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In bearing diagnostics using a data-driven modeling approach, a concern is the need for data from all possible scenarios to build a practical model for all operating conditions. This paper is a study on bearing diagnostics with the concurrent occurrence of multiple defect types. The authors are not aware of any work in the literature that studies this practical problem. A strategy based on one-versus-all (OVA) class binarization is proposed to improve fault diagnostics accuracy while reducing the number of scenarios for data collection, by predicting concurrent defects from training data of normal and single defects. The proposed OVA diagnostic approach is evaluated with empirical analysis using support vector machine (SVM) and C4.5 decision tree, two popular classification algorithms frequently applied to system health diagnostics and prognostics. Statistical features are extracted from the time domain and the frequency domain. Prediction performance of the proposed strategy is compared with that of a simple multi-class classification, as well as that of random guess and worst-case classification. We have verified the potential of the proposed OVA diagnostic strategy in performance improvements for single-defect diagnosis and predictions of BPFO plus BPFI concurrent defects using two laboratory-collected vibration data sets.

The battery management system (BMS) is a critical component of electric and hybrid electric vehicles. The purpose of the BMS is to guarantee safe and reliable battery operation. To maintain the safety and reliability of the battery, state monitoring and evaluation, charge control, and cell balancing are functionalities that have been implemented in BMS. As an electrochemical product, a battery acts differently under different operational and environmental conditions. The uncertainty of a battery’s performance poses a challenge to the implementation of these functions. This paper addresses concerns for current BMSs. State evaluation of a battery, including state of charge, state of health, and state of life, is a critical task for a BMS. Through reviewing the latest methodologies for the state evaluation of batteries, the future challenges for BMSs are presented and possible solutions are proposed as well.

Lead-zirconate-titanate (PZT) nanoscale powder was first synthesized by the sol-gel method, then PZT and 0–3 type PZT/chrysotile fiber (CSF)/cement composite (PZTCC) wafers were fabricated after grind-mixing PZT powder with strontium carbonate and/or cement, ductile CSF in tandem with press-sintered process, respectively. The crystal structure (XRD), microstructure (SEM), piezoelectric properties after surface silver penetration, and polarization of the PZT and PZTCC wafer were investigated. Furthermore, self-sensing responses under either impulse or cyclic loading and micro-hardness toughness of PZTCC were also investigated. Results show that the incorporation of CSF and cement admixture weakens the perovskite crystalline peak of PZTCC; reduces the corresponding piezoelectric coefficient from 119.2 pC/N to 32.5 pC/N; but effectively bridges the gap on the toughness between PZTCC and concrete since the corresponding microhardness with 202.7 MPa of PZTCC is close to that of concrete. A good linear and fast electrical response against either impulse or cyclic loading of the PZTCC is achieved with their respective sensitivity, linearity, and repeatability to 1.505 mV/N, 2.42%, and 2.11%. The sensing responses and toughness of PZTCC is encouraging as an intrinsic piezoelectric sensor for real-time health monitoring of ductile concrete structures.

This research article presents dual-frequency correlation models for predicting the growth of elasticity and flexural strength of engineered cementitious composites (ECCs) using microwave nondestructive inspection technique. Parallel measurements of microwave properties and mechanical properties of ECC specimens were firstly undertaken in the sense of cross-disciplinary experiments. Regression models were developed via means of nonlinear regression to the measured data. The purpose of the study is: (i) to monitor the flexural strength and elasticity growth; and (ii) to predict their mature values under the influence of different initial water contents, via microwave effective conductance at early ages. It has been demonstrated that both the modulus of rupture (MOR) and modulus of elasticity (MOE) can be accurately modeled and correlated by microwave conductance using exponential functions. The moduli developed as a function of conductance whereas the regression coefficient exhibited a linear relation with water-to-binder ratio. These findings have highlighted the effectiveness of the microwave non-destructive technique in inspecting the variation of liquid phase morphology of ECCs. The dual-frequency correlation can be used for structural health monitoring, which is not only for prediction but also provides a means of verification.

