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  • 1.
    Leturiondo, Urko
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan.
    Hybrid modelling in condition monitoring2016Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Assuring the reliability, availability, maintainability and safety of assets is key to business success. A logical first step is to consider the requirements of assets in the design process. However, these concepts must also be assured during the assets’ operation. Consequently, it is important to have knowledge of their actual condition.

    The condition monitoring of assets and their subsequent maintenance are changing with the rapid evolution of electronics and information and communication technologies. The contribution of such technologies to the monitoring of cyber-physical systems in the context of Industry 4.0 is important.

    In the era of big data, the ease of getting, storing and processing data is crucial. However, the trend towards big data is not as effective in the field of condition monitoring as in others. One of the challenges of today’s condition monitoring is the lack of data on those assets not allowed to operate beyond their pre-established maintenance limit. Datasets miss advanced degradation states of assets and fail to predict rarely occurring outliers, but both have a great impact on operation; in other words, data-driven methods are limited and cannot accurately tackle scenarios outside the training dataset.

    This thesis proposes augmenting such datasets with the addition of synthetic data generated by physics-based models describing the dynamic behaviour of assets. It argues a combination of physics-based and data-driven modelling, known as hybrid modelling, can overcome the aforementioned limitations. It proposes an architecture for hybrid modelling, based on data fusion and context awareness and oriented to diagnosis and prognosis.

    The thesis applies some of the key parts of this architecture to rotating machinery, developing a physics-based model for a rotating machine from an electromechanical point of view and following a multi-body approach. It verifies and validates the model following guidelines suggested in the literature and using experimental data acquired in predefined tests with a commercial test rig.

    The developed physics-based model is used to generate synthetic data in different degradation states, and these data are fused with condition monitoring data acquired from the test rig. A data-driven approach is used to train an algorithm with the resulting fused data, adapting the clusters obtained by an algorithm to the context in which the machine is operating. The hybrid model is applied specifically for fault detection, localisation and quantification. The use of context data is found to enhance the results and is the key to providing context-driven services in the future.

    In short, the model is ready to react to faults that have not occurred in reality, with a severity that has not been reached in a specific operating context but has been introduced in the physics-based modelling.

  • 2.
    Leturiondo, Urko
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Salgado, Oscar
    IK4-Ikerlan.
    Hybrid modelling in condition-based maintenance for smart assets2016Konferensbidrag (Refereegranskat)
    Abstract [en]

    When it comes to take proper maintenance decisions regarding reliability and safety of a system, there is a need to perform a right health assessment. Thus, acquiring signals from the system in healthy and damaged conditions gives the chance to analyse the effect of the state of the system on its response. However, it is usually hard to perform diagnosis and prognosis using only tests from the real system. The advances in technologies involving internet of things, cloud computing and big data lead to a situation in which this analysis of acquired data can be complemented by the use physics-based modelling. Thus, a combination of both data-driven and physics-based approaches can be implemented thanks to the aforementioned progress. In this paper an architecture to implement hybrid modelling is proposed, based on data fusion between real data and synthetic data obtained by simulations of a physics-based model. This architecture has two analysis levels: an online process carried out in a local basis and virtual commissioning performed in the cloud. The former results in failure detection analysis for avoiding upcoming failures whereas the latter has as aim a further analysis involving both diagnosis and prognosis.

  • 3.
    Leturiondo, Urko
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan.
    Mishra, Madhav
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Salgado, Oscar
    IK4-Ikerlan.
    Synthetic data generation in hybrid modelling of rolling element bearings2015Ingår i: Insight (Northampton), ISSN 1354-2575, E-ISSN 1754-4904, Vol. 57, nr 7, s. 395-400Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Diagnosis and prognosis processes are necessary to optimise the dependability of systems and ensure their safe operation. If there is a lack of information, faulty conditions cannot be identified and undesired events cannot be predicted. It is essential to predict such events and mitigate risks, but this is difficult in complex systems.Abnormal or unknown faults cause problems for maintenance decision makers. We therefore propose a methodology that fuses data-driven and model-based approaches. Real data acquired from a real system and synthetic data generated from a physical model can be used together to perform diagnosis and prognosis.As systems have time-varying conditions related to both the operating condi- tions and the healthy or faulty state of systems, the idea behind the proposed methodology is to generate synthetic data in the whole range of conditions in which a system can work. Thus, data related to the context in which the system is operating can be generated.We also take a first step towards implementing this methodology in the field of rolling element bearings. Synthetic data are generated using a physical model that reproduces the dynamics of these machine elements. Condition indicators such as root mean square, kurtosis and shape factor, among others, are calculated from the vibrational response of a bearing and merged with the real features obtained from the data collected from the functioning systemFinally, the merged indicators are used to train SVM classifiers (support vector machines), so that a classification according to the condition of the bearing is made independently of the applied loading conditions even though some of the scenarios have not yet occurred.

