Health Monitoring Strategies Research Articles

Monitoring structural behaviour of concrete beams reinforced with steel and GFRP bars using acoustic emission and digital image correlation techniques

The paper investigates the flexure behaviour and cracking pattern of reinforced concrete beams utilizing steel and GFRP reinforcement using a judicious combination of non-destructive testing techniques of Acoustic Emission (AE) and Digital Image Correlation (DIC). AE parameters including amplitude, number of AE hits, average frequency (AF) and rise angle (RA) successfully correlate with cracking initiation and its progression in steel reinforced concrete (RC) and GFRP RC beams. The plot between AF-RA value effectively classifies the different failure modes in the two types of beams. Simultaneous drop in AF and a significant jump in AE duration at the same instant give a fair indication of critical failure points. AE-XY plots and longitudinal strain profiles obtained from DIC give a real-time representation of progressive AE activity and strains respectively. While AE is proposed to characterize the onset and propagation of the cracking pattern and mode of failure in the beams, DIC assists to visualize the displacement and strain fields while highlighting discontinuities in the displacement field. The work is aimed at developing a health monitoring strategy for RC beams with steel or GFRP bars which is in-situ, non-invasive and provides pre-emptive warning in real-time so that catastrophic failures can be completely avoided.

Data-Driven Framework for Tool Health Monitoring and Maintenance Strategy for Smart Manufacturing

Tool health monitoring and maintenance scheduling are crucial to empower smart manufacturing. Focusing on realistic needs, this study aims to develop a data-driven framework that integrates partial least squares and exponentially weighted moving-average for feature selection and model construction to monitor and predict tool health via analyzing status data collected from the sensors and thus derive the optimal maintenance strategy for smart production. Indeed, the proposed approach can deal with multi-collinearity of equipment and process data efficiently. An empirical study is conducted for validation in a leading thin film transistor liquid crystal displays manufacturing company. The results have shown practical viability of the proposed approach to provide an early detection of abnormal tool status, prolong the maintenance cycles for enhancing capacity utilization and productivity, and thus reduce the cost. Indeed, the developed solution is implemented in real settings as partial effort for enabling Industry 3.5.

FRACTAL DIMENSION ANALYSIS FOR ASSESSING THE HEALTH CONDITION OF A TRUSS STRUCTURE USING VIBRATION SIGNALS

During the last years, civil infrastructure has experienced an increasing development to satisfy the society’s demands such as communication, transportation, work and living spaces, among others. In this sense, the development and application of methods to guarantee the structure optimal operation, known as Structural Health Monitoring schemes, are necessary in order to avoid economic and human losses. Modern schemes employ the structure vibration response as any damage will modify the structure physical properties, which will be reflected in the vibration response. Thus, by measuring the waveform changes of the response, the structure condition can be determined. Considering this fact, this paper investigates the effectiveness of Katz fractal dimension, Higuchi fractal dimension, Box fractal dimension, Petrosian fractal dimension, and Sevcik fractal dimension which are nonlinear measurements to extract features of vibration signals in order to determine the health condition of a 3D 9-bay truss-type bridge. The obtained results show that the algorithms corresponding to Higuchi and Petrosian fractal dimension algorithms exceed the other nonlinear measurements in efficiency to discriminate between a healthy structure and a damage produced by corrosion.

Efficient and high‐precision time synchronization for wireless monitoring of civil infrastructure subjected to sudden events

The implementation of a wireless smart sensor network (WSSN) to monitor a structure subjected to sudden events (e.g., earthquakes) poses many challenges. One typical challenge is to synchronize the data from different sensors during event-triggered sensing, because each wireless sensor has its own independent clock and their triggering time is generally different and unknowable. The challenge becomes more acute when real-time data acquisition is required to support vibration control or rapid damage assessment under sudden events. Although various strategies have been proposed to achieve synchronized sensing of WSSNs, all of these strategies fall short in addressing the challenges of sudden-event monitoring. In this paper, we first proposed an efficient two-stage time synchronization strategy for general structural health monitoring as a baseline and implemented it on a next-generation wireless smart sensor. Building upon the baseline method, efficient time synchronization strategies are proposed, which are fully autonomous and highly efficient for sudden-event monitoring. Two classes of events are considered: short-duration events, for example, bridge impacts, during which initial data loss issues are critical, and long-duration events, such as strong winds, which can benefit significantly from employing real-time monitoring. For short-duration events, a post-event time synchronization strategy is developed, which autonomously performs offline synchronization after measurement. For long-duration events, a real-time time synchronization strategy is proposed, which can support real-time applications under sudden events, for example, vibration control. These time synchronization strategies are experimentally validated to demonstrate the efficiency and accuracy (under 20-μs maximum error) of the proposed sudden-event synchronized sensing strategies.

