Browsing by Keyword "Predictive maintenance"
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Item Advanced Prognostics for a Centrifugal Fan and Multistage Centrifugal Pump Using a Hybrid Model(Springer Science and Business Media Deutschland GmbH, 2023) Vila-Forteza, Marc; Jimenez-Cortadi, Alberto; Diez-Olivan, Alberto; Seneviratne, Dammika; Galar-Pascual, Diego; Juuso, Esko; Galar, Diego; Tecnalia Research & Innovation; IAPredictive maintenance is fully implemented in the oil and gas industry, and the impressive development of field sensors, big data, and digital twins offers a wide field for the ongoing experimentation and development of diagnostic and prognostic tools for machinery. Although a wide range of technologies and sensors is available, vibration analysis remains the preferred predictive technique for rotating machinery diagnostics. It is well-known, widely used, and has proven efficacious in evaluating the health of rotating machinery and preventing failures. Taking advantage of vibration analysis development and computing capabilities, this study develops three digital twins of one multistage centrifugal pump and two centrifugal fans using real vibration data and synthetic data. This hybrid model approach permits the use of failure data which are not usually found in the normal operation of these machines. The study improves and tunes the accuracy of those models using real operating data obtained from a distributed control system (DCS), thus obtaining results in accordance with process conditions. Maintenance decisions can be supported by these models. They are based on online vibration and process data; they diagnose the health of a machine and give its remaining useful life (RUL). The models may also be used for other API plant assets (multistage centrifugal pumps or centrifugal fans) by changing the configuration parameters and process DCS tags.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.Item Smart connected and interactive production control in a distributed environment(2011) Schlegel, Thomas; Thiel, Simon; Foursa, Maxim; Meo, Fabrizio; Larrañaga, Josu; Ibarbia, Jon Agirre; Haidegger, Géza; Mezgár, István; Paniti, Imre; Praturlon, Anja Herrmann; Canou, Joseph; ROBOTICA_AUTOMA; GENERALThe European research project INT-MANUS embedded in the I*PROMS European network of excellence addresses the increasing demand for flexibility and adaptivity, which is summarised by rapid reconfiguration of complete factories, flexible reaction to new demands as well as related aspects in human computer interaction (HCI), software, and production systems. The project's main goal has been to develop a new technology for the production plants of the future: the Smart Connected Control Platform (SCCP). This platform allows controlling a factory with the help of an open distributed and learning agent platform that integrates machines, robots, and human personnel. It offers an enterprise service bus like concept for dynamic and decentrally controlled production systems, which flexibly connects machines and IT systems like robotic transport systems, terminals, mobile control systems, etc.Item Synthetic data generation in hybrid modelling of railway HVAC system(2020) Gálvez, Antonio; Diez-Olivan, Alberto; Seneviratne, Dammika; Galar, Diego; Tecnalia Research & Innovation; INDUSTRY_THINGSThis paper proposes a hybrid model (HyM) for a heating, ventilation and air conditioning (HVAC) system installed in a passenger train. This HyM fuses data from two sources: data taken from the real system and synthetic data generated using a physics-based model of the HVAC. The physical model of the HVAC was developed to include the sensors located in the real system and new virtual sensors reproducing the behaviour of the system while a failure mode (FM) is simulated. Statistical features are calculated from the selected signals. These features are labelled according to the related FMs and are merged with the features calculated from the data from the real system. This data fusion allows us to classify the condition indicators of the system according to the FMs. The merged features are used to train a neural network (NN), which achieves a remarkable accuracy. Accuracy is a key concern of future research on the detection and diagnosis of a multiple faults and the estimation of the remaining useful life (RUL) through prognosis. The outcome is beneficial for the proper functioning of the system and the safety of the passengers.Item Towards an architecture for big data analytics leveraging edge/fog paradigms(Association for Computing Machinery, 2019-09-09) Díaz-De-Arcaya, Josu; Miñon, Raül; Torre-Bastida, Ana I.; Duchien, Laurence; Koziolek, Anne; Mirandola, Raffaela; Martinez, Elena Maria Navarro; Quinton, Clement; Scandariato, Ricardo; Scandurra, Patrizia; Trubiani, Catia; Weyns, Danny; HPAAn industry transformation is being boosted by Big Data and Cloud technologies. We present a Big Data architecture, which expands the life cycle of data processing through the Edge, Fog and Cloud computing layers. The proposed architecture takes advantage of the strengths of each: the Cloud layer executes heavy analytical processes, the Fog is responsible for the ingestion and performing aggregations, and the Edge manages devices and actuators. The proposed architecture tackles two main goals, 1) latencies and response times can be reduced by bringing the analytics closer to where the data is generated and 2) the use of computing resources is optimised. In order to conceptualise this architecture, an orchestration module is proposed with the goal of optimising the deployment of analytical workloads across the three layers, by evaluating their computing resources. In addition to this, another module is designed to monitor the performance of such workloads allowing the redistribution of tasks assigned to each node. These modules will be implemented in a real case scenario in the train domain.