Bright Edward - Academia.edu (original) (raw)
Papers by Bright Edward
Journal of Engineering Research and Sciences
Neural Network regression (NNR) is considered more effective as compared to multiple neural netwo... more Neural Network regression (NNR) is considered more effective as compared to multiple neural networks model readily available in Azure to evaluate the Remaining Useful Life (RUL) of bearing in this work because it performs better than other models when used and was demonstrated as a non-programing technique for analyzing enormous data without the use of Hive, Hadoop, Pig, etc. To complement the earlier paper, we further used statistical means in verifying our results. Using this non-parametric non-linear approach is intuitively appealing to forecast the Remaining Useful Life (RUL) of a bearing. Over the years the Azure cloud service platform has gained recognition as a major forecasting technique toolbox of forecasters, NNR model implementations have surged, hence its inclusion here on its' use on the NASA FEMTO-ST Institute (Franche-Comté ÉlectroniqueMécaniqueThermique et Optique-Sciences et Technologies) bearing dataset. Azure is a machine learning platform from Microsoft that allows developers to write, test, and deploy algorithms and has been motivationally proven adequate and useful for predicting the RUL of bearings. As seen in so many recent articles, NNR Artificial Intelligence is a model among many others readily available for computing on the platform that has been successfully used for non-programming of the enormous dataset and applied for forecasting the RUL of Bearing. This has added value in the forecasting phase. The novelty in this work is related to the application of NNR where we were able to combine the Dickey-Fuller Test with NNR to ensure that the data needed to be used with NNR is fit for application to yield optimal prediction results and our previous result from the past paper was further established. A satisfactory judgmental result was obtained; making Azure's work studio a reasonable place to predict without much programming expertise. We tested the findings from the National Aeronautics and Space Administration (NASA) database for the person that came first in the competition by comparing our Azure model observations with the NNR observations collected. Ultimately, we showed the finding is enhanced by the AZURE model.
Material Science & Engineering International Journal
One of the primary causes of Nigeria's underdevelopment is a lack of electricity to power the... more One of the primary causes of Nigeria's underdevelopment is a lack of electricity to power the country's industries. Rather than relying solely on fossil fuels to generate electricity in Nigeria, a variety of other sources, such as nuclear energy, must be considered. A Nuclear Power Plant's (NPP) location is a critical step in its development. This paper thus presents a methodology for resolving the issue of NPP site selection. For this project, the fuzzy Grey Relational Analysis (GRA) method was chosen. To assess the feasibility of the approach, one of Nigeria's six south-south states was chosen as the location of a hypothetical nuclear power plant. According to the findings, the NPP would be best located in Delta State, which had the highest gray relational grade of 0.7897.
Journal of Engineering and Applied Science
When condition-based maintenance (CBM) is combined with proper decision support systems, it leads... more When condition-based maintenance (CBM) is combined with proper decision support systems, it leads to enhanced utilization of resources and increased productivity which tends towards business efficiency. The forecasting of the future condition, the remaining operating life, or probability of stable system behavior, based on data from acquired condition monitoring is referred to as prognosis which is an important part of the CBM process. Despite auto-regression integrated moving average (ARIMA) time series modeling, being long established and dating back to the 1960s, it has surged through new advances over the years and is now recognized as a major forecasting technique. Its application is therefore investigated here in the context of the FEMTO–ST Institute (Franche-Comté Électronique Mécanique Thermique et Optique-Sciences et Technologies) bearing dataset. The work discussed in this article uses a time series approach which contributes to modeling and forecasting the remaining usefu...
Journal of Mechanical and Energy Engineering, 2020
A lot of uncertainties and complexities exist in real life problem. Unfortunately, the world appr... more A lot of uncertainties and complexities exist in real life problem. Unfortunately, the world approaches such intricate realistic life problems using traditional methods which has failed to offer robust solutions. In recent times, researchers look beyond classical techniques. There is a model shift from the use of classical techniques to the use of standardized intelligent biological systems or evolutionary biology. Genetic Algorithm (GA) has been recognized as a prospective technique capable of handling uncertainties and providing optimized solutions in diverse area, especially in homes, offices, stores and industrial operations. This research is focused on the appraisal of GA and its application in real life problem. The scenario considered is the application of GA in 0-1 knapsack problem. From the solution of the GA model, it was observed that there is no combination that would give the exact weight or capacity the 35 kg bag can carry but the possible range from the solution model is 34 kg and 36 kg. Since the weight of the bag is 35 kg, the feasible or near optimal solution weight of items the bag can carry would be 34 kg at benefit of 16. Additional load beyond 34 kg could lead to warping of the bag.
