A Novel Approach for Shearer Cutting Load Identification through Integration of Improved Particle Swarm Optimization and Wavelet Neural Network

Wang, Zhongbin; Si, Lina; Tan, Chao; Liu, Xinhua

doi:10.1155/2014/521629

Cited by 14 publications

(11 citation statements)

References 24 publications

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“…To solve this kind of problem, evolutionary algorithms (EAs) are the common methods [9,[37][38][39]. Some distinct research in the scheduling context that can also provide some new ideas for FMS with AGVs [40][41][42]. The model proposed in this research will be optimized in three-dimensions, as mentioned earlier using three evolutionary algorithms (sectional encoding genetic algorithm (SE-GA), sectional encoding discrete particle swarm optimization (SE-DPSO) and hybrid sectional encoding genetic algorithm and discrete particle swarm optimization (H-SE-GA-DPSO)) and the corresponding experiments and a comparison among them are introduced.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective and Multi-Dimensional Optimization Scheduling Method Using a Hybrid Evolutionary Algorithms with a Sectional Encoding Mode

Guo

2019

Sustainability

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Aimed at the problem of the green scheduling problem with automated guided vehicles (AGVs) in flexible manufacturing systems (FMS), the multi-objective and multi-dimensional optimal scheduling process is defined while considering energy consumption and multi-function of machines. The process is a complex and combinational process, considering this characteristic, a mathematical model was developed and integrated with evolutionary algorithms (EAs), which includes a sectional encoding genetic algorithm (SE-GA), sectional encoding discrete particle swarm optimization (SE-DPSO) and hybrid sectional encoding genetic algorithm and discrete particle swarm optimization (H-SE-GA-DPSO). In the model, the encoding of the algorithms was divided into three segments for different optimization dimensions with the objective of minimizing the makespan and energy consumption of machines and the number of AGVs. The sectional encoding described the sequence of operations of related jobs, the matching relation between transfer tasks and AGVs (AGV-task), and the matching relation between operations and machines (operation-machine) respectively for multi-dimensional optimization scheduling. The effectiveness of the proposed three EAs was verified by a typical experiment. Besides, in the experiment, a comparison among SE-GA, SE-DPSO, H-SE-GA-DPSO, hybrid genetic algorithm and particle swarm optimization (H-GA-PSO) and a tabu search algorithm (TSA) was performed. In H-GA-PSO and TSA, the former just takes the sequence of operations into account, and the latter takes both the sequence of operations and the AGV-task into account. According to the result of the comparison, the superiority of H-SE-GA-DPSO over the other algorithms was proved.

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Section: Introductionmentioning

confidence: 99%

A Multi-Objective and Multi-Dimensional Optimization Scheduling Method Using a Hybrid Evolutionary Algorithms with a Sectional Encoding Mode

Guo

2019

Sustainability

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“…With the development of coal mining technology and stringent requirement for colliery safety, the automation of fully mechanized coal face has been inevitable trend. As a major coal mining machine, shearer plays a pivotal role in getting high-security and high efficiency of exploitation [ 1 , 2 ]. In order to realize the automated control of shearer, multiple coal-rock interface recognizing and tracking methods [ 3 – 6 ] were proposed, but these methods are not satisfactory because of the poor working conditions of coal mining such as narrow space, high coal dust, low visibility, and large noise [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

An Improved Genetic Fuzzy Logic Control Method to Reduce the Enlargement of Coal Floor Deformation in Shearer Memory Cutting Process

Tan

Wang

et al. 2016

Computational Intelligence and Neuroscience

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In order to reduce the enlargement of coal floor deformation and the manual adjustment frequency of rocker arms, an improved approach through integration of improved genetic algorithm and fuzzy logic control (GFLC) method is proposed. The enlargement of coal floor deformation is analyzed and a model is built. Then, the framework of proposed approach is built. Moreover, the constituents of GA such as tangent function roulette wheel selection (Tan-RWS) selection, uniform crossover, and nonuniform mutation are employed to enhance the performance of GFLC. Finally, two simulation examples and an industrial application example are carried out and the results indicate that the proposed method is feasible and efficient.

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“…Another concern is that many casualties occur in collieries. Therefore, it is necessary to efficiently and accurately identify shearer cutting conditions, which is becoming a challenging and significant research subject [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Data Fusion Identification for Shearer Cutting Conditions Based on Parallel Quasi-Newton Neural Networks and the Dempster-Shafer Theory

Wang

Liu

et al. 2015

Sensors

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In order to efficiently and accurately identify the cutting condition of a shearer, this paper proposed an intelligent multi-sensor data fusion identification method using the parallel quasi-Newton neural network (PQN-NN) and the Dempster-Shafer (DS) theory. The vibration acceleration signals and current signal of six cutting conditions were collected from a self-designed experimental system and some special state features were extracted from the intrinsic mode functions (IMFs) based on the ensemble empirical mode decomposition (EEMD). In the experiment, three classifiers were trained and tested by the selected features of the measured data, and the DS theory was used to combine the identification results of three single classifiers. Furthermore, some comparisons with other methods were carried out. The experimental results indicate that the proposed method performs with higher detection accuracy and credibility than the competing algorithms. Finally, an industrial application example in the fully mechanized coal mining face was demonstrated to specify the effect of the proposed system.

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A Novel Approach for Shearer Cutting Load Identification through Integration of Improved Particle Swarm Optimization and Wavelet Neural Network

Cited by 14 publications

References 24 publications

A Multi-Objective and Multi-Dimensional Optimization Scheduling Method Using a Hybrid Evolutionary Algorithms with a Sectional Encoding Mode

A Multi-Objective and Multi-Dimensional Optimization Scheduling Method Using a Hybrid Evolutionary Algorithms with a Sectional Encoding Mode

An Improved Genetic Fuzzy Logic Control Method to Reduce the Enlargement of Coal Floor Deformation in Shearer Memory Cutting Process

Multi-Sensor Data Fusion Identification for Shearer Cutting Conditions Based on Parallel Quasi-Newton Neural Networks and the Dempster-Shafer Theory

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