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Detection for weak fault in planetary gear trains based on an improved maximum correlation kurtosis deconvolution

MEASUREMENT SCIENCE AND TECHNOLOGY 2020年第2期31卷 025101-025101页

作者： Zhang, Jianqun Zhong, Min Zhang, Jun Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350116 Fujian Peoples R China

Weak faults in planetary gear trains are difficult to detect due to the interference of background noise and the amplitude modulation effect. In order to detect weak faults in planetary gearboxes, this paper proposes a comprehensive diagnostic methodology, referred to as improved maximum correlation kurtosis deconvolution (IMCKD). The proposed diagnostic methodology combines MCKD with variational mode decomposition (VMD) and the grasshopper optimization algorithm (GOA) to solve the difficulty of selecting MCKD parameters. An optimal mode obtained from VMD is selected to determine the deconvolution period of T in MCKD. The filter length L and the number of shifts M are determined by GOA. The effectiveness of the IMCKD methodology is proven by the successful detection of an incipient root crack in the sun gear of a single stage planetary gearbox. The advantages of the proposed IMCKD method are further highlighted by a comparison between two illustrious weak fault diagnosis methods, MED-SK and PSO-SR.

关键词： planetary gear train weak fault diagnosis maximum correlated kurtosis deconvolution variational mode decomposition grasshopper optimization algorithm

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Real-Time Construction Simulation Coupling a Concrete Temperature Field Interval Prediction Model with Optimized Hybrid-Kernel RVM for Arch Dams

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ENERGIES 2020年第17期13卷 4487页

作者： Song, Wenshuai Guan, Tao Ren, Bingyu Yu, Jia Wang, Jiajun Wu, Binping Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety Tianjin 300350 Peoples R China

Joint grouting simulation is an important aspect of arch dam construction simulation. However, the current construction simulation model simplifies the temperature factors in joint grouting simulation, which leads to the difference between the simulation results and the actual construction schedule. Furthermore, the majority of existing temperature prediction research is based on deterministic point predictions, which cannot quantify the uncertainties of the prediction values. Thus, this study presents a real-time construction simulation method coupling a concrete temperature field interval prediction model to address these problems. First, a real-time construction simulation model is established. Secondly, this paper proposes a concrete temperature interval prediction method based on the hybrid-kernel relevance vector machine (HK-RVM) with the improved grasshopper optimization algorithm (IGOA). The hybrid-kernel method is adopted to ensure the prediction accuracy and generalization ability of the model. Additionally, the improved grasshopper optimization algorithm (IGOA), which utilizes the tent chaotic map and cosine adaptive method to improve the algorithm performance, is developed for the parameter optimization of HK-RVM. Thirdly, concept drift detection based on variable window technology is proposed to update the prediction model. Finally, an arch dam project in China is used as a case study, by which the superiority and applicability of the proposed method are proven.

关键词： arch dam construction simulation concrete temperature field interval prediction relevance vector machine grasshopper optimization algorithm concept drift

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Prioritizing and queueing the emergency departments' patients using a novel data-driven decision-making methodology, a real case study

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 195卷 116568-116568页

作者： Alipour-Vaezi, Mohammad Aghsami, Amir Jolai, Fariborz Univ Tehran Coll Engn Sch Ind & Syst Engn POB 11155-4563 Tehran Iran KN Toosi Univ Technol KNTU Sch Ind Engn Tehran Iran

One of the principal problems in epidemic disruptions like the COVID-19 pandemic is that the number of patients needing hospitals' emergency departments' services significantly grows. Since COVID-19 is an infectious disease, any aggregation has to be prevented accordingly. However, few aggregations cannot be prevented, including hospitals. To the best of our knowledge, COVID-19 is a life-threatening disease, especially for people in poor health conditions. Therefore, it sounds reasonable to optimize the health care queuing systems to minimize the infection rate by prioritizing patients based on their health condition so patients with a higher risk of infection will leave the queue sooner. In this paper, relying on data mining models and expert's opinions, we propose a method for patient classification and prioritizing. The optimal number of servers (treatment systems) will be determined by benefiting from a mixed-integer model and the grasshopper optimization algorithm.

