This paper introduces a novel optimization approach called Recuperated Seed Search Optimization(RSSO),designed to address challenges in solving mechanical engineering design problems.Many optimization techniques strug...This paper introduces a novel optimization approach called Recuperated Seed Search Optimization(RSSO),designed to address challenges in solving mechanical engineering design problems.Many optimization techniques struggle with slow convergence and suboptimal solutions due to complex,nonlinear natures.The Sperm Swarm Optimization(SSO)algorithm,which mimics the sperm’s movement to reach an egg,is one such technique.To improve SSO,researchers combined it with three strategies:opposition-based learning(OBL),Cauchy mutation(CM),and position clamping.OBL introduces diversity to SSO by exploring opposite solutions,speeding up convergence.CM enhances both exploration and exploitation capabilities throughout the optimization process.This combined approach,RSSO,has been rigorously tested on standard benchmark functions,real-world engineering problems,and through statistical analysis(Wilcoxon test).The results demonstrate that RSSO significantly outperforms other optimization algorithms,achieving faster convergence and better solutions.The paper details the RSSO algorithm,discusses its implementation,and presents comparative results that validate its effectiveness in solving complex engineering design challenges.展开更多
Sensors,vital elements in data acquisition systems,play a crucial role in various industries.However,their exposure to harsh operating conditions makes them vulnerable to faults that can compromise system performance....Sensors,vital elements in data acquisition systems,play a crucial role in various industries.However,their exposure to harsh operating conditions makes them vulnerable to faults that can compromise system performance.Early fault detection is therefore critical for minimizing downtime and ensuring system reliability.This paper delves into the contemporary landscape of fault diagnosis techniques for sensors,offering valuable insights for researchers and academicians.The papers begin by exploring the different types and causes of sensor faults,followed by a discussion of the various fault diagnosis methods employed in industrial sectors.The advantages and limitations of these methods are carefully examined,paving the way for highlighting current challenges and outlining potential future research directions.This comprehensive review aims to provide a thorough understanding of current advancements in sensor fault diagnosis,enabling readers to stay abreast of the latest developments in this rapidly evolving field.By addressing the challenges and exploring promising research avenues,this paper seeks to contribute to the development of more robust and effective sensor fault diagnosis methods,ultimately improving the reliability and safety of industrial and agricultural systems.展开更多
The underlying study investigates single valued neutrosophic entropy based adaptive sensitive frequency band selection for variational mode decomposition(VMD)for the purpose of identifying defective components in an a...The underlying study investigates single valued neutrosophic entropy based adaptive sensitive frequency band selection for variational mode decomposition(VMD)for the purpose of identifying defective components in an axial pump.The proposed methodology is applied in the following steps.First,VMD is applied for decomposing vibration signals into various frequency bands,called as modes.After computing energy of each VMD,the lower(minimum)and upper(maximum)bounds from these energy readings are extracted for defect conditions,such as outer race,inner race,worn piston,faulty cylinder and valve plate,and blocked hole of the piston.Thereafter,energy interval ranges are obtained and further converted into the form of single valued neutrosophic sets(SVNSs).Then,the proposed neutrosophic entropy measure is deployed for quantifying the non-linear connection between each bearing defect conditions and various frequency bands.The mode having maximum neutrosophic entropy value is designated to the“most sensitive”frequency band.Thereafter,envelope demodulation is applied to the most sensitive selected frequency band for finding defective components.The proposed neutrosophic entropy and VMD based methodology is effective in providing a better insight for selecting suitable frequency band for carrying out envelope demodulation in comparison to existing methods.展开更多
文摘This paper introduces a novel optimization approach called Recuperated Seed Search Optimization(RSSO),designed to address challenges in solving mechanical engineering design problems.Many optimization techniques struggle with slow convergence and suboptimal solutions due to complex,nonlinear natures.The Sperm Swarm Optimization(SSO)algorithm,which mimics the sperm’s movement to reach an egg,is one such technique.To improve SSO,researchers combined it with three strategies:opposition-based learning(OBL),Cauchy mutation(CM),and position clamping.OBL introduces diversity to SSO by exploring opposite solutions,speeding up convergence.CM enhances both exploration and exploitation capabilities throughout the optimization process.This combined approach,RSSO,has been rigorously tested on standard benchmark functions,real-world engineering problems,and through statistical analysis(Wilcoxon test).The results demonstrate that RSSO significantly outperforms other optimization algorithms,achieving faster convergence and better solutions.The paper details the RSSO algorithm,discusses its implementation,and presents comparative results that validate its effectiveness in solving complex engineering design challenges.
基金supported by the National Center of Science,Poland under Sheng2 project No.UMO-2021/40/Q/ST8/00024:NonGauMech—New Methods of Processing Non-Stationary Signals (Identification,Segmentation,Extraction,Modeling)with Non-Gaussian Characteristics for the Purpose of Monitoring Complex Mechanical Structures.
文摘Sensors,vital elements in data acquisition systems,play a crucial role in various industries.However,their exposure to harsh operating conditions makes them vulnerable to faults that can compromise system performance.Early fault detection is therefore critical for minimizing downtime and ensuring system reliability.This paper delves into the contemporary landscape of fault diagnosis techniques for sensors,offering valuable insights for researchers and academicians.The papers begin by exploring the different types and causes of sensor faults,followed by a discussion of the various fault diagnosis methods employed in industrial sectors.The advantages and limitations of these methods are carefully examined,paving the way for highlighting current challenges and outlining potential future research directions.This comprehensive review aims to provide a thorough understanding of current advancements in sensor fault diagnosis,enabling readers to stay abreast of the latest developments in this rapidly evolving field.By addressing the challenges and exploring promising research avenues,this paper seeks to contribute to the development of more robust and effective sensor fault diagnosis methods,ultimately improving the reliability and safety of industrial and agricultural systems.
基金co-supported by the National Natural Science Foundation of China(Nos.U1909217,U1709208)the Zhejiang Provincial Natural Science Foundation of China(No.LD21E050001)the Zhejiang Special Support Program for High-level Personnel Recruitment of China(No.2018R52034).
文摘The underlying study investigates single valued neutrosophic entropy based adaptive sensitive frequency band selection for variational mode decomposition(VMD)for the purpose of identifying defective components in an axial pump.The proposed methodology is applied in the following steps.First,VMD is applied for decomposing vibration signals into various frequency bands,called as modes.After computing energy of each VMD,the lower(minimum)and upper(maximum)bounds from these energy readings are extracted for defect conditions,such as outer race,inner race,worn piston,faulty cylinder and valve plate,and blocked hole of the piston.Thereafter,energy interval ranges are obtained and further converted into the form of single valued neutrosophic sets(SVNSs).Then,the proposed neutrosophic entropy measure is deployed for quantifying the non-linear connection between each bearing defect conditions and various frequency bands.The mode having maximum neutrosophic entropy value is designated to the“most sensitive”frequency band.Thereafter,envelope demodulation is applied to the most sensitive selected frequency band for finding defective components.The proposed neutrosophic entropy and VMD based methodology is effective in providing a better insight for selecting suitable frequency band for carrying out envelope demodulation in comparison to existing methods.