Brief interventions for NCDs risk factors

Abstract Background Brief interventions (BIs) are short, structured, nonconfrontational conversations to motivate and support individuals to consider and plan changes in unhealthy behaviours. Noncommunicable diseases (NCDs) are a huge global burden responsible for a large number of premature deaths and other economic and social challenges. The WHO “Best Buys” report highlights BIs as an effective tool to address NCD risk factors in the general population. BIs are not quick fixes, but part of a long-term behavior change strategy. An integrated BI package is needed to prevent unintended consequences, such as increased physical activity leading to increased consumption of unhealthy foods. BIs can be applied in a wide range of settings, including primary care in the general population, prisons and other settings for vulnerable groups. Results BI deliver immediate and long-term health, economic and social benefits. For example, BIs for smoking cessation can help individuals increase their life expectancy by up to 10 years, as well as reducing the risk of NCDs including stroke, lung cancer and CHD. Economic benefits include increased personal income and higher productivity, whilst social benefits include improved relationships and children being less likely to take up smoking. Conclusions WHO have published reports and training packages focused on the 5As and 5Rs brief interventions model in four major NCD risk factors: smoking, alcohol, unhealthy nutrition and physical inactivity. However, BI implementation remains low. To address this, WHO are currently working on a Manual for Integrated BI for NCD risk factors to support countries to implement, establish and promote BIs in their primary health care setting. It will contain information on health systems organization, intervention delivery, training and capacity building for healthcare professionals, referrals and follow-up, health literacy, and monitoring and evaluation strategies.

Systematic sensor placement for structural anomaly detection in the absence of damaged states

In structural health monitoring (SHM), risk assessment and decision strategies rely primarily on sensor responses. Simulated data can be generated to emulate the monitoring phenomena under different natural operational and environmental conditions in order to discriminate relevant features and thus identify potential anomalies. Reduced order modeling techniques and one-class machine learning algorithms allow to efficiently achieve this goal for a fixed number and location of sensors. However, since the number of sensors available on a structure is often a limitation for SHM, identifying the optimal locations that maximize the observability of the discriminant features becomes a fundamental task. In this work we propose to use the variational approximation of sparse Gaussian processes to systematically place a fixed number of sensors over a structure of interest. The healthy parametric variations of the structure are included by clustering the inducing inputs, i.e., the outcome of variational inference. This technique is tested on several numerical examples and is demonstrated to be efficient in detecting damages. In particular, it allows for considering the realistic case where damage types and locations are a priori unknown, thus, overcoming the main limitation of existing sensor placement strategies for SHM.

Hierarchical multiscale permutation entropy-based feature extraction and fuzzy support tensor machine with pinball loss for bearing fault identification

Recently, tensor learning has been applied to intelligent bearing health monitoring successfully. In the past tensor learning based fault identification approaches, the clipped time–frequency images (TFIs) are usually taken as the input tensor data. However, the manual clipping method is usually used to reduce the dimension of TFIs, which may easily lose some critical fault information. Hence, we present hierarchical multiscale permutation entropy (HMPE) as a new feature representation method to avert this drawback from the perspective of statistical measure. In practice, the extracted fault feature (HMPE) may often be corrupted by noise. Hence, fuzzy support tensor machine with pinball loss (Pin-FSTM) as a new tensor-based classifier is developed, which can reduce noise sensitivity and minimize classification errors. Finally, a novel bearing health monitoring strategy based on HMPE and Pin-FSTM is presented, which has been demonstrated to have an excellent identification performance through two experiments. Compared with the state-of-the-art permutation entropy-based fault identification algorithms, the presented scheme is more superior in accuracy and stability. Besides, this research provides a new direction for tensor learning further applying in bearing health monitoring.

