Browse Topic: Big data
Metallurgical cranes have a high risk of structural fatigue damage and failure under complex working conditions such as high temperature, heavy load, and strong electromagnetic interference. This article proposes a data-driven structural fatigue damage health monitoring system. This system integrates fiber Bragg grating sensing technology, rigid flexible coupling multi-body dynamics simulation, and big data analysis methods to construct a sensor optimization layout strategy based on rigid flexible coupling virtual prototype simulation, achieving real-time perception of stress states in key parts such as the mid span and end beam corners of the main beam. Develop a visualization system that integrates health monitoring, damage diagnosis, and life prediction. This system can dynamically evaluate the structural health status of metallurgical cranes and predict the remaining life of the structure based on a nonlinear cumulative damage model. On site engineering applications have shown that the monitoring and prediction visualization system can effectively improve the intelligent and safe operation and maintenance level of metallurgical cranes, providing a data foundation and possibility for their predictive maintenance.
The global automotive industry has reached a new era. If 2025 was defined by the cautious exploration of “experimental pilots” and the collection of vast data lakes from connected vehicle fleets, 2026 marks the year that data finally gains a mind of its own within the assembly plant. We are witnessing a transition from passive automation to integrated, agentic autonomy. This is a shift that moves beyond simple programmed robotic arms and toward systems capable of independent reasoning and real-time optimization. This evolution is not just a technical upgrade; it is a fundamental restructuring of how vehicles are built, de-risked, and scaled in an increasingly volatile global economy.
The article is devoted to a comprehensive analysis of the digital transformation of education using the example of a project to train engineering personnel for the innovative transport industry in Russia. Special attention is paid to the introduction of hybrid formats, digital platforms, inclusivity, issues of digital inequality, as well as the experience of the National Research Center of the Russian Federation FSUE NAMI and interaction with leading universities in the country. A comparative analysis with foreign initiatives, including modern AI solutions for inclusive education, is presented, as well as the impact of the project to create educational and methodological centers on the professional motivation of teachers.
We present a novel processing approach to extract a ship traffic flow framework in order to cope with problems such as large volume, high noise levels and complexity spatio-temporal nature of AIS data. We preprocess AIS data using covariance matrix-based abnormal data filtering, develop improved Douglas-Peucker (DP) algorithm for multi-granularity trajectory compression, identify navigation hotspots and intersections using density-based spatial clustering and visualize chart overlays using Mercator projection. In experiments with AIS data from the Laotieshan waters in the Bohai Bay, we achieve compression rate up to 97% while maintaining a key trajectory feature retention error less than 0.15 nautical miles. We identify critical areas such as waterway intersections and generate traffic flow heatmap for maritime management, route planning, etc.
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In-Use emission compliance regulations globally mandate that machines meet emission standards in the field, beyond dyno certification. For engine manufacturers, understanding emission compliance risks early is crucial for technology selection, calibration strategies, and validation routines. This study focuses on developing analytical and statistical methods for emission compliance risk assessment using Fleet Intelligence Data, which includes high-frequency telematics data from over 500K machines, reporting more than 1000 measures at 1Hz frequency. Traditional analytical methods are inadequate for handling such big data, necessitating advanced methods. We developed data pipelines to query measures from the Enterprise Data Lake (A Structured Data storage system), address big data challenges, and ensure data quality. Regulatory requirements were translated into software logic and applied to pre-processed data for emission compliance assessment. The resulting reports provide actionable insights on NOx sensor activity, engine warmup operations, high-risk drive cycles, and load profiles across different operation regimes. This approach significantly reduces the reliance on costly and labor-intensive physical testing with Portable Emissions Measurement Systems (PEMS) by integrating advanced analytical methods into the workflow. By leveraging high-frequency telematics data, this method enables engineers to identify failed machines in the field more efficiently. It also provides valuable insights and reasoning behind these failures, facilitating quicker and more informed decision-making. This not only enhances emission compliance monitoring but also optimizes resource allocation and reduces overall regulatory risks. In summary, the developed methods enable effective emission compliance monitoring, reduce regulatory risks, and help optimize calibration strategies by understanding customer usage patterns. These methods are scalable for various emission regulations.
In view of the complexity of railway engineering structure, the systematicness of professional collaboration and the high reliability of operation safety, this paper studied the spatial-temporal information data organization model with all elements in whole domain for Shuozhou-Huanghua Railway from the aspect of Shuozhou-Huanghua Railway spatial-temporal information security. Taking the unique spatial-temporal benchmark as the main line, the paper associated different spatial-temporal information to form an efficient organization model of Shuozhou-Huanghua Railway spatial-temporal information with all elements in the whole domain, so as to implement the effective organization of massive spatial-temporal information in various specialties and fields of Shuozhou-Huanghua Railway; By using GIS (Geographic Information System) visualization technology, spatial analysis technology and big data real-time dynamic rendering technology, it was realized the real-time dynamic visualization display of different spatial-temporal information and organization relationship of Shuozhou-Huanghua Railway, which provided auxiliary decision-making and spatial information technology support for independent prediction and early warning of Shuozhou-Huanghua Railway operation safety situation.
