Browsing by Keyword "Railway"
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Item Activating inclusive growth in railway SMEs by workplace innovation(2020-09) Carranza, Garazi; Garcia, Marta; Sanchez, Begoña; Policies for Innovation and TechnologyThe digital revolution is happening, transforming the way we move and produce. Success in the digital revolution means that the rail industries need to use the best available technologies focusing on people. The managerial and organizational practices adopted by railway entities have considerable significance for Railway's ability to succeed in global competition. One of the challenges for railway entities is to deliver innovative products, offering quickness and flexibility to respond to changing demands from their customers. Non-technological innovations and especially Workplace innovation, have a key role to play in the digitalization and acceleration of technological developments, therefore in the railway sector competitiveness. This draws attention to the importance of innovation climate and employees' commitment aiming at improving staff motivation and working conditions, thereby enhancing labor productivity, organizational performance, innovation capability, reactivity to market change, and consequently business competitiveness. As with any emerging opportunity, there is no established path to follow to activate inclusive growth in railway SMEs to uptake Workplace innovation. To address these issues, this paper develops and tests a research model that covers individual behavior, organizational practices, and process practices of innovation among employees, analyzing the impact of Workplace Innovation on firm performance.Item Condition-Based Maintenance of HVAC on a High-Speed Train for Fault Detection(2021-06-12) Ciani, Lorenzo; Guidi, Giulia; Patrizi, Gabriele; Galar, Diego; Tecnalia Research & InnovationReliability-centered maintenance (RCM) is a well-established method for preventive maintenance planning. This paper focuses on the optimization of a maintenance plan for an HVAC (heating, ventilation and air conditioning) system located on high-speed trains. The first steps of the RCM procedure help in identifying the most critical items of the system in terms of safety and availability by means of a failure modes and effects analysis. Then, RMC proposes the optimal maintenance tasks for each item making up the system. However, the decision-making diagram that leads to the maintenance choice is extremely generic, with a consequent high subjectivity in the task selection. This paper proposes a new fuzzy-based decision-making diagram to minimize the subjectivity of the task choice and preserve the cost-efficiency of the procedure. It uses a case from the railway industry to illustrate the suggested approach, but the procedure could be easily applied to different industrial and technological fields. The results of the proposed fuzzy approach highlight the importance of an accurate diagnostics (with an overall 86% of the task as diagnostic-based maintenance) and condition monitoring strategy (covering 54% of the tasks) to optimize the maintenance plan and to minimize the system availability. The findings show that the framework strongly mitigates the issues related to the classical RCM procedure, notably the high subjectivity of experts. It lays the groundwork for a general fuzzy-based reliability-centered maintenance method.Item Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach(2021-06-16) Gálvez, Antonio; Diez-Olivan, Alberto; Seneviratne, Dammika; Galar, Diego; Tecnalia Research & Innovation; INDUSTRY_THINGSHeating, ventilation, and air conditioning (HVAC) systems installed in a passenger train carriage are critical systems, whose failures can affect people or the environment. This, together with restrictive regulations, results in the replacement of critical components in initial stages of degradation, as well as a lack of data on advanced stages of degradation. This paper proposes a hybrid model-based approach (HyMA) to overcome the lack of failure data on a HVAC system installed in a passenger train carriage. The proposed HyMA combines physics-based models with data-driven models to deploy diagnostic and prognostic processes for a complex and critical system. The physics-based model generates data on healthy and faulty working conditions; the faults are generated in different levels of degradation and can appear individually or together. A fusion of synthetic data and measured data is used to train, validate, and test the proposed hybrid model (HyM) for fault detection and diagnostics (FDD) of the HVAC system. The model obtains an accuracy of 92.60%. In addition, the physics-based model generates run-to-failure data for the HVAC air filter to develop a remaining useful life (RUL) prediction model, the RUL estimations performed obtained an accuracy in the range of 95.21–97.80% Both models obtain a remarkable accuracy. The development presented will result in a tool which provides relevant information on the health state of the HVAC system, extends its useful life, reduces its life cycle cost, and improves its reliability and availability; thus enhancing the sustainability of the system.