44 research outputs found
Deep Learning-Based Machinery Fault Diagnostics
This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis
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Novel particle swarm optimization algorithms with applications to healthcare data analysis
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.Optimization problem is a fundamental research topic which has been receiving increasing
interest according to its application potential in almost all real-world systems
including engineering systems, large-scaled complex networks, healthcare management
systems and so on. A large number of heuristic algorithms have been developed with
the purpose of effectively solving the optimization problems during the past few decades.
Served as a powerful family of heuristic algorithms, the particle swarm optimization
(PSO) algorithm has been successfully employed in a variety of practical applications
in dealing with optimization problems. The PSO algorithm has exhibited more competitive
performance than many popular evolutionary computation approaches because
of its easy implementation, fast convergence and comprehensive ability of converging
to a satisfactory solution. Nevertheless, there is still much room to improve the PSO
algorithm in terms of both the convergence rate and the population diversity.
To summarize, there are three challenging problems in developing new variant PSO
algorithms with hope to further improve the convergence rate of the PSO algorithm
and maintain the population diversity: 1) how to adjust the control parameters of the
PSO algorithm; 2) how to achieve the balance between the local search and the global
search during the evolution process; and 3) how to guarantee the search ability of the
particles and avoid premature convergence.
In this thesis, we address the above mentioned challenging problems and aim to
design effective variant PSO algorithms with applications in intelligent data analysis.
It should be pointed out that all the developed PSO algorithms in this thesis have
been evaluated by comparing with some currently popular variant PSO algorithms.
• With the aim to improve the convergence rate of the optimizer, an adaptive
weighting PSO algorithm is put forward where a sigmoid-function-based weighting strategy is introduced to adjust the acceleration coefficients. With this weighting
strategy, the distances from the particle to the global best position and from the
particle to its personal best position are both taken into consideration, thereby
having the distinguishing feature of enhancing the convergence rate.
• As with other evolutionary computation approaches, the modification of parameters
is an efficient method for improving the search ability of the algorithm. We
present a randomised PSO algorithm where Gaussian white noise with adjustable
intensity is utilized to randomly perturb the acceleration coefficients in order to
explore and exploit the problem space thoroughly.
• To further develop a novel PSO algorithm with promising search ability, we
propose a randomly occurring distributedly delayed particle swarm optimization
(RODDPSO) algorithm which demonstrates competitive performance in seeking
the optimal solution. The randomly occurring distributed time delays not only
contribute to a thorough exploration of the search space but also achieve a proper
balance between the local exploitation and the global exploration.
• To fully investigate the application potential of the developed PSO algorithms,
we apply the RODDPSO algorithm to intelligent data analysis (including data
clustering and classification problems). We optimize the initial cluster centroids
of the K-means clustering algorithm and the hyperparameters of the deep neural
network by using the RODDPSO algorithm. The developed PRODDPSO-based
algorithms are successfully employed in patients’ triage categorization and patient
attendance disposal problems with satisfactory performanc
Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems
Multi-objective swarm intelligence-based (MOSI-based) metaheuristics were proposed to solve multi-objective optimization problems (MOPs) with conflicting objectives. Harris’s hawk multi-objective optimizer (HHMO) algorithm is a MOSIbased algorithm that was developed based on the reference point approach. The reference point is determined by the decision maker to guide the search process to a particular region in the true Pareto front. However, HHMO algorithm produces a poor approximation to the Pareto front because lack of information sharing in its population update strategy, equal division of convergence parameter and randomly generated
initial population. A two-step enhanced non-dominated sorting HHMO (2SENDSHHMO) algorithm has been proposed to solve this problem. The algorithm includes (i) a population update strategy which improves the movement of hawks in
the search space, (ii) a parameter adjusting strategy to control the transition between exploration and exploitation, and (iii) a population generating method in producing the initial candidate solutions. The population update strategy calculates a new position of hawks based on the flush-and-ambush technique of Harris’s hawks, and selects the best hawks based on the non-dominated sorting approach. The adjustment strategy enables the parameter to adaptively changed based on the state of the search space. The initial population is produced by generating quasi-random numbers using Rsequence followed by adapting the partial opposition-based learning concept to improve the diversity of the worst half in the population of hawks. The performance of the 2S-ENDSHHMO has been evaluated using 12 MOPs and three engineering MOPs. The obtained results were compared with the results of eight state-of-the-art
multi-objective optimization algorithms. The 2S-ENDSHHMO algorithm was able to generate non-dominated solutions with greater convergence and diversity in solving most MOPs and showed a great ability in jumping out of local optima. This indicates the capability of the algorithm in exploring the search space. The 2S-ENDSHHMO algorithm can be used to improve the search process of other MOSI-based algorithms and can be applied to solve MOPs in applications such as structural design and signal processing
Parametric Optimization of Taper Cutting Process using Wire Electrical Discharge Machining (WEDM)
Significant technological advancement of wire electrical discharge machining (WEDM) process has been observed in recent times in order to meet the requirements of various manufacturing fields especially in the production of parts with complex geometry in precision die industry. Taper cutting is an important application of WEDM process aiming at generating complex parts with tapered profiles. Wire deformation and breakage are more pronounced in taper cutting as compared with straight cutting resulting in adverse effect on desired taper angle and surface integrity. The reasons for associated problems may be attributed to certain stiffness of the wire. However, controlling the process parameters can somewhat reduce these problems. Extensive literature review reveals that effect of process parameters on various performance measures in taper cutting using WEDM is also not adequately addressed. Hence, study on effect of process parameters on performance measures using various advanced metals and metal matrix composites (MMC) has become the predominant research area in this field. In this context, the present work attempts to experimentally investigate the machining performance of various alloys, super alloys and metal matrix composite during taper cutting