Big Data technologies have become quite ubiquitous in the last years, allowing for the storage of substantial amounts of data, typically flight test data as recorded by the flight test installation. On recent helicopter prototypes, we generate in excess of 50 GB of raw data per flight hour, usually in a format not adequate for efficient large-scale processing. With some specific optimizations and the setup of a specialized infrastructure, there are now practicable means to store timeseries in ways that allow for requests spanning hundreds or thousands of flights to complete within minutes, opening the way to some substantial savings and new insights. However, to make the most of these data and make informed decisions it is often quite important to store contextual data that go beyond the pure timeseries data, typically on helicopters where optional installations can have a significant impact on aircraft performance or behavior. This paper explores the various kinds of data and metadata related with flight tests, how to collect them and relate them with one another in order to maximize raw data value and avoid some common pitfalls. We define more accurately the data of interest, where to collect them and some ideas to further improve data collection in the future.
The Auto industry has relied upon traditional testing methodologies for product development and Quality testing since its inception. As technology changed, it brought a shift in customer demand for better vehicles with the highest quality standards. With the advent of EVs, OEMs are looking to reduce the going-to-market time for their products to win the EV race. Traditional testing methodologies have relied upon data received from various stakeholders and based on the same tests are planned. The data used is highly subjective and lacks variety. OEMs across the world are betting big on telematics solutions by pushing more and more vehicles with telematics devices as standard fitment. The data from such vehicles which gets generated in high levels of volume, variety and velocity can aid in the new age of vehicle testing. This live data cannot be simply simulated in test environments. The device generates hundreds of signals, frequently in a fraction of seconds. Multiple such signals can be combined to create KPIs that correspond to specific traits of vehicle health. Specific telematics KPIs can be used as inputs in test benches. With this data, the vehicle gets tested against real-world conditions. Another important aspect of vehicle testing is road conditions. OEMs can only do so much in testing the vehicles in various different road conditions. However, with telematics devices road condition data can be directly generated corresponding to the international roughness index. This data along with KPIs will bring in a new perspective of vehicle development testing and enable the manufacturer to better understand market problems and to take effective countermeasures. With these KPIs, Possibilities are limitless, and these data can be used to create a digital twin of the vehicle, enabling the OEM to assess the vehicle without even physically attending.
Using current technologies, a single “entry level” vehicle has millions of electrical signals sent through dozens of modules, sensors and actuators, and those signals can be sent over the air, creating a telemetry data that can be used for several ends. One electrical device is set up to have diagnosis, in order to make maintenance feasible and support repair, plus giving improvement directions for specialists on new developments and specifications, but in several cases the diagnosis can only determine the mechanism of failure, but not the event that triggered that failure. Current evaluation method involves teardown, testing and knowledge from the involved specialized team, but this implies in recovering of failed parts, which in larger automakers with thousands of dealers/repair shops, reduces the sample for analyses when there is a systemic issue with one component. This specificity is usual in Propulsions systems, regarding electro-mechanical devices, and sensors, also in electrochemical devices, such as batteries and others, when a systemic issue appears, the teardown reveals its failure, but now why it failed. Based on that information and needs a methodology using big data mining and tools combined with available telemetry data in order to detect statistically main events or contributors/variables that triggers a failure event. That sort of methodology is helpful and more agile since it doesn’t depend on recovering of parts to give directions of which potential event may trigger a failure event, supporting in systemic/application comprehension of any component failure which uses electrical signals monitored within vehicle, and doesn’t depend on extraction of failed components, it can use and consider every single failed vehicle, for one specific component, as basis for analyses and identification of failure event, which will support in systemic correction/improvement and adjustment/improvement of specification for future and specific developments.
The automotive industry is going through one of its greatest restructuring, the migration from internal combustion engines to electric powered / internet connected vehicles. Adapting to a new consumer who is increasingly demanding and selective may be one of the greatest challenges of this generation, Original Equipment Manufacturers (OEM) have been struggling to keep offering a diversified variety of features to their customers while also maintaining its quality standards. The vehicles leave the factory with an embedded SIM Card and a telematics module, which is an electronic unit to enable communication between the car, data center. Connected vehicles generate tens of gigabytes of data per hour that have the potential to be transformed into valuable information for companies, especially regarding the behavior and desires of drivers. One of the techniques used to gather quality feedback from the customers is the NPS it consists of open questions focused on top-of-mind feedback. Here is where AI and ML comes into play, using NLP and several other computational techniques to download, extract, structure, read, process, understand and categorize all this data into specific predetermined categories, allowing engineers to accelerate fixing quality issues and improving user experience. The ML model developed in this article identify costumer complains in an enormous data lake and groups them into categories. After a significative amount of data is collected and grouped into it enables the algorithm to predict future trends and together with real time connected vehicle data the model can alert the responsible engineers to develop an action to solve the problem without more customers even actually experience the failure. The ML algorithm is still on its development phase, but the initial results are promising, we have successfully processed more them 6 millioncustomers feedback finding problems with precision and accuracy close to 90%.