Experimental determination of the effect of annealing on the microstructure and mechanical proper... more Experimental determination of the effect of annealing on the microstructure and mechanical properties of a cold work 70 - 30 brass, was carried out by subjecting specimens of the material to various degrees of cold-work (20%, 40% and 60%), by straining using a tensile machine. The specimens for each degree of cold work were then annealed at 250°C, 350°C, 450°C and 600°C, for 30 minutes. The approach involves the use of metallographic techniques: grinding, polishing and etching to reveal the microstructure while tensile test was carried out on the specimen using a Monsanto tensometer so as to obtain the load/extension graph from which the tensile strength and hardness values were obtained. From the results obtained, it was conclusive that annealing produced finer grains and eliminates prior cold work whereby the material becomes ductile. However, there should be an appreciable deformation for this effect to be noticed. One important aspect of re-crystallization in structural materials is that there is a loss of strength which accompanies disappearance of the cold-worked grains when subjected to high temperature applications. Yet, it is often difficult to establish the exact range of permissible temperature. This work establishes a range for the re-crystallization of alpha brass as 350°C < TC < 450°C, where TC is the re-crystallization temperature. Thus, it will be safe to apply this material at temperatures below 350°C, without fear of structural changes with accompanying lost in strength.
Shot peening (SP) is a controlled and systematic process of surface treatment that has a large nu... more Shot peening (SP) is a controlled and systematic process of surface treatment that has a large number of controllable process parameters that make its application highly challenging. It involves the shooting of small and hard metallic balls at a targeted surface, with the aim of enhancing the fatigue strength of the workpiece under unfavorable service conditions. The compressive residual stress (CRS) induced by this application is expensive to evaluate experimentally. This paper presents a numerical model of the impact of a single-shot on a metallic surface, with the aim to set the stage for a realistic multiple shots peening simulation. The approach proposed herein is a sequential Discrete Element-Finite Element (DE-FE) coupled simulation, based on the use of different types of coefficients of restitution (CoRs) with emphasis on the energetic CoR. The energetic CoR relates the shot/target contact forces to the fractional strain energy needed for localized plastic deformation of the near-surface layer in the workpiece. The generated results of the induced compressive residual stresses (CRS) and equivalent plastic strain (PEEQ) from single-shot simulations are validated with similar results from the literature. Our study clarifies the strain energy aspects of a single-shot impact responsible for the desired effects of CRS and PEEQ, thereby laying the groundwork for accurate and realistic modeling of the SP process via the DEM-FEM approach.
Journal of Engineering Research and Sciences
Neural Network regression (NNR) is considered more effective as compared to multiple neural netwo... more Neural Network regression (NNR) is considered more effective as compared to multiple neural networks model readily available in Azure to evaluate the Remaining Useful Life (RUL) of bearing in this work because it performs better than other models when used and was demonstrated as a non-programing technique for analyzing enormous data without the use of Hive, Hadoop, Pig, etc. To complement the earlier paper, we further used statistical means in verifying our results. Using this non-parametric non-linear approach is intuitively appealing to forecast the Remaining Useful Life (RUL) of a bearing. Over the years the Azure cloud service platform has gained recognition as a major forecasting technique toolbox of forecasters, NNR model implementations have surged, hence its inclusion here on its' use on the NASA FEMTO-ST Institute (Franche-Comté ÉlectroniqueMécaniqueThermique et Optique-Sciences et Technologies) bearing dataset. Azure is a machine learning platform from Microsoft that allows developers to write, test, and deploy algorithms and has been motivationally proven adequate and useful for predicting the RUL of bearings. As seen in so many recent articles, NNR Artificial Intelligence is a model among many others readily available for computing on the platform that has been successfully used for non-programming of the enormous dataset and applied for forecasting the RUL of Bearing. This has added value in the forecasting phase. The novelty in this work is related to the application of NNR where we were able to combine the Dickey-Fuller Test with NNR to ensure that the data needed to be used with NNR is fit for application to yield optimal prediction results and our previous result from the past paper was further established. A satisfactory judgmental result was obtained; making Azure's work studio a reasonable place to predict without much programming expertise. We tested the findings from the National Aeronautics and Space Administration (NASA) database for the person that came first in the competition by comparing our Azure model observations with the NNR observations collected. Ultimately, we showed the finding is enhanced by the AZURE model.
Material Science & Engineering International Journal
One of the primary causes of Nigeria's underdevelopment is a lack of electricity to power the... more One of the primary causes of Nigeria's underdevelopment is a lack of electricity to power the country's industries. Rather than relying solely on fossil fuels to generate electricity in Nigeria, a variety of other sources, such as nuclear energy, must be considered. A Nuclear Power Plant's (NPP) location is a critical step in its development. This paper thus presents a methodology for resolving the issue of NPP site selection. For this project, the fuzzy Grey Relational Analysis (GRA) method was chosen. To assess the feasibility of the approach, one of Nigeria's six south-south states was chosen as the location of a hypothetical nuclear power plant. According to the findings, the NPP would be best located in Delta State, which had the highest gray relational grade of 0.7897.
