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Free iron oxide in soil is a product of weathering, migration, and transformation of soil parent materials. Its form, activation, and aging degree can reflect the soil forming process and environment. It is also the main carrier of soil variable charge and an important mineral bonding substance in soil, which can directly reflect the formation environment, formation process, and climate change of the soil. Therefore, accurate determination of free iron in soil has important practical significance. At present, free iron is measured using ultraviolet spectrophotometry. The experimental process for free iron is long, requiring the reduction of high valent iron to low valent iron. In addition, it requires a color development time of up to 24h, which is time-consuming and cannot meet the needs of large-scale sample testing. Flame atomic absorption spectrometry method is used to determine free iron in soil, and the influence of experimental conditions on the determination of free iron is studied. By optimizing the amount of sodium bicarbonate, reaction temperature, reaction time, and cleaning times during the sample determination process, a method for determining free iron in soil is established. The optimal experimental conditions are determined as follows: weigh 0.5g of sample, add 20mL of sodium citrate solution and 2mL of sodium bicarbonate solution, and cleaned once each at a reaction temperature of 75−80℃ for 15min. The detection limit (3σ) of this method is 0.05g/kg, the quantification limit is 0.20g/kg, the linear correlation coefficient of the standard curve is 0.9995, the precision (RSD, n=6) is 1.95%−3.76%, and the standard error is less than 5%, which meets the analysis requirements of soil investigation. It can be widely applied to the analysis of soil and sediment samples in soil investigation.

Aiming at the problem of the poor working environment and the fault mode is difficult to identify of the gearbox of military armored vehicles， based on the existing methods， the noise assisted analysis， LMD and BP neural network are combined to apply to the fault diagnosis of armored vehicle gearbox. Firstly， the vibration signals under the four typical states of normal gearbox， bearing clearance fault， indentation of outer ring and broken tooth of gear of the gearbox are collected on the self-built test bench. Then， the signal is decomposed by the noise-assisted LMD method， and the energy eigenvalues of the first eight PF components are extracted. The extracted feature values are used as the input of the BP neural network. The fault type of the gearbox is identified based on the output result. The results show that the method can be effectively applied to the fault diagnosis of military armored vehicle gearbox， and the diagnostic accuracy rate is 92.5%. An effective reference way for other special gearbox diagnosis is provided by this study， and it has certain engineering practical value.