Browsing by Author "Ruiz-Carcel, Cristobal"

Now showing 1 - 6 of 6
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    An autonomous rail-road amphibious robotic system for railway maintenance using sensor fusion and mobile manipulator
    (Elsevier, 2023-08-02) Liu, Haochen; Rahman, Miftahur; Rahimi, Masoumeh; Starr, Andrew; Durazo-Cardenas, Isidro; Ruiz-Carcel, Cristobal; Ompusunggu, Agusmian; Hall, Amanda; Anderson, Robert
    The current maintenance of railway infrastructure replies heavily on human involvement, requiring possession of the track section during maintenance, resulting in high costs and inefficient execution. This paper proposes an autonomous rail-road amphibious robotic system for railway inspection and maintenance tasks. By virtue of its road and rail-autonomous mobility, it is able to execute the complete maintenance execution flow in multiple phases. The system provides flexible track job location access, low-cost maintenance execution, and reduced track network possession. The payload mobile manipulator and sensor fusion enhance the system's capabilities for multiple types of inspection and repair. The design of a command and control system was guided by a rule-based expert system strategy to enable remote operation of the whole system. The developed demonstrator of a track wheel accompanied unmanned ground vehicle was integrated and demonstrated in both operational and realistic track environments with multiple testing activities of remote operation, navigation, accurate job detection, inspection, and repair, confirming effective job completion and logical human interaction. The proposed method produces an outstanding hardware-software integrated robotic inspection and repair system with a high level of technological readiness for autonomous railway maintenance and intelligent railway asset management.
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    Change detection in streaming data analytics: a comparison of Bayesian online and martingale approaches
    (Elsevier, 2020-12-18) Namoano, Bernadin; Emmanouilidis, Christos; Ruiz-Carcel, Cristobal; Starr, Andrew G.
    On line change detection is a key activity in streaming analytics, which aims to determine whether the current observation in a time series marks a change point in some important characteristic of the data, given the sequence of data observed so far. It can be a challenging task when monitoring complex systems, which are generating streaming data of significant volume and velocity. While applicable to diverse problem domains, it is highly relevant to monitoring high value and critical engineering assets. This paper presents an empirical evaluation of two algorithmic approaches for streaming data change detection. These are a modified martingale and a Bayesian online detection algorithm. Results obtained with both synthetic and real world data sets are presented and relevant advantages and limitations are discussed.
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    Data-based detection and diagnosis of faults in linear actuators
    (IEEE, 2018-03-27) Ruiz-Carcel, Cristobal; Starr, Andrew
    Modern industrial facilities, as well as vehicles and many other assets, are becoming highly automated and instrumented. As a consequence, actuators are required to perform a wide variety of tasks, often for linear motion. However, the use of tools to monitor the condition of linear actuators is not widely extended in industrial applications. This paper presents a data-based method to monitor linear electro-mechanical actuators. The proposed algorithm makes use of features extracted from electric current and position measurements, typically available from the controller, to detect and diagnose mechanical faults. The features are selected to characterize the system dynamics during transient and steady-state operation and are then combined to produce a condition indicator. The main advantage of this approach is the independence from a need for a physical model or additional sensors. The capabilities of the method are assessed using a novel experimental linear actuator test rig specially designed to recreate fault scenarios under different operating conditions.
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    Data-driven wheel slip diagnostics for improved railway operations
    (Elsevier, 2022-09-27) Namoano, Bernadin; Ruiz-Carcel, Cristobal; Emmanouilidis, Christos; Starr, Andrew G.
    Wheel slip activity detection is crucial in railway maintenance, as it can contribute to avoiding wheel damage but also track deteriorations leading to significant maintenance costs, trains delays, as well as the risk of accidents. Wheel slip activity is characterised by lower adhesion between track and wheel, especially in braking conditions, locking the wheels. It is complex to model or predict, being influenced by a multitude of factors including ambient conditions, global vehicle load, track and axle quality, leaves and objects present on the rail, steep incline, oxidation of the rails, and braking forces applied to the wheels. This paper presents a combined wavelet and tuned Long-Short Term Memory (LSTM) approach for the detection of wheel slip from time series data collected from real-world trains. Results provide evidence of superior performance over methods such as decision trees and random forests, naïve Bayes, k-nearest neighbours, logistic regression, and support vector machines.
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    Enabling the human in the loop: Linked data and knowledge in industrial cyber-physical systems
    (Elsevier, 2019-03-19) Emmanouilidis, Christos; Pistofidis, Petros; Bertoncelj, Luka; Katsouros, Vassilis; Fournaris, Apostolos; Koulamas, Christos; Ruiz-Carcel, Cristobal
    Industrial Cyber-Physical Systems have benefitted substantially from the introduction of a range of technology enablers. These include web-based and semantic computing, ubiquitous sensing, internet of things (IoT) with multi-connectivity, advanced computing architectures and digital platforms, coupled with edge or cloud side data management and analytics, and have contributed to shaping up enhanced or new data value chains in manufacturing. While parts of such data flows are increasingly automated, there is now a greater demand for more effectively integrating, rather than eliminating, human cognitive capabilities in the loop of production related processes. Human integration in Cyber-Physical environments can already be digitally supported in various ways. However, incorporating human skills and tangible knowledge requires approaches and technological solutions that facilitate the engagement of personnel within technical systems in ways that take advantage or amplify their cognitive capabilities to achieve more effective sociotechnical systems. After analysing related research, this paper introduces a novel viewpoint for enabling human in the loop engagement linked to cognitive capabilities and highlighting the role of context information management in industrial systems. Furthermore, it presents examples of technology enablers for placing the human in the loop at selected application cases relevant to production environments. Such placement benefits from the joint management of linked maintenance data and knowledge, expands the power of machine learning for asset awareness with embedded event detection, and facilitates IoT-driven analytics for product lifecycle management.
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    Estimation of powder mass flow rate in a screw feeder using acoustic emissions
    (Elsevier, 2017-07-08) Ruiz-Carcel, Cristobal; Starr, Andrew; Nsugbe, Ejay
    Screw feeders are widely used in powder processes to provide an accurate and consistent flow rate of particles. However this flow rate is rarely measured or controlled. This investigation explores the use of generalised norms and moments from structural-borne acoustic emission (AE) measurements as key statistics indicators for the estimation of powder mass flow rate in a screw feeder. Experimental work was carried out acquiring AE measurements from an industrial screw feeder working with four different types of material at different dispensation rates. Signal enveloping was used in first place to eliminate high frequency components while retaining essential information such as peaks or bursts caused by particle impacts. Secondly a set of generalised norms and moments is extracted from the signal, and their correlation with mass flow rate was studied and assessed. Finally a general model able to estimate mass flow rate for the four different types of powders tested was developed.