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Because of the influence of harsh and variable working environments, the vibration signals of rolling bearings for combine harvesters usually show obvious characteristics of strong non-stationarity and nonlinearity. Accomplishing accurate fault diagnosis using these signals for rolling bearings is a challenging subject. In this paper, a novel fault diagnosis method based on composite-scale-variable dispersion entropy (CSvDE) and self-optimization variational mode decomposition (SoVMD) is proposed, systematically combining the nonstationary signal analysis approach and machine learning technology. Firstly, an improved SoVMD algorithm is developed to realize adaptive parameter optimization and to further extract multiscale frequency components from original signals. Subsequently, a CSvDE-based feature learning model is established to generate the multiscale fault feature space (MsFFS) of frequency components for the improvement of fault feature learning ability. Finally, the generated MsFFS can serve as the inputs of the Softmax classifier for fault category identification. Extensive experiments on the vibration datasets collected from rolling bearings of combine harvesters are conducted, and the experimental results demonstrate the more superior and robust fault diagnosis performance of the proposed method compared to other existing approaches. [ABSTRACT FROM AUTHOR]

Fault identification under variable operating conditions is a task of great importance and challenge for equipment health management. However, when dealing with this kind of issue, traditional fault diagnosis methods based on the assumption of the distribution coherence of the training and testing set are no longer applicable. In this paper, a novel state identification method integrated by time-frequency decomposition, multi-information entropies, and joint distribution adaptation is proposed for rolling element bearings. At first, fast ensemble empirical mode decomposition was employed to decompose the vibration signals into a collection of intrinsic mode functions, aiming at obtaining the multiscale description of the original signals. Then, hybrid entropy features that can characterize the dynamic and complexity of time series in the local space, global space, and frequency domain were extracted from each intrinsic mode function. As for the training and testing set under different load conditions, all data was mapped into a reproducing space by joint distribution adaptation to reduce the distribution discrepancies between datasets, where the pseudolabels of the testing set and the final diagnostic results were obtained by the k-nearest neighbor algorithm. Finally, five cases with the training and testing set under variable load conditions were used to demonstrate the performance of the proposed method, and comparisons with some other diagnosis models combined with the same features and other dimensionality reduction methods were also discussed. The analysis results show that the proposed method can effectively recognize the multifaults of rolling element bearings under variable load conditions with higher accuracies and has sound practicability. [ABSTRACT FROM AUTHOR]

In this study, we developed a hybrid form of rainfall-runoff model by integrating the variable infiltration capacity (VIC) model with a wavelet neural network (WNN) based on the binary gravitational search algorithm (BGSA). The streamflow of each subbasin in the Jinshajiang River Basin was first simulated by VIC model, then the simulated runoff of each subbasin and antecedent total basin runoff were decomposed via discrete wavelet transformation into a number of subseries components with different time scales. Finally, BGSA was employed to optimize the number of hidden layers and identify the appropriate subset of WNN inputs from a set of candidate subseries components. The proposed VIC_BGSA_WNN model was then compared to the traditional VIC model and reference methods based on correlation to determine effective wavelet components, and results indicated that our approach is feasible and effective. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms require large number of function evaluations to locate the global optimum, making it computationally prohibitive on dealing with expensive problems. Surrogate-based optimization methods have shown promising ability on accelerating the convergence speed. However, it is still a challenging work for surrogate-assisted methods to deal with high-dimensional expensive problems because it is hard to approximate the objective function in high-dimensional space. In this paper, a novel radial basis function surrogate model assisted evolutionary algorithm for high-dimensional expensive optimization problems (RSAEH) is proposed. Specifically, the proposed algorithm consists of local search part and surrogate-guided prescreening part. In the local search part, the local surrogate is built by radial basis function with the most promising training sample points, and the optima (or near-optima) is located by optimizer to conduct exact function evaluation. In the surrogate-guided prescreening part, the current best sample point is refined by using sequential quadratic programming, thus guide the mutation direction by using differential evolution operator, and promising offspring prescreened by surrogate model are evaluated using exact function evaluation. To validate the effectiveness of the proposed algorithm, it is tested on benchmark problems with dimension ranging from 30 to 100, as well as a real-world oil reservoir production optimization problem. The proposed algorithm achieved best optimization results on 16 benchmark functions among 21 benchmark function sets in comparison with other algorithms. The performance of RSAEH is competitive especially on 100-D benchmark functions. In addition, RSAEH also showed promising performance on a real-world oil reservoir production optimization problem with 160 variables, in comparison with several state-of-the-art algorithms. • A novel radial basis function surrogate model assisted evolutionary algorithm RSAEH is proposed. • The main contribution is that the proposed RSAEH adopt SQP to guide the mutation direction of DE operator. • RSAEH achieved best optimization results on 16 benchmark functions among 21 benchmark function sets. • RSAEH showed promising performance on oil reservoir production optimization problem with 160 variables. [ABSTRACT FROM AUTHOR]

