Modelización Híbrida Para El Diagnóstico Y Pronóstico De Fallos en El Sector Del Transporte.
Datos Adquiridos Y Datos Sintéticos.

Mishra, Madhav; Leturiondo, Urko; Salgado, Óscar; Pascual, Diego Galar

doi:10.6036/7252

Cited by 9 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Convective heat transfer blocks model heat transfer in a thermal network by convection due to fluid motion (in this case, the air). The rate of heat transfer (19) is proportional to the temperature difference, heat transfer coefficient and surface area in contact with the fluid.…”

Section: Failure Hybrid Model Of the Use Casementioning

confidence: 99%

“…DTs, on the contrary, provide two new information sources: firstly, physics-based models can allow us to understand their real behaviour, and secondly, physical simulation enables the generation of synthetic data for potential new scenarios, such as potential anomalies or failure conditions. Moreover, hybrid models, considered to be a combination of physics-based models and data analytics, provide a powerful tool for diagnosis and prognosis [19]. Hybrid models developed with this purpose are a good basis for DT creation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid-Model-Based Digital Twin of the Drivetrain of a Wind Turbine and Its Application for Failure Synthetic Data Generation

et al. 2023

View full text Add to dashboard Cite

Computer modelling and digitalization are integral to the wind energy sector since they provide tools with which to improve the design and performance of wind turbines, and thus reduce both capital and operational costs. The massive sensor rollout and increase in big data processing capacity over the last decade has made data collection and analysis more efficient, allowing for the development and use of digital twins. This paper presents a methodology for developing a hybrid-model-based digital twin (DT) of a power conversion system of wind turbines. This DT allows knowledge to be acquired from real operation data while preserving physical design relationships, can generate synthetic data from events that never happened, and helps in the detection and classification of different failure conditions. Starting from an initial physics-based model of a wind turbine drivetrain, which is trained with real data, the proposed methodology has two major innovative outcomes. The first innovation aspect is the application of generative stochastic models coupled with a hybrid-model-based digital twin (DT) for the creation of synthetic failure data based on real anomalies observed in SCADA data. The second innovation aspect is the classification of failures based on machine learning techniques, that allows anomaly conditions to be identified in the operation of the wind turbine. Firstly, technique and methodology were contrasted and validated with operation data of a real wind farm owned by Engie, including labelled failure conditions. Although the selected use case technology is based on a double-fed induction generator (DFIG) and its corresponding partial-scale power converter, the methodology could be applied to other wind conversion technologies.

show abstract

Section: Failure Hybrid Model Of the Use Casementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid-Model-Based Digital Twin of the Drivetrain of a Wind Turbine and Its Application for Failure Synthetic Data Generation

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Diagnostics shows the current state of a system. It starts once a fault or abnormal behavior is detected and involves failure mode and effect analysis (FMEA) [9,10]. More specifically, a diagnostic process localizes a faulty component by detecting and isolating a fault based on a pre-selected failure mode (FM), and then, tracing the relationship between the data acquired from the system and degradation.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach

et al. 2021

View full text Add to dashboard Cite

Heating, 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.

show abstract

“…Physics-based models use physical laws and mathematical formulations to obtain the response of a system under certain operating conditions, making them an appropriate choice for such endeavours [14,37]. These models can be used together with other methods, such as the data-driven approach or symbolic modelling, to create a hybrid model that aims to overcome the limitations of each method [9,25]. In short, the development of physics-based models can facilitate the maintenance of REBs.…”

Section: Introductionmentioning

confidence: 99%

Multi-body modelling of rolling element bearings and performance evaluation with localised damage

Leturiondo¹,

Salgado²,

Galar³

2016

EiN

View full text Add to dashboard Cite

Modelización Híbrida Para El Diagnóstico Y Pronóstico De Fallos en El Sector Del Transporte. Datos Adquiridos Y Datos Sintéticos.

Cited by 9 publications

References 0 publications

Hybrid-Model-Based Digital Twin of the Drivetrain of a Wind Turbine and Its Application for Failure Synthetic Data Generation

Hybrid-Model-Based Digital Twin of the Drivetrain of a Wind Turbine and Its Application for Failure Synthetic Data Generation

Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach

Multi-body modelling of rolling element bearings and performance evaluation with localised damage

Contact Info

Product

Resources

About