RETRACTED ARTICLE: Economic IoT strategy: the future technology for health monitoring and diagnostic of agriculture vehicles

Today’s Agriculture vehicles (AgV)s are expected to encompass mainly the three requirements of customers; economy, the use of High technology and reliability. In this manuscript, we investigate the technology solution for efficient health monitoring and diagnostic (HM&D) strategy to maximize the field efficiency and minimize the machine cost. Based on the data captured by various IoT sensors, we demonstrate the facts to shift the HM&D technology from costly sensor to economic microphone based mechanism. The adopted strategy is capable to reduce the bulky data transmission on the internet, and to increase the up-time of AgVs. We experimented on the essential red hot chili peppers system of the AgV’s backbone hydraulic system—the hydraulic filter and pump. The measurement system analysis is adopted to determine the preciseness of data captured near the considered components. The envision of the correlation between the collected data extracts significant information to draw the facts to embrace the future HM&D technology shift. Correlation between the signals captured from costly sensors and Microphone for the generated faults in hydraulic components demonstrates the effectiveness of audio to replace existing HM&D technology.

An innovative hybrid strategy for structural health monitoring by modal flexibility and clustering methods

Structural health monitoring is usually implemented by model-driven or data-driven methods. Both of them have their advantages and disadvantages. This article proposes an innovative hybrid strategy as a combination of model-driven and data-driven approaches to detecting and locating damage in civil structures. In this regard, modal flexibility matrices of the undamaged and damaged conditions are initially derived from their modal frequencies and mode shapes. Subsequently, the discrepancy between these matrices is proposed as a damage-sensitive feature. To increase damage detectability and localizability, the modal flexibility discrepancy matrix is expanded by the Kronecker product and then converted into a vector by a simple vectorization algorithm yielding vector-style feature samples. To detect and locate damage, this article introduces the k-medoids and density-based spatial clustering of applications with noise techniques. The vector-style feature samples are incorporated into these clustering methods to obtain two different damage indices including the direct clustering outputs and their Frobenius norms. The great novelty of this article is to develop an innovative hybrid strategy for damage detection and localization under noise-free and noisy conditions so that the damage-sensitive feature is obtained from a model-driven scheme and the decision-making is carried out by a data-driven strategy. A shear-building frame and the numerical model of the ASCE benchmark structure are used to validate the accuracy and performance of the proposed methods. Results demonstrate that the hybrid strategy presented here is influentially able to detect and locate damage in the presence of noisy modal data.

Identifying Unique Power System Signatures for Determining Vulnerability of Critical Power System Assets

In this paper, the finer granularity of phasor measurement unit (PMU) data is exploited to develop a data-driven ap- proach for accurate health assessment of large power trans- formers(LPTs). There research demonstrates how variations in signal-to-noiseratios (SNRs) of PMU measurements can be used as a reliable metric for health assessment. However, a single PMU device maybe affected by multiple equipment located in its neighborhood. To address the challenge of identifying the equipment that is primarily responsible for the degradation inquality of the PMU measurements,an in- telligent sensor selection scheme is proposed,which ensures that every critical equipment is associated with a unique identifying signature. The proposed algorithm is based on a real LPT failure event that occurred in the US Southwest. The inferences drawn from the proposed PMU-based health monitoring scheme can be easily supplemented with other LPT sensors to facilitate proactive intervention before the point-of-no-return is reached.