With increasing wind or solar farm and pump storage plant integrations, more and more large-scale renewable energy integration systems are taking shape gradually, which brings up challenges to optimized control these systems. Especially, due to the reversible properties of pumped storage hydro unit (PSHU), it is apt to fall into the ‘S’ characteristic area which can lead to a wide range of unit rotational speed oscillation under no-load condition at middle-low water head. In order to solve these unstable running problems effectively, an adaptively fast fuzzy fractional order PID (AFFFOPID) control method for PSHU is proposed in this paper. A combination of fuzzy logic controller and fractional order PID is adopted by the proposed control method, and error tracking element of PSHU guide vane opening is added to improve the regulation rate. Then, an accurate mathematical model of pump-turbine with complete characteristic curves is established for the PSHU regulating system (PSHURS). Meantime, a novel bacterial-foraging chemotaxis gravitational search algorithm (BCGSA) is used for optimized parameters selection of AFFFOPID method. The BCGSA which is based on the standard gravitational search algorithm accelerates convergence speed with a combination of the P best – G best -guided strategy and adaptive elastic-ball method. Chemotaxis operator of bacterial foraging algorithm is also added into the BCGSA which is devised to step out the local optimal with a certain probability. Furthermore, simulation tests under different running conditions are utilized to demonstrate the feasibility and robustness of the AFFFOPID. The results are analyzed in detail, which demonstrates the pump-turbine model and the proposed AFFFOPID method are practicable. Comparison with other methods clearly shows that AFFFOPID using BCGSA outperforms the others in most cases. [ABSTRACT FROM AUTHOR]

Data-driven surrogate models, which are trained by samples to replace time-consuming numerical simulations, have been widely used to solve production optimization problems in recent years. It is a challenging and meaningful subject to research advanced surrogate-model-based methods that can obtain superior optimization performance within a limited time budget. The key is to enhance the quality of each training sample, i.e., the contribution of each sample to the overall optimization performance improvement, because the acquisition of each sample requires a numerical simulation that generally costs tens of minutes or even several hours. To obtain samples with enhanced quality, a novel approach named surrogate-reformulation-assisted multitasking knowledge transfer (SRAMT) for production optimization is proposed in this research. Multiple surrogate models, which can imitate the landscape of the initial production optimization problem, are constructed with diverse samples as reformulations of the target problem. These models reflect different landscape information of the same problem and thus can be regarded as multiple associated optimization instances, which just provide a solid foundation for the subsequent process. Then, an advanced optimization method, namely multitasking optimization (MTO), is leveraged to find optimal solutions for these surrogates. MTO can handle several optimization instances simultaneously and boost the performance of each one by transferring useful knowledge among tasks. Besides, in the absence of prior knowledge about the target production optimization task, as in most situations, an approach is proposed to determine the frequency of knowledge transfer adaptively based on the similarity between surrogates to improve efficiency and stability. To verify the effect, four 100-dimensional benchmark functions and two reservoir models are tested on the method and the results are in comparison with those obtained by differential evolution (DE) algorithm and three other surrogate-model-based methods. The results show that the proposed method can achieve optimal well controls which can get the highest net present value (NPV) for target production optimization problems and have superior convergence speed. • A method based on surrogate reformulation and multitasking enhancement is proposed. • Knowledge is transferred among two data-driven surrogate models for better samples. • The transfer frequency is controlled adaptively to avoid the negative transfer. • The method is applied to four benchmark functions and two reservoir examples. [ABSTRACT FROM AUTHOR]

In recent years, evolutionary computation (EC) has gained increasing attention in the field of production optimization due to its powerful global search ability and derivative-free characteristic. However, state constraints are much more challenging to handle in comparison with the explicit constraints. To alleviate this difficulty, this paper presents a framework called surrogate-assisted differential evolution with an effective constraint-handling technique—the feasibility rule with the incorporation of objective function information (SADE-FROFI). The novel constraint-handling technique used in this work achieves an effective balance between constraints and objective function by revising the well-known feasibility rule (FR), which can effectively maintain the diversity of population and help the population jump out of local optimal solution. Moreover, to address the computationally time-consuming issue of numerical simulator, a multi-surrogate strategy is introduced specially for state constraints. Two benchmark functions are tested to verify the effectiveness of new constraint-handling technique (FROFI). The efficacy of the proposed method is validated on two synthetic reservoir models, named three-channel model and PUNQ-S3 model, respectively. The experimental results demonstrated that the proposed method can find better solutions on the basis of satisfying all constraints in comparison with several existing methods. • A new constrained production optimization framework is proposed based on surrogate-assisted algorithm and novel constraint-handling technique. • The new methodology is successfully applied to two constrained production optimization problems. • The optimization results of two reservoir models show the great performance of the proposed method. [ABSTRACT FROM AUTHOR]