关键词： Data Mining Queueing Systems COVID-19 Classification grasshopper optimization algorithm

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Optimal VMD-Based Signal Denoising for Laser Radar via Hausdorff Distance and Wavelet Transform

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IEEE ACCESS 2019年 7卷 167997-168010页

作者： Hua, Tuan Dai, Keren Zhang, Xiangjin Yao, Zongchen Wang, Hongjian Xie, Kefeng Feng, Tao Zhang, He Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210094 Peoples R China Nanjing Res Inst Elect Technol Nanjing 210039 Peoples R China Hubei Inst Aerosp Technol Gen Design Inst Wuhan 430034 Peoples R China 38 Inst China Elect Technol Grp Corp Hefei 230031 Peoples R China

Laser radar echo signals are easily contaminated by noise, such as background light and electronic noise, and this noise is an obstacle for the subsequent signal detection. However, the conventional denoising methods cannot achieve satisfactory effects when the signal-to-noise-ratio (SNR) is ultralow. In this paper, a novel denoising method for laser radar echo signals based on the parameter-optimal variational mode decomposition (VMD) combined with the Hausdorff distance (HD) and wavelet transform (WT) is proposed. Compared with conventional VMD-based methods, the proposed method utilizes a newly developed grasshopper optimization algorithm (GOA) to obtain the optimal combination of parameters for the VMD. Then, the HD is applied to select the relevant modes and then uses the basis function to reconstruct the signal. In addition, the relevant modes are further processed by the WT denoising method, which allows the reconstructed signal to obtain a higher SNR. The simulation and experimental results show the feasibility, effectiveness and robustness of the proposed method compared to three other available denoising techniques. The proposed method could promote the distance measurement performance of laser radars in harsh environments.

关键词： Laser radar echo signal denoising variational mode decomposition Hausdorff distance wavelet transform grasshopper optimization algorithm

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Efficient customer behaviour prediction in Indian metropolitan cities for E-commerce applications

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EXPERT SYSTEMS 2024年第9期41卷 e13604-e13604页

作者： Suresh, B. S. Suresh, M. St Peters Inst Higher Educ & Res Dept Management Studies Chennai India St Peters Inst Higher Educ & Res Dept Commerce Chennai India

The primary objective of this manuscript is to find the service quality factors, which influence customer satisfaction, promotional efforts taken by the retailer, and the perception of customers towards the organized retail sector. This investigation uses both offline and online retail sales datasets. Particularly, in the case of the offline retail sale dataset, an energy-efficient analysis is conducted to collect scalable, accurate, and real-time data. The collected data is used in enhancing several aspects of e-commerce operations, including customer experience optimization and inventory management, which results in better decision-making with increased efficiency. After data collection, the outliers are eliminated by implementing the z-score technique. The removal of outliers increases the variability of collected data which reduces statistical power. The statistically sufficient data are given to the Randomized grasshopper optimization algorithm (RGOA) for optimal instance selection. Finally, the selected optimal instances are given to the deep neural network (DNN) model for future customer behaviour prediction. The efficacy of the RGOA-DNN model is analysed by using evaluation measures like mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean percentage error (MPE) and mean absolute percentage error (MAPE). The numerical analysis states that the RGOA-DNN model obtained a minimal error rate with MSE of 0.09 and 0.10, RMSE of 0.13 and 0.14, MAE of 0.12 and 0.16, MPE of 0.10 and 0.11 and MAPE of 0.08 and 0.10 on the offline and online retail sales datasets. The RGOA-DNN model has a minimal error rate in future customer behaviour prediction related to the conventional regression models. Furthermore, the elimination of inactive instances or selection of optimal instances reduces the model complexity to linear and computational time to 22.10 and 23.12 s on the offline and online retail sales datasets.

关键词： deep neural network energy-efficient data collection error rate future customer behaviour prediction grasshopper optimization algorithm Indian metropolitan cities mean absolute error mean absolute percentage error root mean squared error z-score technique

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Simulation of dew point temperature in different time scales based on grasshopper algorithm optimized extreme gradient boosting

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JOURNAL OF HYDROLOGY 2022年 606卷 127452-127452页

作者： Dong, Jianhua Zeng, Wenzhi Lei, Guoqing Wu, Lifeng Chen, Haorui Wu, Jingwei Huang, Jiesheng Gaiser, Thomas Srivastava, Amit Kumar Wuhan Univ State Key Lab Water Resources & Hydropower Engn S Wuhan 430072 Peoples R China Nanchang Inst Technol Sch Hydraul & Ecol Engn Nanchang 330099 Jiangxi Peoples R China China Inst Water Resources & Hydropower Res State Key Lab Simulat & Regulat Water Cycle River Beijing 100038 Peoples R China Univ Bonn Inst Crop Sci & Resource Conservat INRES Crop Sci Grp Katzenburgweg 5 D-53115 Bonn Germany