Swaying displacement measurement for structural monitoring using computer vision and an unmanned aerial vehicle

Data acquisition is the challenging and crucial step for any structural health monitoring (SHM) scheme, especially on numerous measurement locations that are typically at very high elevations or largely inaccessible areas, which are often linked to time-consuming, costly, and to some extent, dangerous sensor implementation and cable wiring. Noncontact vision-based measurement techniques have been recognized recently as a primarily feasible approach, although it is still characterized by some limitations. To address these constraints, the proposed study introduced an enhanced noncontact displacement measurement method that employed an unmanned aerial vehicle (UAV) and computer vision algorithms. Since UAV can carry cameras to approach any difficult-to-reach regions, the proposed system can overcome several bottlenecks of the state-of-the-art vision-based methods with regard to finding a stationary place for the camcorder and for mitigating the inaccuracy induced by the long distance between the camcorder and the measurement location. Guided by the schematic framework for the system, a camera was mounted on the UAV for filming of the measurement point, and then displacements on that point were determined by a key-point vision-based measurement method. Moreover, translations generated by the UAV were obtained by means of reference objects on the background. Additionally, an autonomous scheme based on Canny edge detection and Hough transform were introduced for calculation of scale factors between the pixel and engineering unit for every image frame to address the issue of very fluctuant distances from the UAV to the measurement location. Subsequently, the actual displacements of the measurement location were measured following the elimination of the UAV motions from the displacement data. The proposed system was verified on an experiment with a small-sized steel tower where the outcomes provided an initial confirmation of the approach’s promising potential.

Vibration‐based detection and classification of structural changes using principal component analysis and ‐distributed stochastic neighbor embedding

This paper describes a structural health monitoring strategy to detect and classify structural changes in structures that can be equipped with sensors. The proposed approach is based on the t-distributed stochastic neighbor embedding (t-SNE), a nonlinear technique that can represent the local structure of high-dimensional data collected from multiple sensors in a plane or spatial representation. We propose the following basic steps for the detection and classification. First, the raw data are preprocessed: We scale the data using the mean-centered group scaling and apply principal component analysis to reduce the dimensionality of the scaled data. Second, t-SNE is applied to represent the scaled and reduced data as points in a plane, defining a cluster for each structural state. Finally, the current structure to be diagnosed is associated with a cluster (or structural state) using three different strategies: (a) the smallest point-centroid distance; (b) the majority voting; and (c) the sum of the inverse distances. The combination of t-SNE with our preprocessing and the three proposed classification strategies signif- icantly improves the quality of the clusters that represent different structural states. We evaluate the performance of our method using experimental data from an aluminum plate instrumented with piezoelectric transducers. Results are presented in the time domain, and they reveal the high classification accuracy and strong performance of this method, with a percentage of correct decisions close to 100% in several scenarios.

Improved electromagnetic coil insulation health monitoring using equivalent circuit model analysis

Impedance monitoring of electromagnetic coils is a means of monitoring insulation degradation, a major cause of failures of these coils. This paper develops an equivalent circuit model aimed at identifying diagnostic and prognostic features in the coil impedance frequency response that can be leveraged into a health-monitoring scheme. To identify impedance and frequency components that provide the greatest correlation with insulation degradation, a sensitivity analysis was performed and compared with experimental data. In contrast to previous research, the results reveal features in the impedance spectrum that are more sensitive to insulation health than the coil resonant frequency.

Analysis of operational and mechanical anomalies in scheduled commercial flights using a logarithmic multivariate Gaussian model

This paper presents a machine learning approach to evaluate the performance of aircrafts using on-board sensor information on commercially scheduled flights with the aim to further improve system health monitoring strategies in air transportation. Logarithmic multivariate Gaussian models are trained to evaluate the performance of aircrafts at different flight phases (takeoff, ascent, cruise, etc.) separately. By including a forward synchronization, feature selection, and mini-batch training process, this model overcomes challenges introduced by the large size and high dimensionality of flight datasets. This framework also addresses the re-sampling issue in existing literature causing difficulties in handling time-series signals with different lengths. For demonstration and validation, the developed model is applied to analyze performance anomalies associated with the mechanical system and pilot operation in a historical flight dataset. Compared with existing literature focusing on similar datasets, this evaluation methodology shows promise in detecting performance anomalies especially at approach and takeoff phases. Therefore, the developed model is expected to be an effective addition to the current anomaly analysis and monitoring technologies for scheduled commercial flights. Applications include assisting transportation management systems by handling large amounts of historical flight datasets to analyze mechanical and operational anomalies, which may potentially improve future aeronautical system design and pilot training.

