The future of industrial inspections is undergoing a transformation, driven by the integration of real-time data and 3D models. This technological shift has an impact on how companies monitor, maintain, and optimise their assets. From drones and IoT sensors to augmented reality and machine learning, these cutting-edge tools are reshaping the landscape of industrial inspections, offering unprecedented accuracy, efficiency, and safety.
To analyse the evolving field of industrial inspections, this article explores the key technologies shaping its future. It delves into the role of real-time data collection and analysis in enhancing decision-making processes. The piece also examines how 3D modelling techniques, including digital twins and building information modelling (BIM), are revolutionising asset management. Additionally, it presents case studies showcasing successful implementations of these technologies, highlighting their practical benefits in various industrial settings.
The Evolution of Industrial Inspection Technologies
Traditional Inspection Methods
Industrial inspection technologies have undergone significant transformations since their inception. The journey began with traditional methods that relied heavily on manual processes. X-ray techniques, discovered by Wilhelm Conrad Röntgen in 1895, were among the first to be applied in industrial settings [1]. Magnetic particle crack detection, pioneered by S.M. Saxby in 1868 and William Hoke in 1917, found its industrial application in 1929 [1]. Penetrant testing, which originated in the 19th century, gained prominence during World War II, particularly in the aircraft industry [1].
Introduction of Real-time Data
The advent of real-time data processing has revolutionised quality inspection processes. This technology enables continuous input, processing, and output of data within milliseconds, a stark contrast to traditional batch processing methods [2]. Real-time data processing offers unparalleled speed, efficiency, and accuracy in quality control. It allows for automated defect detection in production lines, with advanced algorithms analysing data from sensors and cameras to identify flaws instantly [2]. This approach has significantly reduced human error and increased the accuracy of quality inspections.
Emergence of 3D Modelling in Inspections
The integration of 3D modelling techniques has further transformed industrial inspections. 3D modelling software has simplified the engineering process, allowing teams to develop integrated designs [3]. This technology has proven to reduce design time significantly, with accurate design drawings, material quantities, and equipment data extracted from 3D models in a fraction of the time compared to traditional methods [3]. The use of 3D designs has also changed project presentations, making walkthroughs for design, construction, commissioning, operations, and maintenance much easier [3]. Terrestrial LiDAR, a laser scanning tool, has emerged as an excellent resource for substation expansion projects, providing comprehensive surveys of equipment, structures, and foundations in just a few hours [3].
Real-time Data Collection and Analysis
IoT Sensors and Data Streams
Industrial IoT sensors play a crucial role in the data collection process, measuring various conditions and sending data points to gateways for cloud transmission [4]. These robust devices operate seamlessly within machine data platforms, responding as data is generated to enable IIoT control [4]. A wide variety of sensors are designed to measure specific conditions, including equipment status, environmental factors, motion, temperature, and vibration [4].
IoT data collection involves using sensors to track device performance in real-time, facilitating activities such as predictive maintenance [5]. This approach saves energy costs while enhancing machine productivity and longevity [5]. IoT sensors can monitor environmental data such as humidity, temperature, movement, and air quality, providing a basis for tracking physical work conditions to avoid calamities like floods and air toxicity [5].
Edge Computing for Instant Processing
Edge computing has emerged as a crucial technology for reducing latency and data transfer costs in industrial settings. By moving computational power closer to data-generating sources, edge computing achieves low latency by reducing the physical distance data must travel for processing and analysis [6]. This is particularly important in Industry 4.0 environments, where real-time responses and data-driven insights are essential [6].
In industrial edge computing, IIoT sensors attached to assembly line robots can transfer speed, temperature, and volume data to nearby edge servers for quick analysis [6]. This enables maintenance and operations supervisors to receive machine health and productivity insights promptly, allowing for preemptive actions to prevent assembly line downtime and costly equipment damage [6].
