1,720,996 research outputs found
Cybersecurity in Railway : A Framework for Improvement of Digital Asset Security
No full textDigitalisation changes operation and maintenance in railways. Emerging digital technologies facilitate implementation of enhanced eMaintenance solutions through utilisation of distributed computing and artificial intelligence. In railway, the digital technology deployment is expected to improve the railway system’s sustainability, availability, reliability, maintainability, capacity, safety, and security including cybersecurity. In digitalised railway, aspects of cybersecurity are essential in order to achieve overall system dependability. Lack of cybersecurity imposes negative impacts on the railways like reputational damage, heavy costs, service unavailability and risk to the safety of employees and passengers. It has been observed, through open access data, that many railway organizations focus on detective measures of security threats with less emphasis on forecasting of cyber-attacks. In order to prepare in advance for cyberattacks, it is essential that Information and Communication Technology (ICT) and Operational Technology (OT) in railways need to undergo continuous updating towards security analytics approach. This approach will help the railways to produce proactive security measures to cyberattacks. In this work, it has been observed that there exists some standards and guidelines related to cybersecurity in railways (e.g. AS 7770- Rail Cyber Security, APTA SS-CCS-004-16, BS EN 50159:2010+A1:2020). These standards and guidelines are proprietary (i.e. either organization-specific or country-specific) and are followed by most of the railway organizations. These proprietary standards and guidelines lack in providing a holistic approach to enable interoperability, scalability, orchestration, adaptability, and agility for railway’s stakeholders. Therefore, there is a need for a generic cybersecurity framework for digitalized railways to facilitate proactive cybersecurity and threat intelligence sharing within the railways. The proposed framework, i.e., Cybersecurity Information Delivery Framework has been developed by integrating existing models, technologies, and standards to minimize the risks of cyber-attacks in the railways. The framework maps different layers of Open System Architecture for Condition-Based Maintenance (OSA-CBM) in the context of cybersecurity to deliver threat intelligence. The framework implements extended Cyber Kill Chain (CKC) and Industrial Control System (ICS) Kill Chain for detecting cyberattacks. The framework also incorporates proposed Railway Defender Kill Chain (RDKC) that enables proactive cybersecurity. Therefore, the proposed framework enables proactive cybersecurity and shares threat intelligence for improving cybersecurity in railways.
Rail Surface Defect Detection and Severity Analysis using CNNs on Camera and Axle Box Acceleration Data
Full text linkRail surface defect detection is a relevant problem in the field of
data-driven railway maintenance. Artificial intelligence and neural networks (NN) for axle box acceleration (ABA) or camera data
show great potential for defect detection and classification. However, a sufficient amount of labeled training data is required, all the
more if the defect severity is to be estimated. A unique dataset of
time-synchronized ABA and camera data is employed that contains
labeled defect instances. For the image analysis, RetinaNet as a
single-stage object detector (with the backbone of ResNet-50 and a
feature pyramid network) is used to achieve high classification performance for the two most common rail surface defects (squat and
corrugation). Additionally, a machine learning-based method on
ABA data to estimate defect severity levels (low, medium, heavy)
is proposed. False positives are detected in the original labels by
both classifiers during evaluation. The inspection of the false positives in image data reveals that defects have been overlooked in the
initial labeling. The insights of this work help to reduce the dependency on labeled data by using only a few labeled samples and
by exploiting complementary data sources instead of increasing the
number of labeled instances
Analysis of systematic influences on the insulation resistance of electronic railway interlocking systems
No full textQuality Assurance in Flow Through Oil and Gas Pipelines
No full textOil and gas pipelines are extensively used in the energy industry. The optimal performance of these pipelines is essential to maintaining energy supply cost-effectively. In downstream operations, flow assurance is of paramount importance. Control valves are vital components of the oil and gas pipelines as they control and regulate the flow parameters. The valves have complex flow areas resulting in the generation of high shear forces which causes strong emulsification in the mixture. This emulsified mixture is difficult to separate and results in extra resources such as additional separation time and chemical additives resulting in a significant increase in the cost of the separation process. In the present work, the effect of the presence of a valve on enhanced mixing in oil and gas pipelines has been quantified. Novel indicators namely, Mixing Coefficient Mc, Modified Mixing Coefficient (MMc) and Velocity-involved Modified Mixing Coefficient (VMMc) based on the in-situ properties have been used for quantifying the mixing behaviour. The computational Fluid Dynamics based globe valve model has been simulated using different velocities and oil volume fractions. Various cross-sectional planes inside the valve and in the straight pipe are created and, Mc, MMc and VMMc are computed at those planes. The mixing behaviour of the valve has been quantified and a considerable increase in the mixing has been observed as compared to the straight pipe. Suggestions have been provided as to how to minimise the mixing effects through the design modifications.</p