Using drive-by health monitoring to detect bridge damage considering environmental and operational effects

Drive-by Health Monitoring utilizes accelerometers mounted on vehicles to gather dynamic response data that can be used to continuously evaluate the health of bridges faster and with less equipment than traditional structural health monitoring practices. Because vehicles and bridges create a coupled system, vehicle acceleration data contains information about bridge frequencies that can be used as health indicators. However, for drive-by health monitoring to be viable, variabilities in dynamic measurements caused by environmental and operational parameters, such as temperature, vehicle speed, traffic, and surface roughness need to be considered. In this paper, a finite element model of a simply supported bridge is developed considering the aforementioned variabilities and various levels of structural damage. Vehicle acceleration data obtained from the model is analyzed in the frequency domain and processed using a neural network architecture. This method is used to determine the relationships between noise inducing variables and changes in vehicle dynamic response spectrum; these relationships are leveraged to predict the overall health of the subject bridge. The results from this study indicate that the proposed approach can serve as a viable health monitoring strategy and should be further tested on physical bridge systems. Reproducibility: our code and data are available at [https://github.com/JSybrandt/HighPerformanceBridgeSim].

Health monitoring among refugees: a state-wide, cross-sectional, population-based study in Germany

Abstract Background Health monitoring in Germany falls short on generating timely, reliable and representative data among migrants, especially transient groups such as asylum seekers and refugees (ASR). We aim to advance health monitoring approaches and obtain reliable estimates on health status and access to health services among ASR in Germany’s 3rd largest federal state. Methods We conducted a state-wide, cross-sectional, population-based health monitoring survey in nine languages among ASR in collective accommodation centres. Questionnaire items capturing health status, access to care and sociodemographic variables were taken from established surveys and translated using a team approach. Random sampling on the level of 1,938 accommodation centres was employed to draw a balanced sample. Multilingual field teams recruited eligible participants using a “door-to-door” approach. Results Fieldwork was conducted in 58 centres, obtaining a response rate of 42.2% (n = 412). Age, sex and nationality of the included sample were comparable to the population of asylum applicants in Germany. Adults reported longstanding limitations (16%), bad/very bad general health (19%), pain (25%), chronic illness (40%), and depressive (46%) and anxiety symptoms (45%). 52% utilised primary and 37% specialist care services in the previous 12 months, while reporting unmet needs for primary (31%) and specialist care (32%). Younger and male participants had above-average health status and below-average utilisation, in contrast to older and female ASR. Conclusions Our health monitoring survey yielded reliable estimates on health status and health care access among ASR, revealing relevant morbidities and patterns of care. Applying rigorous epidemiological methods in linguistically diverse, transient and marginalized populations is challenging, but feasible. Integration of this approach into state- and nation-wide health monitoring strategies is needed to sustain this approach as a health planning tool. Key messages Applying rigorous epidemiological methods in linguistically diverse, transient and marginalized populations is challenging, but feasible. Sustainable integration of multi-lingual epidemiological methods is required in state- and nation-wide health monitoring to capture health status and access to health services for ASR.

Incremental Capacity Analysis-Based Impact Study of Diverse Usage Patterns on Lithium-Ion Battery Aging in Electrified Vehicles

Aging assessment is critical for lithium-ion batteries (LIBs) as the technology of choice for energy storage in electrified vehicles (EVs). Existing research is mainly focused on either increasing modeling precision or improving algorithm efficiency, while the significance of data applied for aging assessment has been largely overlooked. Moreover, reported studies are mostly confined to a specific condition without considering the impacts of diverse usage patterns on battery aging, which is practically challenging and can greatly affect battery degradation. This paper addresses these issues through incremental capacity (IC) analysis, which can both utilize data directly available from on-board sensors and interpret degradations from a physics-based perspective. Through IC analysis, the optimal health feature (HF) and the state of charge (SOC)-based optimal data profile for battery aging assessment have been identified. Four stress factors, i.e., depth-of-discharge (DOD), charging C-rate, operating mode, and temperature, have been selected to jointly characterize diverse usage patterns. Impact analysis of different stress factors through the optimal HF with the SOC-based optimal data profile from aging campaign experiments have generated practical guidance on usage patterns to improve battery health monitoring and lifetime control strategies.