AI-powered Predictive Maintenance
Artificial intelligence tools are designed to mimic human intelligence for specific tasks, allowing for automated analysis of operational conditions to predict potential equipment failures [7]. By assessing current machine performance against baseline data, AI tools can pinpoint small reductions in efficiency that may suggest the need for maintenance [7].
To evaluate machine performance and maintenance needs, AI solutions require continuous access to historical and current data [7]. Machine learning algorithms play a critical role in separating relevant data points (signals) from noise, enabling more accurate predictions [7]. The rise of Industry 4.0 and smart factories has provided massive datasets for companies to utilise in predictive maintenance, provided they have the right ML algorithms and AI frameworks in place [7].
3D Modeling Techniques in Industrial Inspections
Photogrammetry and Structure from Motion
Photogrammetry has emerged as a powerful tool in industrial inspections, offering accurate data on-demand and producing specific outputs like digital terrain models or basemaps [8]. This technique involves obtaining precise measurements and three-dimensional models from photographs, analysing the geometry and spatial properties of images to extract data and create accurate representations of objects and environments [9].
In telecom inspections, specialised software like PIX4Dinspect simplifies the process. A drone flight around a tower can provide all the necessary data to create a digital twin. The software uses AI to automatically identify antenna features, such as angle, orientation, and azimuth, before presenting the digital twin for analysis [8].
LiDAR Scanning for Precise Measurements
LiDAR scanning has revolutionised industrial inspections by offering rapid and accurate 3D modelling capabilities. This technology uses laser devices to capture digital images of objects, producing a cloud of points that represents all solid obstacles encountered [10]. LiDAR scans can document existing conditions of a plant in just two to three days, compared to weeks when using traditional surveying methods. These scans are accurate to 1/8th of an inch, surpassing the reliability of traditional surveying techniques [11].
The 3D models created from LiDAR scans enable engineers to measure anything in the files, including pipe diameters, structure diameters, and distances. This capability has an impact on the design process for new systems, making it more efficient and accurate [11].
Digital Twin Creation and Applications
Digital twins have become integral to modern industrial inspections. A digital twin is a formal digital representation of an asset, process, or system that captures attributes and behaviours suitable for communication, storage, interpretation, or processing within a specific context [12]. These virtual replicas contain data about their real-world counterparts, including specifications, design models, production process data, and operational information [12].
Digital twins incorporate computational or analytic models to describe, understand, and predict operational states and behaviours. They may include physics-based models, engineering simulations, data models based on statistics, machine learning, and AI. Additionally, 3D models and augmented reality models can aid human understanding of operational states or behaviours of real-world objects [12].
The application of digital twins in industrial inspections has led to improved project coordination, better decision-making, and more efficient quality control processes. By providing a virtual representation of physical assets, digital twins enable stakeholders to identify defects, discrepancies, and potential hazards more effectively, reducing the need for costly rework [9].
Case Studies: Successful Implementation of Real-time Data and 3D Models
Oil and Gas Industry
The Norwegian Petroleum Directorate has leveraged machine learning to analyse downhole and seismic well data, enhancing understanding of petroleum systems and identifying missed hydrocarbon zones [13]. CGG, a global provider of earth science data, has developed new 3D technologies for carbon capture, storage, and utilisation, utilising seismic imaging and reservoir models to meet regulatory requirements [13]. Continental Resources conducted 3D seismic surveys in the Williston Basin, enabling better control over sandstone drilling and optimal formation thickness identification [13].
Manufacturing Sector
Manufacturers have gained deeper insights into their 3D models through geometric search, streamlining supply chain operations [14]. This approach has eliminated redundancies in parts inventory, potentially saving millions of dollars annually and enhancing supply chain resilience [14]. By consolidating geometrically identical components, such as screws, organisations have reduced excess inventory and improved procurement efficiency [14]. This standardization has saved engineers time during the design process and reduced time to market [14].
