A Non-Intrusive Pressure Sensor by Detecting Multiple Longitudinal Waves.

Pressure vessels are widely used in industrial fields, and some of them are safety-critical components in the system-for example, those which contain flammable or explosive material. Therefore, the pressure of these vessels becomes one of the critical measurements for operational management. In the paper, we introduce a new approach to the design of non-intrusive pressure sensors, based on ultrasonic waves. The model of this sensor is built based upon the travel-time change of the critically refracted longitudinal wave (LCR wave) and the reflected longitudinal waves with the pressure. To evaluate the model, experiments are carried out to compare the proposed model with other existing models. The results show that the proposed model can improve the accuracy compared to models based on a single wave.

At the limit? Using operational data to estimate train driver human reliability.

Human reliability analysis plays an important role in the safety assessment and management of rail operations. This paper discusses how the increasing availability of operational data can be used to develop an understanding of train driver reliability. The paper derives human reliability data for two driving tasks, stopping at red signals and controlling speed on approach to buffer stops. In the first of these cases, a tool has been developed that can estimate the number of times a signal is approached at red by trains on the Great Britain (GB) rail network. The tool has been developed using big data techniques and ideas, recording and analysing millions of pieces of data from live operational feeds to update and summarise statistics from thousands of signal locations in GB on a daily basis. The resulting driver reliability data are compared to similar analyses of other train driving tasks. This shows human reliability approaching the currently accepted limits of human performance. It also shows higher error rates amongst freight train drivers than passenger train drivers for these tasks. The paper highlights the importance of understanding the task specific performance limits if further improvements in human reliability are sought. It also provides a practical example of how big data could play an increasingly important role in system error management, whether from the perspective of understanding normal performance and the limits of performance for specific tasks or as the basis for dynamic safety indicators which, if not leading, could at least become closer to real time.

Multipath ultrasonic gas flow-meter based on multiple reference waves.

Several technologies can be used in ultrasonic gas flow-meters, such as transit-time, Doppler, cross-correlation and etc. In applications, the approach based on measuring transit-time has demonstrated its advantages and become more popular. Among those techniques which can be applied to determine time-of-flight (TOF) of ultrasonic waves, including threshold detection, cross correlation algorithm and other digital signal processing algorithms, cross correlation algorithm has more advantages when the received ultrasonic signal is severely disturbed by the noise. However, the reference wave for cross correlation computation has great influence on the precise measurement of TOF. In the applications of the multipath flow-meters, selection of the reference wave becomes even more complicated. Based on the analysis of the impact factors that will introduce noise and waveform distortion of ultrasonic waves, an averaging method is proposed to determine the reference wave in this paper. In the multipath ultrasonic gas flow-meter, the analysis of each path of ultrasound needs its own reference wave. In case study, a six-path ultrasonic gas flow-meter has been designed and tested with air flow through the pipeline. The results demonstrate that the flow rate accuracy and the repeatability of the TOF are significantly improved by using averaging reference wave, compared with that using random reference wave.