Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability

Nieto, P.J. Garcı́a; García–Gonzalo, Esperanza; Lasheras, Fernando Sánchez; Juez, Francisco Javier de Cos

doi:10.1016/j.ress.2015.02.001

Cited by 240 publications

(45 citation statements)

References 33 publications

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“…Compared with conventional algorithms, particle swarm optimization (PSO) is a simple and effective random algorithm to solve constrained problems [37,38]. As shown in Equation (13), the reliability-based and cost-oriented product optimization model has two optimization objectives, the maximum reliability and minimum cost, but they are negatively correlated.…”

Section: Adoption Of Cultural-based Dmopso Using Crowding Distance Somentioning

confidence: 99%

Reliability-Based and Cost-Oriented Product Optimization Integrating Fuzzy Reasoning Petri Nets, Interval Expert Evaluation and Cultural-Based DMOPSO Using Crowding Distance Sorting

Zhang

Feng

et al. 2017

Applied Sciences

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Featured Application: By integrating fuzzy reasoning Petri Nets, interval expert evaluation and cultural-based dynamic multi-objective particle swarm optimization (DMOPSO) using crowding distance sorting, the proposed reliability-based and cost-oriented product optimization method is effective to conduct the early product design. A reasonable reliability optimization design scheme can be output, and it is of important guiding significance for the product detailed design. The superiority of the proposed method lies in its outstanding ability to deal with the uncertainties information and dynamic product failure propagation. Abstract:In reliability-based and cost-oriented product optimization, the target product reliability is apportioned to subsystems or components to achieve the maximum reliability and minimum cost. Main challenges to conducting such optimization design lie in how to simultaneously consider subsystem division, uncertain evaluation provided by experts for essential factors, and dynamic propagation of product failure. To overcome these problems, a reliability-based and cost-oriented product optimization method integrating fuzzy reasoning Petri net (FRPN), interval expert evaluation and cultural-based dynamic multi-objective particle swarm optimization (DMOPSO) using crowding distance sorting is proposed in this paper. Subsystem division is performed based on failure decoupling, and then subsystem weights are calculated with FRPN reflecting dynamic and uncertain failure propagation, as well as interval expert evaluation considering six essential factors. A mathematical model of reliability-based and cost-oriented product optimization is established, and the cultural-based DMOPSO with crowding distance sorting is utilized to obtain the optimized design scheme. The efficiency and effectiveness of the proposed method are demonstrated by the numerical example of the optimization design for a computer numerically controlled (CNC) machine tool.

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Section: Adoption Of Cultural-based Dmopso Using Crowding Distance Somentioning

confidence: 99%

Reliability-Based and Cost-Oriented Product Optimization Integrating Fuzzy Reasoning Petri Nets, Interval Expert Evaluation and Cultural-Based DMOPSO Using Crowding Distance Sorting

Zhang

Feng

et al. 2017

Applied Sciences

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“…Compared with other EAs, the advantages of PSO are being simple, being easy to achieve, and few parameters to be adjusted. Using PSO with appropriate parameters can significantly improve the accuracy of SVM [25–28]. The formulas to update the primitive velocity and location are shown as follows:

\begin{matrix} T18 \\ {\vec{v}}_{i} ⟵ ω {\vec{v}}_{i} + \vec{U} (0, ϕ_{1}) \otimes ({\vec{p}}_{i} - {\vec{x}}_{i}) + \vec{U} (0, ϕ_{2}) \\ \otimes ({\vec{p}}_{g} - {\vec{x}}_{i}), \\ {\vec{x}}_{i} ⟵ {\vec{x}}_{i} + {\vec{v}}_{i}, \end{matrix}

where

{\vec{x}}_{i}

is the current location;

{\vec{p}}_{i}

is the previous personal best position;

{\vec{p}}_{g}

is the previous global best position;

{\vec{v}}_{i}

is velocity and ω is inertia weight;

\overset{U}{true} false(normal0, ϕ_{i} false)

represents a vector of random numbers uniformly distributed in [0, φ i ] which is randomly generated at each iteration and for each particle; ⊗ is componentwise multiplication…”

Section: Motion Recognition and Parameter Optimizationmentioning

confidence: 99%

An Interactive Astronaut-Robot System with Gesture Control

Liu

Luo

2016

Computational Intelligence and Neuroscience

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Human-robot interaction (HRI) plays an important role in future planetary exploration mission, where astronauts with extravehicular activities (EVA) have to communicate with robot assistants by speech-type or gesture-type user interfaces embedded in their space suits. This paper presents an interactive astronaut-robot system integrating a data-glove with a space suit for the astronaut to use hand gestures to control a snake-like robot. Support vector machine (SVM) is employed to recognize hand gestures and particle swarm optimization (PSO) algorithm is used to optimize the parameters of SVM to further improve its recognition accuracy. Various hand gestures from American Sign Language (ASL) have been selected and used to test and validate the performance of the proposed system.

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“…The velocity of the particle determines its moving direction and distance by adjusting dynamically according to the movement experience of its own and other particles, and realizes the individual optimization in the feasible solution space [13].…”

Section: Pso Algorithm Study On Svm Temperature Compensation Of Liquimentioning

confidence: 99%

“…Each particle moves in the solution space, and the individual position is updated by tracking individual extremum and swarm extremum. Individual extremum refers to the optimal position of the fitness value obtained in the positions the individual experienced [12,13]. Swarm extremum refers to the optimal location of the fitness of all particles in the population.…”

Section: Pso Algorithm Study On Svm Temperature Compensation Of Liquimentioning

confidence: 99%

See 1 more Smart Citation

Study on SVM Temperature Compensation of Liquid Ammonia Volumetric Flowmeter Based on Variable Weight Pso

Lin¹,

Ping²,

Gao³

et al. 2015

IJHT

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When the quality of liquid ammonia is measured by volumetric flowmeter, the traditional quadratic expression method can't meet the accuracy of temperature compensation in modern coal chemical industry. So the temperature compensation method by support vector machine (SVM) regression is presented, and kernel function parameter σ of SVM is optimized by variable weight particle swarm optimization (PSO). After the performance analysis and comparison in PSO, the suitable linear inertia weight method is selected. Experimental results show that the temperature compensation accuracy of the SVM method based on the variable weight PSO is significantly higher than that of the traditional quadratic expression method.

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Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability

Cited by 240 publications

References 33 publications

Reliability-Based and Cost-Oriented Product Optimization Integrating Fuzzy Reasoning Petri Nets, Interval Expert Evaluation and Cultural-Based DMOPSO Using Crowding Distance Sorting

Reliability-Based and Cost-Oriented Product Optimization Integrating Fuzzy Reasoning Petri Nets, Interval Expert Evaluation and Cultural-Based DMOPSO Using Crowding Distance Sorting

An Interactive Astronaut-Robot System with Gesture Control

Study on SVM Temperature Compensation of Liquid Ammonia Volumetric Flowmeter Based on Variable Weight Pso

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