries in both steady-state and transient operation.

A Novel On-Line Substation Instrument Transformer Health Monitoring System Using Synchrophasor Data

Substation asset health has always been one of the utmost concerns for utilities. The substation secondary assets such as current transformers, potential transformers, and coupling capacitor voltage transformers play vital roles for system situational awareness and grid operations. This paper proposes a novel on-line substation instrument transformer health monitoring system (SITHMS), which merely uses time-synchronized and high-resolution phasor measurement unit (PMU) data. The SITHMS can provide early warning and alert utility personnel to diagnose the equipment malfunction and take proactive actions for asset management to prevent equipment failure. The core component of the SITHMS is a model-based three-phase substation-level linear state estimator, which features a built-in largest normalized residual (LNR) module that can detect anomalies in PMU data by using substation model and configuration, so as to probe if there is malfunction or potential failure of these secondary equipment. In addition, we present the system's implementation architecture and testing environment setup. Case studies with the PSCAD simulated data and field PMU data from actual substations at American Electric Power (AEP) demonstrate the effectiveness of the proposed system.

In-Line Nanostructuring of Glass Fibres Using Different Carbon Allotropes for Structural Health Monitoring Application

By the in-line nanostructuring of glass fibres (GF) during the glass fibre melt spinning process, the authors achieve an electro-mechanical-response-sensor. The glass fibre interphase was functionalized with different highly electrically conductive carbon allotropes such as carbon nanotubes, graphene nanoplatelets, or conductive carbon black. On-line structural health monitoring is demonstrated in continuous glass fibre-reinforced polypropylene composites during a static or dynamic three-point bending test. The different carbon fillers exhibit qualitative differences in their signal quality and sensitivity due to the differences in the aspect ratio of the nanoparticles, the film homogeneity, and the associated electrically conductive network density in the interphase. The occurrence of irreversible signal changes during dynamic loading may be attributed to filler reorientation processes caused by polymer creeping or to the destruction of the electrically conductive paths due to the presence of cracks in the glass fibre interphase. Further, the authors found that sensor embedding hardly influences the tensile properties of continuous GF reinforced polypropylene (PP) composite.

Fast Fuel Variation and Identification of SOFC System Changes Using Online Health Monitoring Tools and Fault Diagnosis

The greatest challenges of the solid oxide fuel cell (SOFC) technology are its reliability and durability, both of them considering a variety of possible fuels. To address those issues, understanding of the ongoing processes in SOFCs is of crucial importance. The ability to monitor electrochemical processes online and to identify fast alternating operating conditions offers a possibility to determine degradation-inducing processes at their preliminary stage. Thus, their early identification would provide timely counteractions and inhibit irreversible SOFC degradation. To guarantee the safe SOFC operation, the present study focuses on: (1) investigation of SOFC behavior during feeding with different fuels, (2) development and application of non-conventional tools for online-monitoring, and (3) identification of failure modes at the early stage.

Eye-tracking introduction considerations in vestibular telerehabilitation in Latvia

The terms of telemedicine, telerehabilitation, and e-care have come into our everyday lives as the necessity to make medical assistance and care more effective. These terms are not buzzwords. Indeed, new technology, innovative solutions, offered by programming tools, modern sensor equipment, wearable devices and bio-signal data sets, provide an ability to ensure necessary remote support to a person with special needs anytime and anywhere. This assistance may include person’s online health monitoring, telerehabilitation and health improvement measures, mobile training, rapid alert button initiation, advisory support, and a set of many other measures supporting patient’s care. This paper displays achievements in creation of vestibular telerehabilitation tools and systems, particularly in Latvia, and discuss further necessary developments, including eye-tracking instruments, which would improve existing telerehabilitation systems.

