Sequential Monte Carlo Method Toward Online RUL Assessment with Applications

Online assessment of remaining useful life(RUL) of a system or device has been widely studied for performance reliability, production safety, system conditional maintenance, and decision in remanufacturing engineering. However,there is no consistency framework to solve the RUL recursive estimation for the complex degenerate systems/device.In this paper, state space model(SSM) with Bayesian online estimation expounded from Markov chain Monte Carlo(MCMC) to Sequential Monte Carlo(SMC) algorithm is presented in order to derive the optimal Bayesian estimation.In the context of nonlinear & non-Gaussian dynamic systems, SMC(also named particle filter, PF) is quite capable of performing filtering and RUL assessment recursively. The underlying deterioration of a system/device is seen as a stochastic process with continuous, nonreversible degrading. The state of the deterioration tendency is filtered and predicted with updating observations through the SMC procedure. The corresponding remaining useful life of the system/device is estimated based on the state degradation and a predefined threshold of the failure with two-sided criterion. The paper presents an application on a milling machine for cutter tool RUL assessment by applying the above proposed methodology. The example shows the promising results and the effectiveness of SSM and SMC online assessment of RUL.

SMC-PHD based multi-target track-before-detect with nonstandard point observations model

Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method was proposed to tackle this issue using a nonstandard point observation model. The method was developed from sequential Monte Carlo(SMC)-based probability hypothesis density(PHD) filter, and it was implemented by modifying the original calculation in update weights of the particles and by adopting an adaptive particle sampling strategy. To efficiently execute the SMC-PHD based TBD method, a fast implementation approach was also presented by partitioning the particles into multiple subsets according to their position coordinates in 2D resolution cells of the sensor. Simulation results show the effectiveness of the proposed method for time-varying multi-target tracking using raw observation data.

Particle flters for probability hypothesis density flter with the presence of unknown measurement noise covariance

In Bayesian multi-target fltering,knowledge of measurement noise variance is very important.Signifcant mismatches in noise parameters will result in biased estimates.In this paper,a new particle flter for a probability hypothesis density（PHD）flter handling unknown measurement noise variances is proposed.The approach is based on marginalizing the unknown parameters out of the posterior distribution by using variational Bayesian（VB）methods.Moreover,the sequential Monte Carlo method is used to approximate the posterior intensity considering non-linear and non-Gaussian conditions.Unlike other particle flters for this challenging class of PHD flters,the proposed method can adaptively learn the unknown and time-varying noise variances while fltering.Simulation results show that the proposed method improves estimation accuracy in terms of both the number of targets and their states.

Reliability Assessment of Integrated Electricity and Natural Gas Transmission Systems in Presence of Power-to-gas and Combined Heat and Power Technologies

Assessing the reliability of integrated electricity and gas systems has become an important issue due to the strong dependence of these energy networks through the power-to-gas(P2G)and combined heat and power(CHP)technologies.The current work,initially,presents a detailed energy flow model for the integrated power and natural gas system in light of the P2G and CHP technologies.Considering the simultaneous load flow of networks,a contingency analysis procedure is proposed,and reliability is assessed through sequential Monte Carlo simulations.The current study examines the effect of independent and dependent operation of energy networks on the reliability of the systems.In particular,the effect of employing both P2G and CHP technologies on reliability criteria is evaluated.In addition,a series of sensitivity analysis are performed on the size and site of these technologies to investigate their effects on system reliability.The proposed method is implemented on an integrated IEEE 24-bus electrical power system and 20-node Belgian natural gas system.The simulation procedure certifies the proposed method for reliability assessment is practical and applicable.In addition,the results prove connection between energy networks through P2G and CHP technologies can improve reliability of networks if the site and size of technologies are properly determined.