Big data is transforming engineering in unprecedented ways. Engineering disciplines are harnessing the power of data analytics to drive innovation, efficiency and informed decision-making.
From industrial to civil, data analytics plays a pivotal role in shaping the future of engineering. In part one of this series, we looked at the types of analytics that engineers working in data use every day. In this article, we’ll explore how different engineering sectors utilize data analytics to optimize processes, enhance safety and propel innovation forward.
Data Analytics Across Engineering Industries
Industrial Engineering
The relationship between data analytics and industrial engineering is at the core of Industry 4.0. Using data analytics, engineers can perform real-time monitoring, optimize production flows, reduce waste and improve product quality. Data analytics also allows engineers to enhance worker satisfaction by analyzing operator interactions, ergonomics and safety data to create safer, more intuitive environments. Here’s some specific examples of how engineers leverage data:
- Operations Research
Example: Engineers can use data to optimize supply chain management, production planning and inventory management using linear programming and other optimization techniques. - Decision Analysis
Example: Decision trees can be used to evaluate potential outcomes and make informed choices about a business decision. - Simulation
Example: A manufacturing plant may create a digital twin to simulate how to optimize its assembly line and reduce bottlenecks.
Civil and Structural Engineering
Next time you’re driving, pay (closer) attention to the flow of traffic. This complex dance is made possible by data science. Traffic engineers carefully collect and analyze data to understand how vehicles move through the transportation network. In fact, big data plays an increasingly vital role in improving and designing our infrastructure, allowing engineers to get projects off the ground faster than ever before. Specific examples include:
- Predictive Models
Example: By analyzing past project data, engineers can develop predictive models to optimize construction schedules, reducing average project timelines. - Traffic Management
Example: Using real-time traffic data to adjust traffic light timings, engineers can reduce congestion and improve traffic flow through dynamic traffic management systems. - Structural Health Monitoring
Example: Engineers can assess structural health by analyzing sensor data to predict potential failures and enhance building safety protocols.
Aerospace Engineering
The airplane you’re boarding on the way to your next vacation collects massive amounts of data, both on the ground and in the air. Routine operations like in-flight sales and food distribution, the plane’s engine performance, flight altitude, stressors and speed are all optimized by data analytics. The aerospace sector was actually one of the earliest adopters of data and would not have achieved the complexity it has today without it. Here’s how some aerospace engineers are using data to their advantage:
- Aerodynamics
Example: By analyzing airflow data over a plane’s wings and fuselage, engineers can reduce drag and improve fuel efficiency using computational fluid dynamics (CFD). - Engine Performance Data
Example: Engine performance can be monitored in real-time to optimize fuel combustion processes, which results in more efficient thrust and reduced emissions. - Flight Data Analysis (FDA)
Example: Data analytics enhances in-flight communication systems by predicting and mitigating potential signal disruptions, ensuring seamless connectivity for both crew and passengers.
Environmental Engineering
It’s essential for an environmental engineer to understand the impact human activity has on our ecosystem. That’s where big data comes in. Engineers use data to find environmentally friendly solutions, such as designing safe routes for waste collection, allocating water treatment processes or designing energy-efficient infrastructure. Examples of how data is used in environmental engineering include:
- Predicting Pollution Hotspots
Example: Environmental engineers can monitor air quality to identify pollution hotspots and reduce industrial emissions using geospatial analytics. - Geospatial Data Analysis
Example: By analyzing satellite imagery and climate data, engineers can predict high-risk erosion areas and design effective control measures. - Research on Environmental Impacts
Example: Assessing construction impacts on noise, habitat disruption and water usage can inform engineers’ designs for eco-friendly construction plans using environmental impact assessments.
Electrical Engineering
Electrical engineers collect real-time data from various electrical and electronic devices, systems and processes. These might include power grids, sensors or communication networks. That data is then analyzed to make informed decisions on the performance, reliability and safety of electrical systems. Examples include:
- Power Grid Management
Example: Engineers analyze data from smart meters and grid sensors to optimize power distribution, reduce outages and improve energy efficiency. - Smart Systems
Example: Analyzing data from smart thermostats and lighting systems helps develop algorithms to improve energy efficiency and user comfort. - Real-Time Monitoring
Example: Engineers use real-time data to monitor and control robots, optimizing their performance, enhancing precision and ensuring safety, thereby improving assembly line efficiency and reducing errors.
Mechanical Engineering
Data analytics are essential to fine-tuning systems and machinery in mechanical engineering, particularly in the automotive industry. Modern cars can collect data from the world outside, including road conditions, weather and global position. This data is used to make future models run more smoothly and improve overall designs and performance, in areas including:
- Product Design and Development
Example: Engineers analyze sensor data from test vehicles to identify and address design flaws early, leading to more reliable and efficient vehicles. - Automation
Example: Data analytics monitors manufacturing robots, optimizing cycle times, reducing errors and improving maintenance schedules to enhance production efficiency. - Energy Conversion
Example: Data engineers use data analytics to optimize internal combustion engines by analyzing fuel consumption, emissions and performance data, resulting in more fuel-efficient and eco-friendly engines.
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Sources:
https://pubs.acs.org/doi/epdf/10.1021/acs.iecr.2c01788
https://www.linkedin.com/advice/0/how-can-you-improve-accuracy-reliability-1c
https://www.allaboutcircuits.com/technical-articles/introduction-to-statistical-analysis-in-electrical-engineering/
https://www.linkedin.com/pulse/big-data-electrical-engineering-essaber-mohammed
https://www.ge.com/digital/documentation/meridium/Help/V43050/Default/Subsystems/ReliabilityAnalytics/Content/ReliabilityAnalyticsOverview.htm
https://www.qarmainspect.com/blog/quality-control-analytics