Restoration of defocused ferrograph images using a large kernel convolutional neural network

Wear debris analysis is used for machine condition monitoring because it is capable of identifying wear related faults in machinery components. In particular, wear mechanisms can be explored based on debris shape and texture features. As current wear monitoring practices prefer rapid data interpretation, on-line ferrograph is favored where debris images are collected and processed on-site with an imaging apparatus. However, such images are often blurred due to lubricant contamination and limited optical field depth. This drawback makes the subsequent investigation of wear debris features a challenging task. To address this issue, a restoration method is developed to reduce the influence of out-of-focus blurring in on-line ferrograph images. Firstly, in order to model the out-of-focus blurring process, a degradation model is constructed with a convolutional neural network. Different from general deep learning structures, larger convolutional kernels are embedded in the network to more comprehensively characterize the blurring process. Next, training samples of different types of debris are collected in the laboratory. The neural network structure then is optimized to minimize the difference between restored images and their corresponding ground truths. The final restoration is conducted based on the trained network via a pixel-wise regression process. To evaluate the performance of the developed method, experiments are carried out with debris images collected from a gear-box test rig. Results have demonstrated that: (1) the proposed restoration strategy allows robust extractions of debris features in greater detail; (2) the developed method is more computationally efficient than traditional deblurring techniques, which enables it to be applicable in on-line machine health monitoring.

On particle filter improvements for on-line crack growth prognosis with guided wave monitoring

Accurate prognosis of fatigue crack growth is of great importance to ensure structural integrity, which is a challenging task due to various uncertainties affecting crack growth. To deal with this problem, the particle filter (PF) based prognostics that incorporates on-line structural health monitoring (SHM) becomes a new trend. However, most existing studies adopt the basic PF algorithm, which needs improvements to meet the requirement for on-line prognosis. It refers to the choice of the importance density and the resampling strategy, as well as the definition of the measurement equation that correlates SHM data to crack states. Till now, no literature addresses this topic in-depth. Aiming at on-line crack growth prognosis, this paper combines four improved PFs with the guided wave based SHM. The study is carried out under two cases respectively, which involve whether or not the measurement equation is accurately trained based on fatigue test data of a kind of aircraft attachment lug. Not only prognostic accuracy and consistency, but also effects of the particle number on the performance and computational cost are analyzed. The result shows advantages and disadvantages of each improved PF for on-line crack growth prognosis, giving instructions to choose appropriate PFs for different application scenarios.

Time-domain Spectral Element Method for Impact Identification of Frame Structures using Enhanced GAs

This paper develops an Enhanced Genetic Algorithm (GA) strategy in conjunction with a time-domain Spectral Finite Element Method (SFEM) for impact identification of framed structures. For this purpose, a spatial truss spectral element is proposed for impact response simulation. In this regard, Gauss-Lobatto-Legendre quadrature rules and points configuration are adopted to construct a diagonal matrix and to gain an optimum computational demands. A decimal and a mixed GA coding system are implemented to locate and re-construct the impact features, respectively. An improved GA-based fitness assessment is designed to significantly accelerate the convergence rate of the optimization strategy. The impact identification of two frame structures is investigated taking into account the influence of externally applied loading. It is concluded that, the proposed mixed coding GA strategy effectively overcomes the main drawbacks of classical GA approach. The developed SFEM is superior to conventional FEM because of its high order interpolation and integration rules. For large structures, impact localization is successfully accomplished very fast, which provides the excellent ability in developing an on-line health monitoring system. It is included that, the robustness of the proposed SFEM lies on the considerably higher computational efficiency in achieving the most accurate results with the less computational costs.

