Compensation of the error of automotive capacitive fuel level sensors when using them on special self-propelled rolling stock

Consumption of diesel fuel by the special rolling stock of Russian Railways per year amounts to tens of thousands of tons, and the issue of reliable accounting and control of its consumption is quite urgent. Currently, part of the special self-propelled rolling stock is equipped with on-board systems for measuring fuel consumption, however, in many units of this equipment, fuel control and accounting is carried out in manual mode. Massive introduction of on-board fuel consumption measurement systems on special self-propelled rolling stock is constrained, on the one hand, by the rather high cost of fuel sensors used on locomotives, on the other hand, by the increased error of relatively inexpensive automotive capacitive fuel level sensors. As part of the laboratory tests of such sensors, it was determined that when they operate on fuel of the same grade, the error corresponds to the passport and is at the level of 1 %, and when operating on fuel of different grades without additional recalibration, the error can reach 4 % or more. This is largely due to the simplified technology for measuring the amount of fuel in units of volume and insufficient compensation for changes in the density of diesel fuel. To solve this problem, an alternative to standard technology for determining the amount of fuel using automotive capacitive fuel level sensors is proposed, in which the dependence of the readings of these sensors on the fuel density at a standard temperature, once obtained in laboratory conditions, is used. Proposed technology of using automotive capacitive fuel level sensors on a special self-propelled rolling stock will allow keeping its relative reduced error at the level of 1 % and will provide measurement of the amount of fuel in units of mass.

Optimal location of water level sensors for monitoring mine water inrush based on the set covering model

Water inrush is one of the major mining disasters that may lead to numerous casualties. The development of information techniques makes it possible to monitor the occurrence and evolution of water inrush. Then, locating monitors for water inrush becomes a primary problem. This study presents a method of optimal location of water level sensors by constructing a set covering model. The monitoring scope of the water level sensor at each location in a given time is computed first based on the numerical simulation of water spreading along mine tunnels. In this simulation, the water inrush quantity is assigned using the mine drainage capability over which an accident may occur. Then the greedy algorithm is used to optimize the number and positions of water level sensors. As results, a mine water disaster can be monitored in the given time after it happened. The proposed method is then verified in the Beiyangzhuang coal mine in the North China. The results show that at least 22, 36, 42, 64 and 106 water level sensors are needed to monitor water disasters in the whole mine within 60, 30, 20, 10 and 5 min, respectively.

MODERNIZATION OF THE LABORATORY STAND «INDUSTRIAL LEVEL SENSORS»

The structural scheme of the existing laboratory stand for the study of methods for level for the purpose of its modernization and implementation in the educational process.

Precise ground-based GNSS-reflectometry water level measurements using multiple low-cost antennas

<p>GNSS-Reflectometry (GNSS-R) is a promising new technique to monitor water levels due to easier and cheaper installation of instruments in remote environments compared to traditional acoustic sensors or pressure gauges. GNSS stations that have been used for reflectometry purposes thus far are designed for monitoring land motion and may cost more than 10,000 USD each. We have found that a low-cost GNSS antenna and receiver (10 USD) can be used to make equally precise water level measurements, with an RMSE of a few centimeters when compared to a collocated acoustic sensor. However, an RMSE of less than one centimeter is typical for water level sensors and this level of accuracy is desired for research purposes. Two of the dominant sources of error in GNSS-R measurements are the effects of random noise in the Signal-to-Noise Ratio (SNR) data and tropospheric delay. Modelling work suggests that these sources of error can be reduced by using multiple low-cost antennas in the same location. In light of this, we have installed an experimental setup of antennas at various locations along the Saint Lawrence River and Initial results show that multiple antennas can be used to provide more precise measurements than a single antenna. Our installations of multiple antennas are less than 5% of the cost of stations that have been used in previous GNSS-R literature. Hence this approach could be applied to install a dense network of water level sensors along rivers, lakes or coastlines at a relatively low cost. We expect that this approach could also be applied to GNSS-R soil moisture or snow depth measurements.</p>