Browsing by Keyword "Fault detection"
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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 Evaluation of a Local Fault Detection Algorithm for HVDC Systems(European Association for the Development of Renewable Energies, Environment and Power Quality, 2019-07) Perez Molina, M.J.; Eguia Lopez, P.; Larruskain Eskobal, M.; Santos Mugica, M.; Rodriguez Sanchez, R.A great increase in the amount of energy generated from clean and renewable sources integrated in the electric power system is expected worldwide in the coming years. High Voltage Direct Current (HVDC) systems are seen as a promising alternative to the traditional Alternating Current (AC) systems for the expansion of the electric power system. However, to achieve this vision, there are some remaining challenges regarding HVDC systems which need to be solved. One of the main challenges is related to fault detection and location in HVDC grids. This paper reviews the main protection algorithms available and presents the evaluation of a local fault detection algorithm for DC faults in a multi-terminal Voltage Source Conversion (VSC) based HVDC grid. The paper analyses the influence of the DC voltage sampling frequency and the cable length in the performance of the algorithm. © 2019, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ).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 Fault detection based on ROCOV in a multi-terminal HVDC grid(European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ), 2020) Perez-Molina, M.J.; Eguia-Lopez, P.; Larruskain-Eskobal, M.; Etxegarai-Madina, A.; Apiñaniz Apiñaniz, S.Protection of a meshed VSC-HVDC grid is a challenge due to the behaviour of DC current and voltage signals during fault conditions. Protection systems must operate in a very short time range. Since fault detection should be very fast, local measurement based algorithms are mostly used; communication based algorithms lack the needed speed as a result of the communication time delay. This way, a ROCOV algorithm is proposed in this paper. This algorithm is analysed for different fault conditions.Item Reliable Modular Multilevel Converter Fault Detection with Redundant Voltage Sensor(2017-01) Picas, Ricard; Zaragoza, Jordi; Pou, Josep; Ceballos, Salvador; POWER ELECTRONICS AND SYSTEM EQUIPMENTThis paper presents a fault-tolerant configuration for the modular multilevel converter (MMC). The procedure is able to detect faults in voltage sensors and semiconductor switching devices, and it can reconfigure the system so that it can keep on operating. Both switch and sensor faults can be detected by comparing the output voltage of a set of submodules (SMs), which is measured by a so-called supervisory sensor, with two calculated reference voltages. Faults in the supervisory sensors are also considered. Sensor faults are overcome by using a measuring technique based on estimates that are periodically updated with the voltage measurements of the supervisory sensors. Additional SMs are included in the arms so that the MMC can bypass a faulty SM and continue operating without affecting the output voltage of the phase-leg. Experimental results obtained from a low-power MMC prototype are presented in order to demonstrate the effectiveness of the proposed techniques.Item Risk Assessment of a Wind Turbine: A New FMECA-Based Tool With RPN Threshold Estimation: A New FMECA-Based tool with RPN threshold estimation(2020) Catelani, Marcantonio; Ciani, Lorenzo; Galar, Diego; Patrizi, Gabriele; Tecnalia Research & InnovationA wind turbine is a complex system used to convert the kinetic energy of the wind into electrical energy. During the turbine design phase, a risk assessment is mandatory to reduce the machine downtime and the Operation & Maintenance cost and to ensure service continuity. This paper proposes a procedure based on Failure Modes, Effects, and Criticality Analysis to take into account every possible criticality that could lead to a turbine shutdown. Currently, a standard procedure to be applied for evaluation of the risk priority number threshold is still not available. Trying to fill this need, this paper proposes a new approach for the Risk Priority Number (RPN) prioritization based on a statistical analysis and compares the proposed method with the only three quantitative prioritization techniques found in literature. The proposed procedure was applied to the electrical and electronic components included in a Spanish 2 MW on-shore wind turbine.