In the fog and haze, the air contains large amounts of H2S, SO2, SO3 and other acids, air conductivity is greatly improved, the relative humidity is also greatly increased, Power transmission lines and electrical equipment in such an environment will increase in the long-running failure ratedecrease the sensitivity of the detection equipment, impact protection device reliability. Weibull distribution is widely used in component failure distribution fitting. It proposes a protection device aging failure rate estimation method based on the least squares method and the iterative method,.Combined with a regional power grid statistics, computing protective equipment failure rate function. Binding characteristics of electrical equipment operation status under haze conditions, optimization methods, get more in line with aging protection equipment failure under conditions of haze characteristics.
According to the problem that traditional chemical methods to the mine water source identification takes a long time, put forward a method for rapid source identification system of mine water inrush based on the technology of laser induced fluorescence (LIF). Emphatically analyzes the basic principle of LIF technology. The hardware composition of LIF system are analyzed and the related modules were selected. Through the fluorescence experiment with the water samples of coal mine in the LIF system, fluorescence spectra of water samples are got. Traditional water source identification mainly according to the ion concentration representative of the water, but it is hard to analysis the ion concentration of the water from the fluorescence spectra. This paper proposes a simple and practical method of rapid identification of water by fluorescence spectrum, which measure the space distance between unknown water samples and standard samples, and then based on the clustering analysis, the category of the unknown water sample can be get. Water source identification for unknown samples verified the reliability of the LIF system, and solve the problem that the current coal mine can't have a better real-time and online monitoring on water inrush, which is of great significance for coal mine safety in production.
In the monitoring system of coal mine gas, due to the use of optical fiber sensing gas, there were some defects include less monitoring points, low utilization rate of equipment and especially high cost, etc.Arming at the existing problem, through the study of network model for methane concentration detection, the monitoring system could achieve multi-point, wide-range online monitoring of methane concentration in real time.Based on the optical multiplexing technology, this paper proposed a optical network model of mixed multiplexing technology combined with the time division multiplexing (TDM) and the space division multiplexing (SDM) technology.The model realized 32 points of gas concentration monitoring with 4 points of space division multiplexing and 8 points of time division multiplexing which is more mature and stable.The experiment show that the accuracy of the 32 sensors’ minimum detectable gas has reached 5ppm and the changes in responsiveness and gas concentration trends are consistent with the theoretical analysis, which is linear in a certain range.Considering the characteristics of EFPI optical fiber sensor, the model makes full use of the advantages of two multiplexing technologies, time division multiplexing and space division multiplexing technology.The network model combines the use of optical fiber in a gas concentration monitoring system to improve the efficiency of the light source and optical signal processing equipment and greatly reduce the cost of system.The accuracy and stability of each sensor can meet the actual requirements to make the monitoring system achieve the goals of stable dynamic wide-rage detection of coal mine gas.
Laser spectroscopy combined with neural network approach is a new method of monitoring coal mine gas. This research
analyses of gas concentration and predicts the process of modeling using BP neural network finds changes law of
concentration, gives the various parameters settings of neural network. Experimental results show that, BP neural
network for early warning of gas concentration is feasible. The study meets an online, real-time, fast agreement of
China's Coal Mine Gas monitoring systems.
In coal industries of our country, gas accident has become the principal contradiction which restricts safety in production
of coal mining. Development of gas surveying system in coal mine has become an important project. In view of the flaws
of the former chemistry examination method, Tunable Diode Laser Technology (TDLAS) can be used to survey gas. It
can use an isolated absorption line of the gas molecular to survey absorption spectrum of gas, and use wavelength
modulation and detect the second-harmonic (2f). The harmonic signal has direct proportion to absorption gas density N;
it will obtain methane density in the gas after standard density methane gas spectral fitting. It can be known from
theoretical analysis and actual survey, using deduction background method to eliminate the interference fringe is very
effective. It may obtain very high selectivity and resolution. It has merits such as high sensitivity, high selectivity and
rapid survey. So it can forecast and alarm gas accident.
In view of the shortage of the former chemistry examination for coal mine gas it uses infrared spectrometry to monitor
gas, and propose the principle of surveying the coal mine gas basing on infrared absorption spectrum methods, and has
given the structure diagram of gas remote sensing alarm system. When infrared spectrum signal including the gas
information goes through the photoelectric detection, then it goes through the Fourier spectroscope to obtain the signal,
and does the enlargement transformation; at last the computer processes and recognizes the data. The theoretical analysis
and the experimental result indicate that after the signal pretreatment including characteristic extraction, background
deduction and normalized processing it obtains a standard standardized spectrum diagram. Based on gas member in
1.65μm absorption characteristic spectrum, when it is in before some background, the mine underground gas exists the
infrared radiation information that could change and arrive to the sensor, and then the alarm system can survey these
difference information and process it to confirm the gas density, when it is under certain threshold value the system will
alarm. Through the comparison, it retrieves the gas density in mine. This method has overcome the traditional method
such as gas chromatography hydrogen flame ionization detector with shortcoming of bad timeliness, has realized
complete non-contact online automatic monitor, and really reflected the gas density.
This paper introduces the principle and schematic diagram of gas monitoring system by means of infrared method. Annealing simulating algorithm is adopted to find the whole optimum solution and the Metroplis criterion is used to make iterative algorithm combination optimization by control parameter decreasing aiming at solving large-scale combination optimization problem. Experiment result obtained by the performing scheme of realizing algorithm training and flow of realizing algorithm training indicates that annealing simulating algorithm applied to identify gas is better than traditional linear local search method. It makes the algorithm iterate to the optimum value rapidly so that the quality of the solution is improved efficiently. The CPU time is shortened and the identifying rate of gas is increased. For the mines with much-gas gushing fatalness the regional danger and disaster advanced forecast can be realized. The reliability of coal-mine safety is improved.