Applications of Data Analytics in Engineering Systems
Data analytics is a rapidly growing and fascinating field that finds its allure in the diverse range of applications it offers. Among these applications, engineering systems are a crucial area of interest in the modern world. Almost everything we encounter, from our everyday devices and machines to complex tools and innovations, relies on engineering systems. However, the operation of these systems can sometimes lead to failures, which may result in significant problems. This is where data analytics comes to the rescue, utilizing cutting-edge analytical techniques like machine learning and statistical analysis to proactively prevent and address potential issues, ensuring the smooth functioning and reliability of engineering systems. Let’s dive into the exciting applications of data analytics in engineering systems.
Healthcare system engineering applies engineering principles to improve healthcare systems. Data analytics plays a crucial role in this field by enabling various applications. Predictive analytics involves using machine learning algorithms to analyze healthcare data and predict outcomes such as readmissions, disease progression, adverse events, and medical outcomes. Decision support systems assist healthcare professionals in making informed decisions about treatment options, surgery types, medication choices, and patient management. Efficiency improvement involves analyzing data to identify bottlenecks, optimize resource allocation, and streamline operations for greater efficiency. Patient safety analysis helps identify patterns and risk factors associated with adverse events, medication errors, and patient harm, leading to improved safety measures. Cost analysis integrates diverse cost data sources to gain insights into costs across different departments, procedures, and services, facilitating resource allocation and cost management decisions. Variation analysis identifies opportunities for cost reduction, standardization, and efficiency improvement. Additionally, data analytics supports cost prediction and forecasting, aiding in budgeting, financial planning, and resource allocation decisions. These data-driven applications enhance decision-making, resource allocation, patient care, and overall efficiency in healthcare systems.
System engineering principles apply to biological systems, explicitly analyzing biological data. Dr. Wang and other researchers from Auburn University utilized data analytic techniques to provide insights and improve their understanding of complex biological systems. The purpose of analyzing the biological data is to analyze the dynamics of biological systems, including gene regulatory networks, metabolic networks, and signal transduction. The researchers used one strategy to improve the biological model to reduce overfitting, including reducing the parameter space, reducing feature space, and increasing sample space. Reducing the parameter space limits the number of parameters to improve the model's reliability. Reducing the feature space will only keep the most significant features to reduce the complexity of the model. Increasing the sample space increases the sample size to improve the accuracy rate. From implementing this strategy, the model accuracy rate increased, and misinterpretation of the model was prevented.
System engineering principles can be effectively applied to the BATP system, which is a wastewater treatment method widely used in civil engineering and environmental engineering. In the BATP system, microorganisms play a crucial role in breaking down organic matter in the wastewater, converting it into harmless byproducts like water and carbon dioxide.
Various techniques are employed in the BATP system to achieve efficient wastewater treatment. One notable technique is real-time input identification, which involves identifying and adjusting input variables or parameters in real time to optimize the wastewater treatment process. These input variables may include the flow rate of wastewater, the amount of air supplied for aeration, the level of dissolved oxygen, and the concentration of nutrients.
Data analytics techniques, such as machine learning algorithms and statistical analysis, are utilized to analyze data in real-time to enhance the performance of the BATP system. Through this analysis, researchers can identify the most suitable values for the input variables, ensuring that the system operates at its peak efficiency and delivers the desired treatment outcomes. The benefits of such optimization include improved treatment efficiency, reduced energy consumption, and cost-effectiveness in wastewater treatment processes.
Smart manufacturing is a modern approach to manufacturing that harnesses advanced technologies and data analytics to optimize production processes and enhance overall efficiency. This innovative method utilizes data generated from various sensors and devices, which is then analyzed to gain valuable insights into production operations. By employing data analytics, manufacturers can identify patterns, predict potential issues, and make data-driven decisions to streamline their processes and improve productivity.
One significant advantage of data analytics in smart manufacturing is the ability to predict equipment failures and detect anomalies, enabling proactive maintenance and reducing downtime. Moreover, data analytics can optimize production schedules, ensuring that resources are utilized efficiently, leading to increased operational efficiency and reduced production costs.
However, as with any technology, data security and privacy concerns are essential in implementing data analytics in manufacturing processes. Safeguarding sensitive data from unauthorized access and ensuring privacy protection for individuals involved in the manufacturing environment are critical challenges that must be addressed.
A case study was conducted in the semiconductor manufacturing industry using data analytics techniques. The primary aim of this study was to enhance the efficiency, quality, and yield of the semiconductor manufacturing process. Several aspects were addressed in this case study, including yield optimization, fault detection, process optimization, quality improvement, and cost reduction.
Yield optimization focuses on identifying areas for improvement to increase efficiency and reduce waste during the manufacturing process. Fault detection played a crucial role in identifying potential issues early on, helping to ensure smooth semiconductor production. Process optimization enhances overall efficiency and productivity throughout the manufacturing process. Quality improvement efforts were geared towards improving product quality and reducing defects in the product life cycle. Lastly, cost reduction strategies were implemented to lower production costs and enhance the cost-effectiveness of semiconductor manufacturing.
Data analytics plays a vital role in engineering systems, serving as a powerful tool to detect failures and address potential issues proactively. Its applications in various domains have proven to be transformative. In healthcare systems, data analytics enables predictive analytics to forecast patient outcomes and supports decision-making for better treatment options. In biological systems, it aids in analyzing gene regulatory networks, metabolic networks, and signal transduction, deepening our understanding of complex biological processes. For BATP systems, data analytics optimizes wastewater treatment processes through real-time input identification, enhancing efficiency and cost-effectiveness. Additionally, data analytics in intelligent manufacturing optimizes production processes, detects faults, and increases operational efficiency. The wide-ranging applications of data analytics in engineering systems underscore its significance in modern-day advancements and problem-solving across diverse industries.
Works Cited
Salazar-Reyna, R., Gonzalez-Aleu, F., Edgar MA Granda-Gutierrez, Diaz-Ramirez, J., Garza-Reyes, J., & Kumar, A. (2022). A systematic literature review of data science, data analytics, and machine learning applied to healthcare engineering systems. Management Decision, 60(2), 300-319. doi:https://doi.org/10.1108/MD-01-2020-0035
He, Q. P., & Wang, J. (2020). Application of systems engineering principles and techniques in biological big data analytics: A review. Processes, 8(8), 951. doi:https://doi.org/10.3390/pr8080951
Behera, Rakesh, et al. "Real Time Identification of Inputs for a BATP System Using Data Analytics." International Journal of Civil Engineering, vol. 15, no. 8, Dec. 2017, pp. 1173+. Gale Academic OneFile,
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Moyne, J., & Iskandar, J. (2017). Big data analytics for smart manufacturing: Case studies in semiconductor manufacturing. Processes, 5(3), 39. doi:https://doi.org/10.3390/pr5030039