Infrastructure and Construction
Digital twins in construction have revolutionised project management by combining 3D models, sensor data, and real-time performance data [15]. This technology has enabled engineers to simulate extreme weather impacts on buildings and bridges, identifying potential risks and implementing preventative measures [15]. Corgan, an architecture firm, utilised Matterport Pro2 cameras to create virtual punch lists and share 3D digital twins with stakeholders, enhancing collaboration throughout the construction process [16]. Swinerton, another construction company, implemented digital twins to reduce client travel time by 100% and cut MEP and architect travel time by 50%, ultimately saving clients thousands of dollars by preventing costly errors and rework [16].
Conclusion
The integration of real-time data and 3D models is causing a revolution in industrial inspections, bringing about significant improvements in accuracy, efficiency, and safety. These technologies have an influence on decision-making processes, asset management, and overall operational effectiveness across various industries. From IoT sensors and edge computing to AI-powered predictive maintenance and digital twins, companies are harnessing these tools to gain deeper insights into their operations and to optimise their inspection procedures.
The case studies presented highlight the practical benefits of these technologies in real-world settings. In the oil and gas industry, manufacturing sector, and infrastructure projects, the implementation of real-time data analysis and 3D modelling techniques has led to better resource management, cost savings, and enhanced collaboration. As these technologies continue to evolve, they are set to further transform industrial inspections, paving the way for smarter, more responsive, and highly efficient industrial processes in the future.
References
[1] – https://www.ndt.net/article/wcndt00/papers/idn378/idn378.htm
[2] – https://risingwave.com/blog/improving-quality-inspection-with-real-time-data-processing/
[3] – https://www.stantec.com/en/ideas/topic/stantec-era/from-stantec-era-5-ways-3d-modeling-changing-the-way-we-design-power-projects.html
[4] – https://www.machinemetrics.com/connectivity/hardware/iiot-sensors
[5] – https://ubidots.com/blog/iot-data-collection/
[6] – https://www.trentonsystems.com/en-us/resource-hub/blog/edge-computing-fourth-industrial-revolution
[7] – https://www.advancedtech.com/blog/the-role-of-ai-in-predictive-maintenance/
[8] – https://www.pix4d.com/blog/five-industries-that-use-photogrammetry
[9] – https://flyguys.com/photogrammetry-in-construction/
[10] – https://control.com/technical-articles/using-3d-inspection-to-improve-part-quality-control-and-production-yields/
[11] – https://www.clarknexsen.com/blog-how-3d-lidar-scanning-adds-value-for-industrial-manufacturing-clients/
[12] – https://hub.iiconsortium.org/portal/Whitepapers/5e95c68a34c8fe0012e7d91b
[13] – https://rextag.com/blog/3D-visualization-for-Oil-Gas-industry-leaders-of-the-O-G-market-in-2022-
[14] – https://www.supplychainbrain.com/blogs/1-think-tank/post/34818-time-to-rethink-the-role-of-3d-models-in-manufacturing-and-logistics
[15] – https://www.toobler.com/blog/digital-twin-in-construction
[16] – https://matterport.com/learn/digital-twin/construction?srsltid=AfmBOooA_NWemJ7a2kBXmxmCcUsgKlIBrawlY3bWmdZtvNBtKJR0mc5P
Industrial inspection FAQs
- How does real-time data improve industrial inspection accuracy?Real-time data enhances inspection accuracy by providing continuous monitoring, allowing immediate action on detected issues, reducing downtime, and ensuring efficient asset management.
- Why is 3D modeling important in industrial inspections?3D modeling offers detailed visualizations of assets, enabling precise measurements, easier defect identification, and better collaboration, leading to more effective inspections.
- What are the benefits of AI in predictive maintenance?AI in predictive maintenance helps predict equipment failures, optimize maintenance schedules, and reduce downtime by analyzing real-time and historical data.
- How do IoT sensors optimize industrial inspections?IoT sensors collect crucial data on equipment conditions like temperature and vibration, enabling real-time monitoring and predictive maintenance for better inspection results.
- Can digital twins reduce errors in industrial projects?Yes, digital twins enhance project management by improving collaboration and visualization, reducing costly errors, especially in complex industries like construction and manufacturing.
AUTHOR: ANAGHA