Remaining useful life prediction of integrated modular avionics using ensemble enhanced online sequential parallel extreme learning machine

Gao, Zehai; Ma, Cunbao; Zhang, Jianfeng; Xu, Guolei

doi:10.1007/s13042-021-01283-y

Cited by 12 publications

(6 citation statements)

References 39 publications

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“…Therefore, in the resource allocation process, safety-critical functions should be assigned to resource modules with a low fault propagation impact factor and low node relative importance as much as possible to minimize the overall fault propagation risk of the resource layer network. Considering the above factors affecting the resource allocation scheme, the following objective function can be established by combining Equation (8): simulates the genetic processes of biological organisms in nature according to the evolutionary law of survival of the fittest and is an optimal solution search method based on the principles of natural selection and genetics [33]. The algorithm was originally proposed by Professor Holland of the University of Michigan [34].…”

Section: Modeling Of Resource Allocation Methods Consideringmentioning

confidence: 99%

“…The IMA system employs a shared resource platform to load software for hosted functions, and the shared mechanism simplifies the equipment development and validation process and improves resource utilization by assembling modular and common physical resources [7]. Different from the dedi-cated resources of "one function and one set of equipment" in the federated avionics system, the function integration and resource sharing characteristics of the IMA system determine that the mapping relationship between the logical layer functions and the physical layer resources is complex and diverse, causing the problem of multiple hosted functions competing for limited general resources [8]. Therefore, a reasonable resource allocation method becomes the key to ensure the effective and reliable execution of the avionics system functions.…”

Section: Introductionmentioning

confidence: 99%

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Research on Resource Allocation Method of Integrated Avionics System considering Fault Propagation Risk

Zhao

Zhang

Dong

et al. 2022

International Journal of Aerospace Engineering

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The integrated modular avionics (IMA) system has coupled cross-linked support characteristics between physical resource entities and logical functions, and the existing resource allocation methods, which mainly consider system performance and resource utilization, do not consider the associated impact of fault propagation among resource entities. Aiming at the fault propagation risk in the resource allocation process of the IMA system, a hierarchical model of the IMA system is established. The fault propagation behavior caused by coupling association during the physical realization of the function layer logical architecture is analyzed, and the impact of different resource allocation methods on the fault propagation behavior is determined. Secondly, resource capacity constraints are established according to the resource requirement for hosted function, and the fault propagation risk model of the IMA system is constructed by considering the fault propagation impact factor, the relative importance of nodes, and the function safety criticality. The resource allocation method is evaluated according to the fault propagation risk model, and a heuristic algorithm is applied to optimize the resource allocation method of the IMA system. The simulation results show that the average propagation probability of the optimized resource allocation scheme decreases by 17.4%, and the overall fault propagation risk of the resource network decreases by 50.3%, indicating that the proposed resource allocation method can effectively improve the safety of the IMA system.

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Section: Modeling Of Resource Allocation Methods Consideringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Resource Allocation Method of Integrated Avionics System considering Fault Propagation Risk

Zhao

Zhang

Dong

et al. 2022

International Journal of Aerospace Engineering

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show abstract

“…In another study [49], the authors implement a parallel OS-ELM algorithm for particulate matter prediction. In a study on the prediction of avionics [50], a core system of modern aircraft is made using ensemble-enhanced OS-ELM. Finally, in another study [51] a regularized mixed-norm OS-ELM (MRO-ELM) algorithm accelerated with a parallel GPU is proposed that outperforms the standard OS-ELM version.…”

Section: Elm Variants Implemented By Using Distributed and Parallel C...mentioning

confidence: 99%

A Review on Large-Scale Data Processing with Parallel and Distributed Randomized Extreme Learning Machine Neural Networks

Gelvez-Almeida,

Mora,

Barrientos

et al. 2024

MCA

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The randomization-based feedforward neural network has raised great interest in the scientific community due to its simplicity, training speed, and accuracy comparable to traditional learning algorithms. The basic algorithm consists of randomly determining the weights and biases of the hidden layer and analytically calculating the weights of the output layer by solving a linear overdetermined system using the Moore–Penrose generalized inverse. When processing large volumes of data, randomization-based feedforward neural network models consume large amounts of memory and drastically increase training time. To efficiently solve the above problems, parallel and distributed models have recently been proposed. Previous reviews of randomization-based feedforward neural network models have mainly focused on categorizing and describing the evolution of the algorithms presented in the literature. The main contribution of this paper is to approach the topic from the perspective of the handling of large volumes of data. In this sense, we present a current and extensive review of the parallel and distributed models of randomized feedforward neural networks, focusing on extreme learning machine. In particular, we review the mathematical foundations (Moore–Penrose generalized inverse and solution of linear systems using parallel and distributed methods) and hardware and software technologies considered in current implementations.

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“…The ELM model consists of an input layer, a hidden layer in the center, and a six-neuron output layer to output the predicted six blockage categories. The ELM is a single-hidden-layer neural network algorithm, which can be used for the establishment of regression and classification models [21]. Compared with the traditional single hidden layer feed-forward neural network, the input weight and the offset are random.…”

Section: Extended Elm Model Structure Designmentioning

confidence: 99%

Machine Learning-Based Satellite Routing for SAGIN IoT Networks

Yuan

Liu

et al. 2022

Electronics

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Due to limited coverage, radio access provided by ground communication systems is not available everywhere on the Earth. It is necessary to develop a new three-dimensional network architecture in a bid to meet various connection requirements. Space–air–ground integrated networks (SAGINs) offer large coverage, but the communication quality of satellites is often compromised by weather conditions. To solve this problem, we propose an extended extreme learning machine (ELM) algorithm in this paper, which can predict the communication attenuation caused by rainy weather to satellite communication links, so as to avoid large path loss caused by bad weather conditions. Firstly, we use Internet of Things (IoT)-enabled sensors to collect weather-related data. Then, the system feeds the data to the extended ELM model to obtain a category prediction for blockage caused by weather. Finally, this information helps the selection of the data transmission link and thus improves the satellite routing performance.

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Remaining useful life prediction of integrated modular avionics using ensemble enhanced online sequential parallel extreme learning machine

Cited by 12 publications

References 39 publications

Research on Resource Allocation Method of Integrated Avionics System considering Fault Propagation Risk

Research on Resource Allocation Method of Integrated Avionics System considering Fault Propagation Risk

A Review on Large-Scale Data Processing with Parallel and Distributed Randomized Extreme Learning Machine Neural Networks

Machine Learning-Based Satellite Routing for SAGIN IoT Networks

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