Dew point temperature (Tdew) plays an important role in hydrology, meteorology, and other related research. This study evaluated the ability of a new machine learning model (hybrid extreme gradient boosting with grasshopper optimization algorithm (GOA-XGBoost)) to estimate Tdew and compared it with two other tree-based models (XGBoost and random forest (RF)). We collected meteorological data namely actual vapor pressure (ea), maximum air temperature (Tmax), minimum air temperature (Tmin), maximum relative humidity (RHmax), minimum relative humidity (RHmin), atmospheric pressure (Pa), 2 m high wind speed (Ud), during 2016-2019 on daily and hourly time scales from the Sijiqinglin station in China to train, test, and validate each model. The results showed that the GOA-XGBoost model performed best, and the RF model had severe over-fitting problems during the validation phase at daily time scale. The models showed the best accuracy and stability when the input was ea (on average R2 =1.000, RMSE = 0.296 degrees C, MBE = 0.001 degrees C, MAE = 0.167 degrees C, and KGE = 0.991). The models had more significant errors when the inputs were Tmax, Tmin (on average R2 = 0.721, RMSE = 6.756 degrees C, MBE = -0.101 degrees C, MAE = 5.071 degrees C, and KGE = 0.771). The estimation loss exhibited by the models were similar for the hourly and daily scale patterns. T and RH were the most basic meteorological factors and adding extraneous factors would affect the estimation accuracy of the model. The variability of meteorological data varied less on an hourly scale than on a daily scale. Therefore, the accuracy of the models was higher, but the data set and the volume of operations became larger. This led to a possible reduction in model stability, but the hourly scales are better suited for assessing the effects of simulations in extreme situations. Taking accuracy and stability into account, the GOA-XGBoost model was the best model and the most practical input for both time scales

关键词： Dew point temperature grasshopper optimization algorithm Extreme gradient boosting Time scale Cross-validation

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Non-linear static and dynamic buckling analysis of laminated composite cylindrical shell embedded in non-linear elastic foundation using the swarm-based metaheuristic algorithms

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EUROPEAN JOURNAL OF MECHANICS A-SOLIDS 2022年 91卷 1页

作者： Foroutan, Kamran Varedi-Koulaei, Seyyed Mojtaba Duc, Nguyen Dinh Ahmadi, Habib Shahrood Univ Technol Fac Mech & Mechatron Engn Shahrood Iran Hanoi Univ Engn & Technol UET Vietnam Natl Univ Dept Technol & Engn Construct & Transportat Hanoi Vietnam Nguyen Tat Thanh Univ NTT Inst High Technol Dist 4 Ho Chi Minh City Vietnam Sejong Univ Dept Civil & Environm Engn Natl Res Lab 209 Neungdong Ro Seoul 05006 South Korea

In this paper, by determining the optimal fiber's angles for maximum critical buckling load, the non-linear static and dynamic buckling analyses of laminated composite cylindrical shells resting on non-linear elastic foundation subjected to external loading are investigated. The laminated composite cylindrical with three, four, and five layers is considered. In the present study, first, the optimal fiber's angles are found using the swarm-based metaheuristic algorithms, then, the non-linear static and dynamic buckling analyses are carried out at optimum fiber directions. According to classical shell theory, von-Karman equation, and Hooke's law, the stressstrain relations are derived for the laminated composite cylindrical shell. Then, based on the Galerkin's method, the discretized motion equation is obtained. For the optimization problem, the design variables are ply angles, and the objective function is maximizing the critical buckling load. Four recently developed swarm-based metaheuristic algorithms including, grey wolf optimizer (GWO), whale optimization algorithm (WOA), grasshopper optimization algorithm (GOA), and salp swarm algorithm (SSA), are utilized to solve these optimization problems. Based on the results, the optimization algorithms have improved the value of the critical buckling load significantly. Also, the WOA algorithm had better results than the others, while the GOA was the weakest.

关键词： Laminated composite cylindrical shell Non-linear elastic foundation Grey wolf optimizer Whale optimization algorithm grasshopper optimization algorithm Salp swarm algorithm Non-linear static and dynamic buckling

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Smart residential load management technique for distribution systems' performance enhancement and consumers' satisfaction achievement

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2019年第4期29卷

作者： Elsayed, Abdallah M. Hegab, Mohamed M. Farrag, Sobhy M. Damietta Univ Fac Engn Dept Elect Engn New Damitta 34517 Egypt Menoufia Univ Shebin El Kom Fac Engn Dept Elect Engn Menoufia Egypt HEGAN Consultancy & Construct Assoc Cairo Egypt SMART Grp Cairo Egypt