The conventional isolation structure with rubber bearings exhibits large deformation characteristics when subjected to infrequent earthquakes, which may lead to failure of the isolation layer. Although passive dampers can be used to reduce the layer displacement, the layer deformation and superstructure acceleration responses will increase in cases of fortification earthquakes or frequently occurring earthquakes. In addition to secondary damages and loss of life, such excessive displacement results in damages to the facilities in the structure. In order to overcome these shortcomings, this paper presents a structural vibration control system where the base isolation system is composed of rubber bearings with magnetorheological (MR) damper and are regulated using the innovative control strategy. The high-order single-step algorithm with continuity and switch control strategies are applied to the control system. Shaking table test results under various earthquake conditions indicate that the proposed isolation method, compared with passive isolation technique, can effectively suppress earthquake responses for acceleration of superstructure and deformation within the isolation layer. As a result, this structural control method exhibits excellent performance, such as fast computation, generic real-time control, acceleration reduction and high seismic energy dissipation etc. The relative merits of the continuity and switch control strategies are also compared and discussed.

The health conditions of complicated concrete structures require intrinsic cement-based sensors with a fast sensing response and high accuracy. In this paper, static, modal, harmonic, and transient dynamic analyses for the 0–3 type piezoelectric cement-based material with interdigitated electrodes (IEPCM) wafer were investigated using the ANSYS finite element numerical approach. Optimal design of the IEPCM was further implemented with electrode distance (P), electrode width (W), and wafer density (H) as the main parameters. Analysis results show that the maximum stress and strain in the x-polarization direction of the IEPCM are 2.6 and 3.19 times higher than that in the y-direction, respectively; there exists no repetition frequency phenomenon for the IEPCM. These indicate 0–3 type IEPCM possesses good orthotropic features, and lateral driving capacity notwithstanding, a hysteresis effect exists. Allowing for the wafer width (Wp) of 1 mm, the optimal design of the IEPCM wafer arrives at the best physical values of H, W and P are 6.2, 0.73 and 1.02 mm respectively, whereas the corresponding optimal volume is 10.9 mm3.

Dielectric characterization of concrete is essential for the wireless structural health monitoring (SHM) of concrete structures. Guo Biao (GB) concrete refers to the concrete mixed and cast in accordance with the Chinese standard. Currently, China is the largest producer and consumer of concrete in the world. However, minimal attention has been paid to the dielectric properties of GB concrete. This paper presents the results of the dielectric constant of GB concrete, where three regression models have been used to present the measurement data from 10 MHz to 6 GHz. The objective is to provide a data set of nominal values of the dielectric constant for ordinary GB concrete. The final goal is to facilitate a compressive strength evaluation via the measured dielectric constant. Measurements of the dielectric constant and compressive strength for five types of ordinary concrete have been undertaken, after 28 days of curing. As the main contribution in this work, the correlation model between the compressive strength and dielectric constant of GB concrete is realized.

This paper presents the results of microwave and mechanical measurements of engineered cementitious composites (ECCs) using a nondestructive microwave near-field detecting technique. The objective of this research is to investigate the correlations between effective conductance and compressive strength of ECCs at various curing ages under the influence of different initial water contents. Parallel measurements and regression analysis on compressive strength and microwave conductance were undertaken. It is shown that the strength evolution of ECCs can be accurately modeled and predicted by using microwave conductance at the early ages using bi-exponential functions. Compressive strength grows as a function of decreasing effective conductance, whereas the regression coefficients of the correlation models have a linear variation with water-to-binder ratios. These findings have highlighted the effectiveness of the microwave technique in detecting the variation of liquid phase morphology and pore structure.