Remaining Useful Life Estimation for Anti-friction Bearing Prognosis Based on Envelope Spectrum and Variational Autoencoder
Full text linkAnti-friction bearings (AFB) are crucial structural components conveying rotating motions in a variety of mechanical systems. To avoid unscheduled breakdowns and fatal failures, remaining useful life (RUL) prediction is of great practical significance in industrial practice for prognostics health management, e.g., optimizing maintenance plan for component replacements. Recently, the artificial intelligence (AI) advancements have provided effective data-driven models for bearing prognostics using machine learning. In this paper, using the variational auto-encoder (VAE) networks as the regression backbone, the bearing RUL is estimated using envelope spectra via measured vibrational data. First, the envelope spectra are utilized for bearing fault detection and the network input features. After the fault is detected, the VAE is used for learning the probabilistic mapping from the spectral input to the estimated RUL value, given its good probabilistic and generative properties over the classical auto-encoder (AE) in content generation and variational inference. The application of the method to the run-to-failure measured vibration data from the experimental rig available online have shown its efficacy in bearing RUL estimation.</p
Smart Online Monitoring of Industrial Pipeline Defects
No full textAcoustic Wave Reflection (AWR) approach seems to be the future avenue for long pipeline monitoring, typically for the oil and gas industries. There are several research studies are available on successful detection of defects using this AWR approach in the pipelines based on the laboratory scaled experiments. The method seems to be successfully applied to few industrial scale pipelines as well. The paper is proposing a smart online monitoring system using this AWR approach together with the modern instrumentation and Internet of Things (IoT) features to integrated wireless sensor node, input acoustic wave signal optimisation and then remote collection of the AWR signal to determine the pipe defect location using the piping layout with the geographical positioning system (GPS). The paper presents the proposed smart online monitoring system
Research on Visual Detection Method of Cantilever Beam Cracks Based on Vibration Modal Shapes
No full textA visual detection method is proposed in this paper to identify cracks in a cantilever beam crack. The method takes full advantages of high spatial resolution of image sensing, relying only on cost-effective ordinary frame rate camera to record the process of free vibration of the cantilever beam, and combines with singular value decomposition method to obtain the vibration mode shapes of the cantilever beam. Then modal shape differences from baseline are taken as the features for detection and diagnosis The effectiveness of the first-order vibration mode shape difference in cantilever beam crack size and location detection is verified by both simulation and experiment.</p
Rotor and Bearing Fault Classification of Rotating Machinery Using Extracted Features from Experimental Vibration Data and Machine Learning Approach
No full textE-monitoring of operation and maintenance task under difficult visual work environment
No full textGodkänd; 2012; 20121214 (andbra
Cybersecurity Issues and Challenges in Industry 4.0
No full textThe convergence of information technology (IT) and operational technology (OT) and the associated paradigm shift toward fourth industrial revolution (aka Industry 4.0) in companies has brought tremendous changes in technology vision with innovative technologies such as robotics, big data, cloud computing, online monitoring, internet of things (IoT), cyber-physical systems (CPS), cognitive computing, and artificial intelligence (AI). However, this transition towards the fourth industrial revolution has many benefits in productivity, efficiency, revenues, customer experience, and profitability, but also imposes many challenges. One of the challenges is to manage and secure large amount of data generated from internet of things (IoT) devices that provide many entry points for hackers in the form of a threat to exploit new and existing vulnerabilities within the network. This chapter investigates various cybersecurity issues and challenges in Industry 4.0 with more focus on three industrial case studies.</p
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