Residential loads represent one of the most important types of existing loads, which represent 54.9% of the total electricity consumption in the Egyptian network. Traditional Egyptian distribution systems are not capable of handling the extra loading over the existing lines. These systems already suffer from many obstacles, which result in increasing service outage rate that would badly decrease distribution system efficiency, reliability, quality, and security. The load demand management can be considered one of the promising solution methods for peak load shaving as well as enhancing system performance and reliability. In this paper, a complete discussion and analysis of different types of Egyptian residential loads including its daily operation performance and time shifting operation capability have been introduced. Then, a generalized formula of the load management problem has been formulated including all objectives that need to be achieved as well as their respective constraints. A new smart load management technique has been proposed to solve this problem. The proposed technique represents the programming part necessary for the load management control centers. The proposed technique accepts the consumers' preferred operation times as input data, which ensure maximum consumers' satisfaction. Applying the proposed solution technique on a real overloaded Egyptian distribution network, the obtained results confirm the efficiency and effectiveness of the introduced methodology to relieve the overloading problem, enhance network performance, and meet the consumers' requirements.

关键词： grasshopper optimization algorithm distribution systems load management loss minimization voltage regulation

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A deep-learning wind speed interval forecasting architecture based on modified scaling approach with feature ranking and two-output gated recurrent unit

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 211卷

作者： Wang, Jianzhou Li, Qiwei Zhang, Haipeng Wang, Ying Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China

Wind power is already an indispensable part of the power supply worldwide, for which wind speed forecasting is technically required as essential work to guarantee its safe and economical generation. To date, the majority of wind speed forecasting models only provide deterministic predictions, which, however, fails in accounting for the uncertainty associated with the wind speed that is critical to practice;the current few models attempting to use interval forecasting to address the uncertainty are always plagued by rigorous hypotheses along with the used interval construction approaches and poor interval quality. To fill the gap, a novel interval forecasting architecture has been developed to better capture the uncertainty. It comprises a modified scaling approach, a two-output Gated Recurrent Unit (GRU), and the efficient feature ranking (FR), getting rid of rigorous hypotheses and constructing reliable prediction intervals. The case study based on real wind farm data sheds light on the advantage of the modified scaling approach, as it outperforms many benchmark approaches and conventional scaling approaches by enhancing the coverage width criterion by 4.07% to 97.76% on average. Further embedding the FR and the deep-learning GRU helps in further narrowing the interval width and reducing the interval deviation.

关键词： Wind speed interval forecasting Deep learning Feature selection grasshopper optimization algorithm Interval forecasting

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A combined continuous-time Markov chain and queueing-inventory model for a blood transfusion network considering ABO/Rh substitution priority and unreliable screening laboratory

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 215卷

作者： Aghsami, Amir Samimi, Yaser Aghaie, Abdollah KN Toosi Univ Technol KNTU Sch Ind Engn Tehran Iran

The uncertain nature of blood supply and demand in a blood supply chain network (BSCN) makes it impossible completely meet the blood demands on time. That's why there is a vital need to develop a comprehensive model for optimally managing blood networks considering all challenges they face with. Although many researches have studied blood inventory management (BIM) in BSCNs, there is still a lack of models simultaneously considering blood inventory levels in hospital blood banks (HBBs) and blood centers (BCs) as a stochastic pro-cess. Also, most models have not evaluated the impact of false positive (FP) and false negative (FN) errors in the screening laboratories (SLs) of BCs. Moreover, no study has modeled the HBB demands as a queueing-inventory model with multiple blood types according to ABO/Rh substitution priority. To fill these gaps, this paper de-velops a two-echelon blood bank network considering ABO/Rh factors and substitution priority in which in-ventory levels of all red blood cell (RBC) types in an HBB and BC are simultaneously modeled as eight dependent continuous-time Markov chains (CTMCs). An unobservable M/M/m queueing-inventory system with infected RBCs and (r, Q) policy in the HBB is proposed. The blood requests arrive at the requests queueing system in the HBB, and after processing and departing from the queue, they leave the HBB with exactly-one RBC unit with the same ABO and Rh (Rhesus) groups or compatible with other types of RBC. Lost sales occur if all possible types of RBC for transfusion are unavailable. HBB continuously checks each RBC type's inventory level and places an order to the BC once the inventory level of each one of them reaches a predetermined reorder point. Due to transfusion-transmissible infections (TTIs), an SL conducts a 100 % quality-assured screening in the BC modeled as an M/M/m queueing system. Due to unreliable inspectors, FP and FN errors are considered in the SL. The stationary distribution of the number of request

关键词： Blood inventory management ABO Rh compatibility and priority CTMC Queueing-inventory models Unreliable screening queueing process grasshopper optimization algorithm

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