The main purpose of this research is to identify how the established quality methodologies, known worldwide as TQC (Total Quality Control) and TQM (Total Quality Management) are supported by the tools of the Quality 4.0 concept that similarly received influence from the disruptive technologies of Industry 4.0 in the last decade. In order to crosscheck the relationship among TQC and TQM and how Quality 4.0 supports these quality systems a qualitative investigation method was adopted through a survey questionnaire applied to one of the most important worldwide automobile company, based also in Brazil, Toyota of Brazil. Based on a literature review and relationship of concepts and synergy among them it was possible analyse and find out conclusions of this research work. The main results were identified as TQC and TQM are very well established concepts of quality and Quality 4.0 concepts and tools have been implemented on a path according to the markets importance prioritization, so then Toyota of Brazil is implementing in a slow motion, but started up using technologies of Industry 4.0 on Quality, promoting a leverage of excellence on quality function overall organization and settling down this Quality 4.0 level.
The Advancement in Connected vehicles Technology in recent years has propelled the use of concepts like the Internet of Things (IoT) and big data in the automotive industry. The progressive electrification of the powertrain has led to the integration of various sensors in the vehicle. The data generated by these sensors are continuously streamed through a telematics device on the vehicle. Data analytics of this data can lead to a variety of applications. Predictive maintenance is one such area where machine learning algorithms are applied to relevant data to predict failure. Field vehicle malfunction or breakdown is costly for manufacturers’ aftermarket services. In the case of commercial vehicles, downtime is the biggest concern for the customer. The use of predictive maintenance techniques can prevent many critical failures by tending to the root cause in the early stages of failure. Engine overheating is one such problem that transpires in diesel engines. Overheating of an engine may lead to various catastrophic failures like a warped cylinder head or cracked cylinder. It is essential to curb such problems at the early stages to save on warranty costs and establish confidence regarding our product in the customer's mind. Here Gaussian Mixture Model is applied to cleaned data to obtain the Engine Coolant Temperature Distribution. based on which the vehicles showing Overheating trends are classified into a separate class. These vehicles are then monitored and Early Failure Alerts for Overheating are triggered in the system for taking proactive measures to prevent the failure.
The global big data market had a revenue of $162.6 billion in 2021.1 Data is becoming more valuable to companies than gold. However, this data has been used, historically, without contributors’ informed consent and without them seeing a penny from the discoveries the data led to. This article discusses how non-fungible tokens (NFTs) can provide a helpful tool for pharmaceutical companies to track contributed data and compensate contributors accordingly. NFTs are unique, untradable cryptographic assets that can be tracked on a blockchain. NFTs provide a unique traceable token that cannot be replicated, providing a perfect tool to store biodata. The term biodata refers to details regarding a patient’s history and behavioral patterns.
Traditional methods of municipal domestic waste analysis and prediction lack precision, while most data’s sample size is not suitable for many neural networks. In this paper, combining the advantage of deep learning methods with the results of association analysis, a waste production prediction method TLSTM is proposed based on long short-term memory(LSTM). It is found that the most influencing factors are population, public cost, household and GDP. Meanwhile, the garbage production in Shanghai will continue to decline in the future, indicating the policy of refuse classification is effective. The R-square index and MSE index of the model were 0.55 and 76571.73 respectively, surpassing other state-of-the-art models. In cooperation with School of Environmental Science and Engineering at Shanghai Jiao Tong University, the dataset comes from the average data of the Shanghai Household Waste Management Regulation from 1980 to 2020. This research method has a certain guiding significance to both the related fields of municipal solid waste management and environmental planning and the application of neural network models in other fields.
Kontron and Intel experts explain how rugged, modular COM Express solutions reduce complexity and allow retrofit of autonomous systems on heavy mobile equipment. Continually transformed with more than a century's advances in capabilities, hydraulics and fuel efficiency, today's heavy mobile equipment must also become more intelligent and better connected. Technologies such as artificial intelligence (AI), deep learning, big data, GPS, 5G and computer vision are proving their mettle - empowering far more efficient ways of carrying out unique and demanding tasks via advanced telematics, advanced driver assistance systems (ADAS) or varying levels of autonomy. Heavy mobile equipment (HME) that can gather and apply data in real time operates and makes decisions in ways that humans cannot. This evolution toward automation promises not only leadership for manufacturers of more advanced systems, but also increased safety, economy, efficiency and ecological compatibility.
On the last generation of Airbus Helicopters rotorcrafts such as H175 or H160, dynamic systems data are collected in a systematic manner in order to perform advanced analytics. Main gearbox (MGB) oil temperature and oil pressure are key parameters to assess the overhaul status of the lubrication and cooling systems. This paper describes new ways of monitoring lubrication and cooling systems behavior, taking advantage of big data capabilities and advanced analytics such as machine learning and physical modeling based approaches.
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