Because of their low cost, photovoltaic (PV) cells made from upgraded metallurgical grade silicon (UMG-Si) are a promising alternative to conventional solar grade silicon-based PV cells. This study investigates the outdoor performance of a 1.26 kW grid-connected UMG-Si PV system over five years, reporting the energy yields and performance ratio and estimating the long-term performance degradation rate. To make this investigation more meaningful, the performance of a mono-Si PV system installed at the same place and studied during the same period of time is presented for reference. Furthermore, this study systematizes and rationalizes the necessity of a data selection and filtering process to improve the accuracy of degradation rate estimation. The impact of plane-of-array irradiation threshold for data filtering on performance ratio and degradation rate is also studied. The UMG-Si PV system’s monthly performance ratio after data filtering ranged from 84% to 93% over the observation period. The annual degradation rate was 0.44% derived from time series of monthly performance ratio using the classical decomposition method. A comparison of performance ratio and degradation rate to conventional crystalline silicon-based PV systems suggests that performance of the UMG-Si PV system is comparable to that of conventional systems.

Newborn screening (NBS) is an important public health program that aims to identify pre-symptomatic healthy babies that will develop significant disease if left undiagnosed and untreated. The number of conditions being screened globally is expanding rapidly in parallel with advances in technology, diagnosis, and treatment availability for these conditions. In Hong Kong, NBS for inborn errors of metabolism (NBSIEM) began as a pilot program in October 2015 and was implemented to all birthing hospitals within the public healthcare system in phases, with completion in October 2020. The number of conditions screened for increased from 21 to 24 in April 2016 and then to 26 in October 2019. The overall recruitment rate of the NBS program was 99.5%. In the period between October 2015 and December 2022, 125,688 newborns were screened and 295 were referred back for abnormal results. The recall rate was reduced from 0.26% to 0.12% after the implementation of second-tier testing. An inherited metabolic disorder (IMD) was eventually confirmed in 47 infants, making the prevalence of IMD in Hong Kong 1 in 2674. At the time of the NBS result, 78.7% of the newborns with IMD were asymptomatic. There were two deaths reported: one newborn with methylmalonic acidemia cobalamin B type (MMACblB) died after the initial crisis and another case of carnitine palmitoyltransferase II deficiency (CPTII) died at 18 months of age after metabolic decompensation. The most common IMD noted were disorders of fatty acid oxidation metabolism (40%, 19 cases), closely followed by disorders of amino acid metabolism (38%, 18 cases), with carnitine uptake defect (19.1%, 9 cases) and citrullinemia type II (17%, 8 cases) being the two most common IMD picked up by the NBSIEM in Hong Kong. Out of the all the IMDs identified, 19.1% belonged to diverse ethnic groups. False negative cases were reported for citrullinemia type II and congenital adrenal hyperplasia during this period.

Background and Objectives: Statins have been extensively utilised in atherosclerotic cardiovascular disease (ASCVD) prevention and can inhibit inflammation. However, the association between statin therapy, subclinical inflammation and associated health outcomes is poorly understood in the primary care setting. Materials and Methods: Primary care electronic health record (EHR) data from the electronic Practice-Based Research Network (ePBRN) from 2012–2019 was used to assess statin usage and adherence in South-Western Sydney (SWS), Australia. Independent determinants of elevated C-reactive protein (CRP) were determined. The relationship between baseline CRP levels and hospitalisation rates at 12 months was investigated. Results: The prevalence of lipid-lowering medications was 14.0% in all adults and 44.6% in the elderly (≥65 years). The prevalence increased from 2012 to 2019 despite a drop in statin use between 2013–2015. A total of 55% of individuals had good adherence (>80%). Hydrophilic statin use and higher intensity statin therapy were associated with elevated CRP levels. However, elevated CRP levels were not associated with all-cause or ASCVD hospitalisations after adjusting for confounders. Conclusions: The prevalence and adherence patterns associated with lipid-lowering medications highlighted the elevated ASCVD-related burden in the SWS population, especially when compared with the Australian general population. Patients in SWS may benefit from enhanced screening protocols, targeted health literacy and promotion campaigns, and timely incorporation of evidence into ASCVD clinical guidelines. This study, which used EHR data, did not support the use of CRP as an independent marker of future short-term hospitalisations.

