Starting from the time variance and uncertainty of accidental discharge, this paper describes the probability of the occurrence of the "normal-accident" alternate state for a risk source using the Markov sta...
详细信息
Starting from the time variance and uncertainty of accidental discharge, this paper describes the probability of the occurrence of the "normal-accident" alternate state for a risk source using the Markov state transfer model, simulates the behaviour of pollutants in rivers using the hydrodynamic and water quality models for non-conservative substances, and tracks the transport path of pollutants in rivers using the water quality model for conservative substances. The above models are coupled with the sequential monte carlo algorithm, and the risk probability analysis model for sudden water pollution in the plain river network is established and applied to the Yixing river network. The results show that (a) the risk probability of exceeding ammonia nitrogen standard (PES of ammonia nitrogen) is lower in the upper reaches and higher in the middle and lower reaches;(b) dynamic changes in pollutant concentration lead to different changes in the PES of ammonia nitrogen in each reach;(c) the differences in the simulated PES values between the sudden scheme and the stable scheme (NPES of ammonia nitrogen) in the upper and middle reaches show a patchy distribution of high and low values, which are related to the risk source location, the water movement direction and the concentration change in the reach after accepting pollutant loads from the risk sources;(d) the NPES of ammonia nitrogen in the lower reaches results from the coupling effect caused by accidental discharges from multiple risk sources;and (e) the different effects of the lower boundary hydrological conditions on the upstream water inflow lead to the different coupling effect on the water quality probability of sections in the downstream area.
Gears are widely used in machines to transmit torque from one shaft to another shaft and to change the speed of a power source. Gear failure is one of the major causes for mechanical transmission system breakdown. The...
详细信息
Gears are widely used in machines to transmit torque from one shaft to another shaft and to change the speed of a power source. Gear failure is one of the major causes for mechanical transmission system breakdown. Therefore, early gear faults must be immediately detected prior to its failure. Once early gear faults are diagnosed, gear remaining useful life (RUL) should be estimated to prevent any unexpected gear failure. In this paper, an intelligent prognostic system is developed for gear performance degradation assessment and RUL estimation. For gear performance degradation assessment, which aims to monitor current gear health condition, first, the frequency spectrum of gear acceleration error signal is mathematically analyzed to design a high-order complex Comblet for extracting gear fault related signatures. Then, two health indicators called heath indicator 1 and health indicator 2 are constructed to detect early gear faults and assess gear performance degradation, respectively, using two individual dynamic Bayesian networks. For gear RUL estimation, which aims to predict future gear health condition, a general sequential monte carlo algorithm is applied to iteratively infer gear failure probability density function (FPDF), which is used to predict gear residual lifetime. One case study is investigated to illustrate how the developed prognostic system works. The vibration data collected from a gearbox accelerated life test are used in this paper, where the gearbox started from a brand-new state, and ran until gear tooth failure. The results show that the developed prognostic system is able to detect early gear faults, track gear performance degradation, and predict gear RUL.
Directional wave spectra generally exhibit several peaks due to the coexistence of wind sea generated by local wind conditions and swells originating from distant weather systems. This paper proposes a new algorithm f...
详细信息
Directional wave spectra generally exhibit several peaks due to the coexistence of wind sea generated by local wind conditions and swells originating from distant weather systems. This paper proposes a new algorithm for partitioning such spectra and retrieving the various systems which compose a complex sea-state. It is based on a sequentialmonte-carloalgorithm which allows to follow the time evolution of the various systems. The proposed methodology is validated on both synthetic and real spectra and the results are compared with a method commonly used in the literature. (c) 2013 Elsevier Ltd. All rights reserved.
暂无评论