Journal of Engineering and Applied Science
When condition-based maintenance (CBM) is combined with proper decision support systems, it leads... more When condition-based maintenance (CBM) is combined with proper decision support systems, it leads to enhanced utilization of resources and increased productivity which tends towards business efficiency. The forecasting of the future condition, the remaining operating life, or probability of stable system behavior, based on data from acquired condition monitoring is referred to as prognosis which is an important part of the CBM process. Despite auto-regression integrated moving average (ARIMA) time series modeling, being long established and dating back to the 1960s, it has surged through new advances over the years and is now recognized as a major forecasting technique. Its application is therefore investigated here in the context of the FEMTO–ST Institute (Franche-Comté Électronique Mécanique Thermique et Optique-Sciences et Technologies) bearing dataset. The work discussed in this article uses a time series approach which contributes to modeling and forecasting the remaining usefu...
Journal of Mechanical and Energy Engineering, 2020
A lot of uncertainties and complexities exist in real life problem. Unfortunately, the world appr... more A lot of uncertainties and complexities exist in real life problem. Unfortunately, the world approaches such intricate realistic life problems using traditional methods which has failed to offer robust solutions. In recent times, researchers look beyond classical techniques. There is a model shift from the use of classical techniques to the use of standardized intelligent biological systems or evolutionary biology. Genetic Algorithm (GA) has been recognized as a prospective technique capable of handling uncertainties and providing optimized solutions in diverse area, especially in homes, offices, stores and industrial operations. This research is focused on the appraisal of GA and its application in real life problem. The scenario considered is the application of GA in 0-1 knapsack problem. From the solution of the GA model, it was observed that there is no combination that would give the exact weight or capacity the 35 kg bag can carry but the possible range from the solution model is 34 kg and 36 kg. Since the weight of the bag is 35 kg, the feasible or near optimal solution weight of items the bag can carry would be 34 kg at benefit of 16. Additional load beyond 34 kg could lead to warping of the bag.
Experimental determination of the effect of annealing on the microstructure and mechanical proper... more Experimental determination of the effect of annealing on the microstructure and mechanical properties of a cold work 70 - 30 brass, was carried out by subjecting specimens of the material to various degrees of cold-work (20%, 40% and 60%), by straining using a tensile machine. The specimens for each degree of cold work were then annealed at 250°C, 350°C, 450°C and 600°C, for 30 minutes. The approach involves the use of metallographic techniques: grinding, polishing and etching to reveal the microstructure while tensile test was carried out on the specimen using a Monsanto tensometer so as to obtain the load/extension graph from which the tensile strength and hardness values were obtained. From the results obtained, it was conclusive that annealing produced finer grains and eliminates prior cold work whereby the material becomes ductile. However, there should be an appreciable deformation for this effect to be noticed. One important aspect of re-crystallization in structural materials is that there is a loss of strength which accompanies disappearance of the cold-worked grains when subjected to high temperature applications. Yet, it is often difficult to establish the exact range of permissible temperature. This work establishes a range for the re-crystallization of alpha brass as 350°C < TC < 450°C, where TC is the re-crystallization temperature. Thus, it will be safe to apply this material at temperatures below 350°C, without fear of structural changes with accompanying lost in strength.
Shot peening (SP) is a controlled and systematic process of surface treatment that has a large nu... more Shot peening (SP) is a controlled and systematic process of surface treatment that has a large number of controllable process parameters that make its application highly challenging. It involves the shooting of small and hard metallic balls at a targeted surface, with the aim of enhancing the fatigue strength of the workpiece under unfavorable service conditions. The compressive residual stress (CRS) induced by this application is expensive to evaluate experimentally. This paper presents a numerical model of the impact of a single-shot on a metallic surface, with the aim to set the stage for a realistic multiple shots peening simulation. The approach proposed herein is a sequential Discrete Element-Finite Element (DE-FE) coupled simulation, based on the use of different types of coefficients of restitution (CoRs) with emphasis on the energetic CoR. The energetic CoR relates the shot/target contact forces to the fractional strain energy needed for localized plastic deformation of the near-surface layer in the workpiece. The generated results of the induced compressive residual stresses (CRS) and equivalent plastic strain (PEEQ) from single-shot simulations are validated with similar results from the literature. Our study clarifies the strain energy aspects of a single-shot impact responsible for the desired effects of CRS and PEEQ, thereby laying the groundwork for accurate and realistic modeling of the SP process via the DEM-FEM approach.