Probabilistic active learning: An online framework for structural health monitoring

Abstract A novel, probabilistic framework for the classification, investigation and labelling of data is suggested as an online strategy for Structural Health Monitoring (SHM). A critical issue for data-based SHM is a lack of descriptive labels (for measured data), which correspond to the condition of the monitored system. For many applications, these labels are costly and/or impractical to obtain, and as a result, conventional supervised learning is not feasible. This fact forces a dependence on outlier analysis, or one-class classifiers, in practical applications, as a means of damage detection. The model suggested in this work, however, allows for the definition of a multi-class classifier, to aid both damage detection and identification, while using a limited number of the most informative labelled data. The algorithm is applied to three datasets in the online setting; the Z24 bridge data, a machining (acoustic emission) dataset, and measurements from ground vibration aircraft tests. In the experiments, active learning is shown to improve the online classification performance for damage detection and classification.

Load-Independent Characterization of Plate Foundation Support Using High-Resolution Distributed Fiber-Optic Sensing.

The evaluation of soil reaction in geotechnical foundation systems such as concrete pavements, mat- and raft foundations is a challenging task, as the process involves both the selection of a representative mechanical model (e.g., Winkler, Continuum, Pasternak, etc.) and identify its prevailing parameters. Moreover, the support characteristics may change with time and environmental situation. This paper presents a new method for the characterization of plate foundation support using high-resolution fiber-optic distributed strain sensing. The approach involves tracking the location of distinct points of zero and maximum strains, and relating the shift in their location to the changes in soil reaction. The approach may allow the determination of the most suited mechanical model of soil representation as well as model parameters. Routine monitoring using this approach may help to asses the degradation of the subsoil with time as part of structural health monitoring strategies. In this paper, fundamental expressions that relate between the location of distinct strain points and the variation of soil parameters were developed based on various analytical foundation support models. Finally, as an initial validation step and to underpin the idea basics, the proposed method was successfully demonstrated on a simple mechanical setup. It is shown that the approach allows for load-independent characterization of the soil response and, in that sense, it is superior to common identification methods.

Health monitoring among asylum seekers and refugees: a state-wide, cross-sectional, population-based study in Germany

BackgroundHealth monitoring in Germany falls short on generating timely, reliable and representative data among migrants, especially transient and marginalized groups such as asylum seekers and refugees (ASR). We aim to advance current health monitoring approaches and obtain reliable estimates on health status and access to essential healthcare services among ASR in Germany’s third largest federal state, Baden-Württemberg.MethodsWe conducted a state-wide, cross-sectional, population-based health monitoring survey in nine languages among ASR and their children in collective accommodation centres in 44 districts. Questionnaire items capturing health status, access to care, and sociodemographic variables were taken from established surveys and translated using a team approach. Random sampling on the level of 1938 accommodation centres with 70,634 ASR was employed to draw a balanced sample of 65 centres with a net sample of 1% of the state’s ASR population. Multilingual field teams recruited eligible participants using a “door-to-door” approach. Parents completed an additional questionnaire on behalf of their children.ResultsThe final sample comprised 58 centres with 1843 ASR. Of the total sample expected eligible (N = 987), 41.7% (n = 412) participated in the survey. Overall, 157 households had children and received a children’s questionnaire; 61% (n = 95) of these were returned. Age, sex, and nationality of the included sample were comparable to the total population of asylum applicants in Germany. Adults reported longstanding limitations (16%), bad/very bad general health (19%), pain (25%), chronic illness (40%), depression (46%), and anxiety (45%). 52% utilised primary and 37% specialist care services in the previous 12 months, while reporting unmet needs for primary (31%) and specialist care (32%). Younger and male participants had above-average health status and below-average utilisation compared to older and female ASR.ConclusionsOur health monitoring survey yielded reliable estimates on health status and health care access among ASR, revealing relevant morbidities and patterns of care. Applying rigorous epidemiological methods in linguistically diverse, transient and marginalized populations is challenging, but feasible. Integration of this approach into state- and nation-wide health monitoring strategies is needed in order to sustain this approach as a health planning tool.