An Adaptive Particle Filtering-based Framework for Real-time Fault Diagnosis and Failure Prognosis of Environmental Control Systems
نویسندگان
چکیده
Maintenance of critical or/complex systems has recently moved from traditional preventive maintenance to Condition Based Maintenance (CBM) exploiting the advances both in hardware (sensors / DAQ cards, etc.) and in software (sophisticated algorithms blending together the state of the art in signal processing and pattern analysis). Along this path, Environmental Control Systems and other critical systems/processes can be improved based on concepts of anomaly detection, fault diagnosis and failure prognosis. The enabling technologies borrow from the fields of modeling, data processing, Bayesian estimation theory and in particular a technique called particle filtering. The efficiency of the diagnostic approach is demonstrated via simulation results.
منابع مشابه
A Robust Adaptive Observer-Based Time Varying Fault Estimation
This paper presents a new observer design methodology for a time varying actuator fault estimation. A new linear matrix inequality (LMI) design algorithm is developed to tackle the limitations (e.g. equality constraint and robustness problems) of the well known so called fast adaptive fault estimation observer (FAFE). The FAFE is capable of estimating a wide range of time-varying actuator fault...
متن کاملA Particle Filtering Approach for On-Line Failure Prognosis in a Planetary Carrier Plate
This paper introduces an on-line particle-filtering-based framework for failure prognosis in nonlinear, non-Gaussian systems. This framework uses a nonlinear state-space model of the plant (with unknown time-varying parameters) and a particle filtering (PF) algorithm to estimate the probability density function (pdf) of the state in real-time. The state pdf estimate is then used to predict the ...
متن کاملA Particle Filtering Framework for Failure Prognosis
Bayesian estimation techniques are finding application domains in machinery fault diagnosis and prognosis of the remaining useful life of a failing component/subsystem. This paper introduces a methodology for accurate and precise prediction of a failing component based on particle filtering and learning strategies. This novel approach employs a state dynamic model and a measurement model to pre...
متن کاملAparticle-filteringapproachforon-line fault diagnosis and failure prognosis
This paper introduces an on-line particle-filtering (PF)-based framework for fault diagnosis and failure prognosis in non-linear, non-Gaussian systems. This framework considers the implementation of two autonomous modules. A fault detection and identification (FDI) module uses a hybrid state-space model of the plant and a PF algorithm to estimate the state probability density function (pdf) of ...
متن کاملOnline Fault Detection and Isolation Method Based on Belief Rule Base for Industrial Gas Turbines
Real time and accurate fault detection has attracted an increasing attention with a growing demand for higher operational efficiency and safety of industrial gas turbines as complex engineering systems. Current methods based on condition monitoring data have drawbacks in using both expert knowledge and quantitative information for detecting faults. On account of this reason, this paper proposes...
متن کامل