using WEDM process. The effect of process parameters such as part thickness, taper angle, pulse duration, discharge current, wire speed and wire tension on various performance measures such as angular error, surface roughness, cutting rate and white layer thickness are studied using Taguchi’s analysis. The functional relationship between the input parameters and performance measures has been developed by using non-linear regression analysis. Simultaneous optimization of the performance measures has been carried out using latest nature inspired algorithms such as multi-objective particle swarm optimization (MOPSO) and bat algorithm. Although MOPSO develops a set of non-dominated solutions, the best ranked solution is identified from a large number of solutions through application of maximum deviation method rather than resorting to human judgement. Deep cryogenic treatment of both wire and work material has been carried out to enhance the machining efficiency of the low conductive work material like Inconel 718. Finally, artificial intelligent models are proposed to predict the various performance measures prior to machining. The study offers useful insight into controlling the parameters to improve the machining efficiency
Parametric Optimization of Taper Cutting Process using Wire Electrical Discharge Machining (WEDM)
Significant technological advancement of wire electrical discharge machining (WEDM) process has been observed in recent times in order to meet the requirements of various manufacturing fields especially in the production of parts with complex geometry in precision die industry. Taper cutting is an important application of WEDM process aiming at generating complex parts with tapered profiles. Wire deformation and breakage are more pronounced in taper cutting as compared with straight cutting resulting in adverse effect on desired taper angle and surface integrity. The reasons for associated problems may be attributed to certain stiffness of the wire. However, controlling the process parameters can somewhat reduce these problems. Extensive literature review reveals that effect of process parameters on various performance measures in taper cutting using WEDM is also not adequately addressed. Hence, study on effect of process parameters on performance measures using various advanced metals and metal matrix composites (MMC) has become the predominant research area in this field. In this context, the present work attempts to experimentally investigate the machining performance of various alloys, super alloys and metal matrix composite during taper cutting using WEDM process. The effect of process parameters such as part thickness, taper angle, pulse duration, discharge current, wire speed and wire tension on various performance measures such as angular error, surface roughness, cutting rate and white layer thickness are studied using Taguchi’s analysis. The functional relationship between the input parameters and performance measures has been developed by using non-linear regression analysis. Simultaneous optimization of the performance measures has been carried out using latest nature inspired algorithms such as multi-objective particle swarm optimization (MOPSO) and bat algorithm. Although MOPSO develops a set of non-dominated solutions, the best ranked solution is identified from a large number of solutions through application of maximum deviation method rather than resorting to human judgement. Deep cryogenic treatment of both wire and work material has been carried out to enhance the machining efficiency of the low conductive work material like Inconel 718. Finally, artificial intelligent models are proposed to predict the various performance measures prior to machining. The study offers useful insight into controlling the parameters to improve the machining efficiency
Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress
Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018
Operating parameters optimization for the aluminum electrolysis process using an improved quantum-behaved particle swarm algorithm
Improvements in the production and energy consumption of the aluminum electrolysis process (AEP) directly depend on the operating parameters of the electrolytic cell. To balance the conflicting goals of efficiency and productivity with reduced energy consumption and emissions, AEP operating parameter optimization is formulated as a constrained multiobjective optimization problem with competing objectives of current efficiency and cell voltage. Then, the improved multiobjective quantum-behaved particle swarm optimization (IMQPSO) algorithm is proposed. The application of an adaptive opposition-based learning strategy and a piecewise Gauss mutation operator can increase the diversity of the population and enhance the global search ability of the IMQPSO. To expand the creativity of the particles, two iterative methods of the mean best position with weighting and the attractor position are redesigned. Experimental analyses are conducted for the benchmark problems and a real case to verify the effectiveness of the proposed method
XVIII International Coal Preparation Congress
Changes in economic and market conditions of mineral raw materials in recent
years have greatly increased demands on the ef fi ciency of mining production. This
is certainly true of the coal industry. World coal consumption is growing faster than
other types of fuel and in the past year it exceeded 7.6 billion tons. Coal extraction
and processing technology are continuously evolving, becoming more economical
and environmentally friendly. “ Clean coal ” technology is becoming increasingly
popular. Coal chemistry, production of new materials and pharmacology are now
added to the traditional use areas — power industry and metallurgy. The leading role
in the development of new areas of coal use belongs to preparation technology and
advanced coal processing. Hi-tech modern technology and the increasing interna-
tional demand for its effectiveness and ef fi ciency put completely new goals for the
University. Our main task is to develop a new generation of workforce capacity and
research in line with global trends in the development of science and technology to
address critical industry issues.
Today Russia, like the rest of the world faces rapid and profound changes
affecting all spheres of life. The de fi ning feature of modern era has been a rapid
development of high technology, intellectual capital being its main asset and
resource. The dynamics of scienti fi c and technological development requires acti-
vation of University research activities. The University must be a generator of ideas
to meet the needs of the economy and national development. Due to the high
intellectual potential, University expert mission becomes more and more called for
and is capable of providing professional assessment and building science-based
predictions in various fi elds.
Coal industry, as well as the whole fuel and energy sector of the global economy
is growing fast. Global multinational energy companies are less likely to be under
state in fl uence and will soon become the main mechanism for the rapid spread of
technologies based on new knowledge. Mineral resources will have an even greater
impact on the stability of the economies of many countries. Current progress in the
technology of coal-based gas synthesis is not just a change in the traditional energy markets, but the emergence of new products of direct consumption, obtained from
coal, such as synthetic fuels, chemicals and agrochemical products. All this requires
a revision of the value of coal in the modern world economy