The use of artificial neural network (ANN) for modeling the useful life of the failure assessment in blades of steam turbines

Rodríguez, José Ignacio Herrera; Hamzaoui, Youness El; Hernández, J.A.; García, J. C.; Flores, J.E.; Tejeda, A.L.

doi:10.1016/j.engfailanal.2013.05.002

Cited by 59 publications

(39 citation statements)

References 30 publications

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“…The output of ANN is RUL prediction or performance degradation assessment, which is used for conducting effective maintenance strategies. ANNs widely used in fault prediction include BPNN [91][92][93][94][95], radial basis function network (RBFN), and RNN [96]. Ahmadzadeh, et al [94], proposed a three-layer feedforward BPNN for RUL estimation of grinding mill liners, which considered degeneration and condition monitoring data as the inputs of ANN, and used RUL as the output of ANN.…”

Section: Annmentioning

confidence: 99%

“…Ahmadzadeh, et al [94], proposed a three-layer feedforward BPNN for RUL estimation of grinding mill liners, which considered degeneration and condition monitoring data as the inputs of ANN, and used RUL as the output of ANN. Rodriguez, et al [95], presented ANN (six input layers, three hidden layers, and one output layer) to predict and simulate the behavior of life-cycle assessment in blades of steam turbines. In view of the shortcomings of traditional incremental training methods in long-term prediction, Malhi, et al [96], proposed an RNN based on competitive learning method to improve the accuracy in long-term prediction of rolling bearings.…”

Section: Annmentioning

confidence: 99%

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Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

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Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

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Section: Annmentioning

confidence: 99%

Section: Annmentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

View full text Add to dashboard Cite

show abstract

“…ANNs have been used in many engineering disciplines such as materials [50,[59][60][61][62][63][64], biochemical engineering [65], medicine [66] and mechanical engineering [67][68][69][70][71].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…This method can be used to determine fuel consumption by taking into consideration a number of variables that influence the fuel consumption of haul trucks. ANNs have been used in many engineering disciplines such as materials [50,[59][60][61]63], biochemical engineering [65], medicine [66] and mechanical engineering [67,68,215]. ANNs are desirable solutions for complex problems as they can interpret the compound relationships between the multiple parameters involved in a problem.…”

Section: Artificial Neural Network Modelmentioning

confidence: 99%

“…One of the main advantages of ANNs is that they can simulate both linear and nonlinear relationships between parameters, using the information provided to train the network. ANNs, also known as parallel distributed processing, are the representation of methods that the brain uses for learning [68]. They are a series of mathematical models that imitate a few of the known characteristics of natural nerve systems and draw on the analogies of adaptive natural learning.…”

Section: Artificial Neural Network Modelmentioning

confidence: 99%

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Development of an advanced data analytics model to improve the energy efficiency of haul trucks in surface mines

Soofastaei¹

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Truck haulage is responsible for a majority of cost in a surface mining operation. Diesel fuel, which is costly and has a significant environmental footprint, is used as a source of energy for haul trucks in surface mines. Reducing diesel fuel consumption would lead to a reduction in haulage cost and greenhouse gas emissions. The determination of fuel consumption is complex and requires multiple parameters including the mine, fleet, truck, fuel, climate and road conditions as input. Data analytics is used to simulate the complex relationships between the input parameters affecting the truck fuel consumption. This technique is also used to optimise the input parameters to minimise the fuel consumption without losing productivity or further capital expenditure for a specific surface mining operation.The aim of this research thesis is to develop an advanced data analytics model to improve the energy efficiency of haul trucks in surface mines. The most important controllable parameters affecting fuel consumption are first identified, namely payload, truck speed and total resistance. These parameters are selected based on the results of an online survey. A comprehensive analytical framework is developed to determine the opportunities for minimising the truck fuel consumption. The first stage of the analytical framework includes the development of an artificial neural network model to determine the relationship between truck fuel consumption and payload, truck speed and total resistance. This model is trained and tested using real data collected from some large surface mines in USA and Australia. A fitness function for the haul truck fuel consumption is successfully generated. This fitness function is then used in the second stage of the analytical framework to develop a computerised learning algorithm based on a novel multi-objective genetic algorithm. The aim of this algorithm is to estimate the optimum values of the three effective parameters to reduce the diesel fuel consumption.The following studies are also conducted to enhance the analysis of haul truck fuel consumption.First, a comprehensive investigation of loading variance influence on fuel consumption and gas emissions in mine haulage operation is carried out. Then, a discrete-event model to simulate the effect of payload variance on truck bunching, cycle time and hauled mine materials is developed. The influence of rolling resistance on haul truck fuel consumption in surface mines is investigated.

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Improved back propagation neural network based on the enrichment for the crack propagation

Wang,

Ye,

Yang

et al. 2023

Numerical Meth Engineering

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Numerical methods have been extensively applied to fracture mechanics, while they cannot simulate the problems without the mechanical models or constitutive equations. Artificial neural networks (ANNs) can be utilized to predict complex fracture problems, but these approaches require large amounts of data for the training. Therefore, in this paper, to combine the advantages of the numerical methods and the ANNs, an improved back propagation neural network (BPNN) is proposed by introducing the enrichment used in the numerical methods into the activation function utilized in the neural networks. The enrichment is able to represent the crack tip field which can accelerate the convergence. At the field near the crack tip, the improved BP solution can converge to the analytical solutions which validate the high accuracy of the proposed method. Without sufficient data, especially the data are missing in the near field of the crack tip, the improved BP method can also achieve high accuracy and convergence, while the conventional BP method may not converge to the predetermined error bound. Numerical simulations of the quasi‐static and fatigue crack problems demonstrate that the improved BP method can accurately predict the crack propagation and its growth rate with relatively little data.

show abstract

The use of artificial neural network (ANN) for modeling the useful life of the failure assessment in blades of steam turbines

Cited by 59 publications

References 30 publications

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Development of an advanced data analytics model to improve the energy efficiency of haul trucks in surface mines

Improved back propagation neural network based on the enrichment for the crack propagation

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