Fault diagnosis using particle filter for MEA typical components

Li, Hongliang; Yannian, Hui; Jianglei, Qu; Sun, Hao

doi:10.1049/joe.2018.0028

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Although particle filter algorithms are widely used in robotics and electronics [4,5], their utilization in chemical processes is far less. The method has been used for single continuous stirred tank reactors (CSTRs) a couple of times [6,7], mainly for the purposes of process control [8,9], but rarely applied in cascade reactors and fault diagnostics.…”

Section: Introductionmentioning

confidence: 99%

Goal-Oriented Tuning of Particle Filters for the Fault Diagnostics of Process Systems

Kenyeres

Abonyi

2023

Processes

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This study introduces particle filtering (PF) for the tracking and fault diagnostics of complex process systems. In process systems, model equations are often nonlinear and environmental noise is non-Gaussian. We propose a method for state estimation and fault detection in a wastewater treatment system. The contributions of the paper are the following: (1) A method is suggested for sensor placement based on the state estimation performance; (2) based on the sensitivity analysis of the particle filter parameters, a tuning method is proposed; (3) a case study is presented to compare the performances of the classical PF and intelligent particle filtering (IPF) algorithms; (4) for fault diagnostics purposes, bias and impact sensor faults were examined; moreover, the efficiency of fault detection was evaluated. The results verify that particle filtering is applicable and highly efficient for tracking and fault diagnostics tasks in process systems.

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Section: Introductionmentioning

confidence: 99%

Goal-Oriented Tuning of Particle Filters for the Fault Diagnostics of Process Systems

Kenyeres

Abonyi

2023

Processes

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“…[4], [5]. The faults of auto-transformer unit and transformer rectifier unit in aircraft electrical power system has been detected by particle filtering state estimation and smoothed residual in [6]. The parity space-based approach has solved the fault detection for discrete-time switched systems under dwell-time switching.…”

Section: Introductionmentioning

confidence: 99%

Real-Time FPGA-Based Hardware Neural Network for Fault Detection and Isolation in More Electric Aircraft

et al. 2019

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More Electric Aircraft (MEA) has a great development prospect in the aviation industry thanks to the progressive power electronics technology and digital systems. Nevertheless, any fault occurring in the power electric system of MEA could be a fatal risk for the safety of the aircraft. To deal with the real-time fault detection and isolation (FDI) on the MEA, we put forward an FPGA-based neural network method which includes two stages: off-line construction using TensorFlow and real-time monitoring on the FPGA. Long Short-Term Memory (LSTM) network is applied because of its capability of learning from the longterm historical information in time series. A comprehensive MEA model based on the Boeing 787 power system created in PSCAD/EMTDC and a commercial electric aircraft model based on Airbus E-Fan in Simscape are simulated to validate the effectiveness and generality of our proposed method. Adequate contrast experiments are conducted to acquire the most applicable architecture and configurations of the network. The results illustrate that the evaluation of the real-time condition can achieve accuracy over 99.5% within one sampling time on FPGA and reasonable hardware resource utilization. INDEX TERMS Fault detection and isolation (FDI), field-programmable gate array (FPGA), gated recurrent unit (GRU), long short-term memory (LSTM), more electric aircraft (MEA), neural networks, real-time systems.

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