In-situ aging monitoring of transformer oil via the relative permittivity and DC conductivity using novel interdigitated dielectrometry sensors (IDS)

Oil filled electrical transformers are present worldwide. Their lifetime is depended on many factors such as load, age and their environment, making the theoretical health prediction a challenge. A smarter approach is to monitor the health of these transformers online using in-situ sensors. Various attempts have been made, but to date a robust, reliable and cost efficient sensor (for industry attractive) that can sense transformer health according to established standards could not be found. We present a novel interdigitated dielectrometry sensor structure for the monitoring of transformer oil degradation. Our capacitive-type sensor uses three electrodes to provide an accurate and precise in-situ measurement of the complex permittivity, εr*, including the DC conductivity, σDC. Measurements were done with these sensors in an artificial oil aging experiment, degrading standard transformer oil and insulating paper at 140 °C over 800 h, mimicking an entire transformer life span of ∼ 60 years. It was found that εr* is proportional to the experiment time, yielding a transformer lifetime, LT. The conductivity increases exponentially with experiment time and is related to the oil acidity number, AN. Importantly, εr* is shown to also reflect the moisture in oil, the degree of polymerisation, DP, and tensile strength, ST, of the insulating paper. This demonstrates the suitability of the presented sensor as an oil health-monitoring instrument. Together with its robust and cost efficient design, it now paves the way for a real-world in-situ, real-time and online health monitoring of electrical transformers.

Time-efficient fault detection and diagnosis system for analog circuits

Time-efficient fault analysis and diagnosis of analog circuits are the most important prerequisites to achieve online health monitoring of electronic equipments, which are involving continuing challenges of ultra-large-scale integration, component tolerance, limited test points but multiple faults. This work reports an FPGA (field programmable gate array)-based analog fault diagnostic system by applying two-dimensional information fusion, two-port network analysis and interval math theory. The proposed system has three advantages over traditional ones. First, it possesses high processing speed and smart circuit size as the embedded algorithms execute parallel on FPGA. Second, the hardware structure has a good compatibility with other diagnostic algorithms. Third, the equipped Ethernet interface enhances its flexibility for remote monitoring and controlling. The experimental results obtained from two realistic example circuits indicate that the proposed methodology had yielded competitive performance in both diagnosis accuracy and time-effectiveness, with about 96% accuracy while within 60 ms computational time.

An online health monitoring system for photovoltaic arrays based on the B/S architecture

In order to improve the working efficiency of the photovoltaic (PV) array inspection system, the application of online monitoring systems for the PV arrays is quickly rising. In this paper, an online PV array monitoring combining with B/S architecture, Python language, and Flask framework is developed to monitor and diagnose the status of PV arrays which can display the data of the PV array and facilitate the staff to monitor the working status of PV array. When a fault occurs, the staff can also register the fault information on the website quickly. The developed system is composed of three parts: data acquisition, data transmission and PV online monitoring website. Firstly, the system uses the Raspberry Pi3 to collect the data when the PV array operates in maximum power point (MPP), and the acquired data is stored locally in the Raspberry Pi. And then, the data is uploaded to the PC side via the FileZilla server software and further transmitted to online monitoring website. Finally, the PV array online monitoring website displays received data, and perform the fault diagnosis of PV array using a kernel based Extreme Learning Machine (KELM). Experimental results show that the total classification accuracy for ten different operating conditions can reach 97.2%.

Temperature Resistant Fiber Bragg Gratings for On-Line and Structural Health Monitoring of the Next-Generation of Nuclear Reactors.

The harsh environment associated with the next generation of nuclear reactors is a great challenge facing all new sensing technologies to be deployed for on-line monitoring purposes and for the implantation of SHM methods. Sensors able to resist sustained periods at very high temperatures continuously as is the case within sodium-cooled fast reactors require specific developments and evaluations. Among the diversity of optical fiber sensing technologies, temperature resistant fiber Bragg gratings are increasingly being considered for the instrumentation of future nuclear power plants, especially for components exposed to high temperature and high radiation levels. Research programs are supporting the developments of optical fiber sensors under mixed high temperature and radiative environments leading to significant increase in term of maturity. This paper details the development of temperature-resistant wavelength-multiplexed fiber Bragg gratings for temperature and strain measurements and their characterization for on-line monitoring into the liquid sodium used as a coolant for the next generation of fast reactors.