The Hopkinson pressure bar has been developed to calibrate and assess high g accelerometers’ capacity. The extreme caution is indispensable for performing calibration of severe characteristics, like the bearable super-high overload peak and wide duration of stress. In the paper, the Hopkinson bar calibrating system is being critically appraised. A limiting formula is deduced based on the stress wave theory. It indicates that the overload peak and duration of stress are limited by the elastic limit and wave speed of Hopkinson bar material. Both stress wave configurations in the form of linear ramp and cosine functions were designed theoretically to meet typical calibrating requirements. They were confirmed experimentally with the aid of the pulse shaping technique. Their corresponding calibration characteristics were analysed critically, and it was found that the cosine stress wave can achieve the values of acceleration peak or duration by π/2 times greater than those obtained with the linear stress wave. Finally, some suggestions are proposed for more extreme calibration requirements.

Considering the low accuracy and low efficiency of the traditional calibration method for base strain sensitivity of accelerometers, a novel base strain sensitivity calibration system with steady harmonic excitation is proposed. The required cantilever beam for calibration is driven by an electromagnetic exciter to generate a base strain varying in a steady harmonic pattern. By applying a Wheatstone bridge circuit, the generated strain with low distortion can be measured. The measurement system with a compensation function can automatically calibrate the base strain sensitivity. The amplitude linearity and frequency response characteristics of the base strain sensitivity in two accelerometers are obtained experimentally, and the uncertainty in the results is 2% ( k = 2).

The paper addresses the problem of experimental studies of miniature tilt sensors based on low-range accelerometers belonging to Microelectromechanical Systems (MEMS). A custom computer controlled test rig is proposed, whose kinematics allows an arbitrary tilt angle to be applied (i.e. its two components: pitch and roll over the full angular range). The related geometrical relationships are presented along with the respective uncertainties resulting from their application. Metrological features of the test rig are carefully evaluated and briefly discussed. Accuracy of the test rig is expressed in terms of the respective uncertainties, as recommended by ISO; its scope of application as well as the related limitations are indicated. Even though the test rig is mostly composed of standard devices, like rotation stages and incremental angle encoder, its performance can be compared with specialized certified machines that are very expensive. Exemplary results of experimental studies of MEMS accelerometers realized by means of the test rig are presented and briefly discussed. Few ways of improving performance of the test rig are proposed.

Since previous health monitoring systems have shown themselves to be unsuccessful in predicting health disorders in dairy cows managed on pasture, the aim of this study was to evaluate the performance of automated health monitoring integrated in an accelerometer-based oestrus detection system (ODS) for dairy cows on pasture. Mixed-breed lactating dairy cows (n=109) in a seasonal-calving herd managed at pasture were fitted with an ODS that provided automated health monitoring. The ODS performed multimetric analysis of behavioural patterns to generate health alerts. Data were collected during the artificial insemination period of 66 days. Clinical examinations and farmer’s observations were used to evaluate the performance of automated health monitoring. During the insemination period, the farmer generated two health alerts, which were classified false positives (2/2; 100%). The ODS generated 31 automated health alerts. Of all automated health alerts, 3/31 (9.7%) were confirmed as true health disorders and 28/31 (90.3%) alerts were classified as false positives. The positive predictive value (PPV) of automated health monitoring was 9.7 (95% CI=2-25.8) %. The ODS was able to alert lactating dairy cows on pasture suffering from health disorders. True health disorders were alerted by the ODS before the farmer noticed them, which could provide early and successful treatment when using the system on-farm for automated health monitoring. The evaluated accuracy of automated health monitoring is opposed to a targeted use of the system for on-farm health monitoring. For further validation, testing on other farms and during the transition period would be of interest.

The study presents the finite element (FE) model update of the existing simple-spans steelconcrete composite bridge structure using a particle swarm optimisation (PSO) and genetic algorithm (GA) approaches. The Wireless Structural Testing System (STS-WiFi) of Bridge Diagnostic, Inc. from the USA, implemented various types of sensors including: LVDT displacement sensors, intelligent strain transducers, and accelerometers that the static and dynamic historical behaviors of the bridge structure have been recorded in the field testing. One part of all field data sets has been used to calibrate the cross-sectional stiffness properties of steel girders and material of steel beams and concrete deck in the structural members including 16 master and slave variables, and that the PSO and GA optimisation methods in the MATLAB software have been developed with the new innovative tools to interface with the analytical results of the FE model in the ANSYS APDL software automatically. The vibration analysis from the dynamic responses of the structure have been conducted to extract four natural frequencies from experimental data that have been compared with the numerical natural frequencies in the FE model of the bridge through the minimum objective function of percent error to be less than 10%. In order to identify the experimental mode shapes of the structure more accurately and reliably, the discrete-time state-space model using the subspace method (N4SID) and fast Fourier transform (FFT) in MATLAB software have been applied to determine the experimental natural frequencies in which were compared with the computed natural frequencies. The main goal of the innovative approach is to determine the representative FE model of the actual bridge in which it is applied to various truck load
configurations according to bridge design codes and standards. The improved methods in this document have been successfully applied to the Vietnamese steel-concrete composite bridge in which the load rating factors (RF) of the AASHTO design standards have been calculated to predict load limits, so the final updated FE model of the existing bridge is well rated with all RF values greater than 1.0. The presented approaches show great performance and the potential to implement them in industrial conditions.

The paper presents major issues associated with the problem of modelling change output accelerometers. The presented solutions are based on the weighted least squares (WLS) method using transformation of the complex frequency response of the sensors. The main assumptions of the WLS method and a mathematical model of charge output accelerometers are presented in first two sections of this paper. In the next sections applying the WLS method to estimation of the accelerometer model parameters is discussed and the associated uncertainties are determined. Finally, the results of modelling a PCB357B73 charge output accelerometer are analysed in the last section of this paper. All calculations were executed using the MathCad software program. The main stages of these calculations are presented in Appendices A−E.