Estimating blood pressure via combination analysis with electrocardiogram and photoplethysmography signals has attracted growing interest in continuous monitoring patients’ health conditions. However, most wearable/portal monitoring devices generally acquire only one kind of physiological signals due to the consideration of energy cost, device weight and size, etc. In this study, a novel adaptive weight learning-based multitask deep learning framework based on single lead electrocardiogram signals is proposed for continuous blood pressure estimation. Specifically, the proposed method utilizes a 2-layer bidirectional long short-term memory network as the sharing layer, followed by three identical architectures of 2-layer fully connected networks for task-specific blood pressure estimation. To learn the importance of task-specific losses automatically, an adaptive weight learning scheme based on the trend of validation loss is proposed. Extensive experiment results on Physionet Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) II waveform database demonstrate that the proposed method using electrocardiogram signals obtains estimating performance of 0.12±10.83 mmHg, 0.13±5.90 mmHg, and 0.08±6.47 mmHg for systolic blood pressure, diastolic blood pressure, and mean arterial pressure, respectively. It can meet the requirements of the British Hypertension Society standard and US Association of Advancement of Medical Instrumentation standard with a considerable margin. Combined with a wearable/portal electrocardiogram device, the proposed model can be deployed to a healthcare system to provide a long-term continuous blood pressure monitoring service, which would help to reduce the incidence of malignant complications to hypertension.

Facilitated by rapid development of the data-intensive techniques together with communication and sensing technology, we can take advantage of smart card data collected through Automatic Fare Collection (AFC) systems to establish connections between public transit and urban spatial structure. In this paper, with a case study on Shenzhen metro system in China, we investigate the agglomeration pattern of passenger flow among subway stations. Specifically, leveraging inbound and outbound passenger flows at subway stations, we propose a clustering refinement approach based on cluster member stability among multiple clusterings produced by isomorphic or heterogeneous clusterers. Furthermore, we validate and elaborate five clusters of subway stations in terms of regional functionality and urban planning by comparing station clusters with reference to government planning policies and regulations of Shenzhen city. Additionally, outlier stations with ambiguous functionalities are detected using proposed clustering refinement framework.

Partial Discharge (PD) pattern recognition plays an important part in electrical equipment fault diagnosis and maintenance. Feature extraction could greatly affect recognition results. Traditional PD feature extraction methods suffer from high-dimension calculation and signal attenuation. In this study, a novel feature extraction method based on Ensemble Empirical Mode Decomposition (EEMD) and Sample Entropy (SamEn) is proposed. In order to reduce the influence of noise, a wavelet method is applied to PD de-noising. Noise Rejection Ratio (NRR) and Mean Square Error (MSE) are adopted as the de-noising indexes. With EEMD, the de-noised signal is decomposed into a finite number of Intrinsic Mode Functions (IMFs). The IMFs, which contain the dominant information of PD, are selected using a correlation coefficient method. From that, the SamEn of selected IMFs are extracted as PD features. Finally, a Relevance Vector Machine (RVM) is utilized for pattern recognition using the features extracted. Experimental results demonstrate that the proposed method combines excellent properties of both EEMD and SamEn. The recognition results are encouraging with satisfactory accuracy.

This paper proposes a novel energy based technique called the Robustness Area (RA) technique that measures power system robustness levels, as a helper for planning Power System Restorations (PSRs). The motivation is on account of the latest blackouts in Brazil, where the local Independent System Operator (ISO) encountered difficulties related to circuit disconnections during the restoration. The technique identifies vulnerable and robust buses, pointing out system areas that should be firstly reinforced during PSR, in order to enhance system stability. A Brazilian power system restoration area is used to compare the guidelines adopted by the ISO with a more suitable new plan indicated by the RA tool. Active power and reactive power load margin and standing phase angle show the method efficiency as a result of a well balanced system configuration, enhancing the restoration performance. Time domain simulations for loop closures and severe events also show the positive impact that the proposed tool brings to PSRs.