  • 4.
    Leturiondo, Urko
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan.
    Mishra, Madhav
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Salgado, Oscar
    IK4-Ikerlan.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Modelo dinámico de rodamientos para su estudio frente a fallos geométricos locales2014Konferensbidrag (Refereegranskat)
  • 5.
    Leturiondo, Urko
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan.
    Mishra, Madhav
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Salgado, Oscar
    IK4-Ikerlan.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Nonlinear response of rolling element bearings with local defects2014Konferensbidrag (Refereegranskat)
    Abstract [en]

    Rolling element bearings have been studied for decades, but more research is required into their dynamics, especially failure due to different kinds of damage in the context of condition monitoring. The appearance of a failure in an element of a bearing, as well as its degradation, can entail not only a malfunction in the system in which it is located, but also a catastrophic failure. This work presents a multi-body model of a rolling element bearing with the objective of analysing the dynamics of the bearing and emphasising the effect of defects in any of its element. The study models the metal-metal contacts between the bearing’s elements using the Hertz contact and the elastohydrodynamic lubricationtheories, both of which are theories of nonlinearity. It also considers the non-stationary regime of bearings and local geometric damage. Its results are compared with results in the literature. Finally, it includes a set of additional results showing different aspects of the response of the bearing.

  • 6.
    Leturiondo, Urko
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan Technology Research Centre, Control and Monitoring Area.
    Salgado, Oscar
    IK4-Ikerlan Technology Research Centre, Control and Monitoring Area.
    Ciani, Lorenzo
    University of Florence, Department of Information Engineering.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Catelani, Marcantonio
    University of Florence, Department of Information Engineering.
    Architecture for hybrid modelling and its application to diagnosis and prognosis with missing data2017Ingår i: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 108, s. 152-162Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The advances in technology involving internet of things, cloud computing and big data mean a new perspective in the calculation of reliability, maintainability, availability and safety by combining physics-based modelling with data-driven modelling. This paper proposes an architecture to implement hybrid modelling based on the fusion of real data and synthetic data obtained in simulations using a physics-based model. This architecture has two levels of analysis: an online process carried out locally and virtual commissioning performed in the cloud. The former results in failure detection analysis to avoid upcoming failures whereas the latter leads to both diagnosis and prognosis. The proposed hybrid modelling architecture is validated in the field of rotating machinery using time-domain and frequency-domain analysis. A multi-body model and a semi-supervised learning algorithm are used to perform the hybrid modelling. The state of a rolling element bearing is analysed and accurate results for fault detection, localisation and quantification are obtained. The contextual information increases the accuracy of the results; the results obtained by the model can help improve maintenance decision making and production scheduling. Future work includes a prescriptive analysis approach.

  • 7.
    Leturiondo, Urko
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan.
    Salgado, Oscar
    IK4-Ikerlan.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Estimation of the reliability of rolling element bearings using a synthetic failure rate2015Konferensbidrag (Refereegranskat)
    Abstract [en]

    As rolling element bearings are key parts of rotating machinery, the estimation of their reliability is very important. In this context, different standards and research articles propose how to estimate fatigue life for different levels of reliability. However, when trying to do calculations based on data from a real system, there are many difficulties because of economic and safety reasons. Consequently, the use of physical models to simulate the cases that are difficult to reproduce in a real system allows us to generate synthetic data related to them. Thus, in this paper a synthetic failure rate of rolling element bearings is calculated using a physical modelling approach. A multi-body model of a bearing is used in order to obtain its dynamic response in non-stationary conditions and in different degradation levels. Thus, synthetic data are generated to cover a range of degradation related to geometric changes in the surface of the parts of the bearing. Some of the output variables of these synthetic data, such as vibration, are used as covariates of a proportional hazard model, which is then trained to estimate the reliability of the bearing. In this way, a synthetic failure rate is obtained in such a way that it can improve the failure rate given by the manufacturers.

  • 8.
    Leturiondo, Urko
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan.
    Salgado, Oscar
    IK4-Ikerlan.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Estimation of the Reliability of Rolling Element Bearings Using a Synthetic Failure Rate2016Ingår i: Current Trends in Reliability, Availability, Maintainability and Safety: An Industry Perspective, Springer International Publishing , 2016, 1, s. 99-112Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    As rolling element bearings are key parts of rotating machinery, the estimation of their reliability is very important. In this context, different standards and research articles propose how to estimate fatigue life for different levels of reliability. However, when trying to do calculations based on data from a real system, there are many difficulties because of economic and safety reasons. Consequently, the use of physical models to simulate the cases that are difficult to reproduce in a real system allows us to generate synthetic data related to them. Thus, in this paper a synthetic failure rate of rolling element bearings is calculated using a physical modelling approach. A multi-body model of a bearing is used in order to obtain its dynamic response in non-stationary conditions and in different degradation levels. Thus, synthetic data are generated to cover a range of degradation related to geometric changes in the surface of the parts of the bearing. Some of the output variables of these synthetic data, such as vibration, are used as covariates of a proportional hazard model, which is then trained to estimate the reliability of the bearing. In this way, a synthetic failure rate is obtained in such a way that it can improve the failure rate given by the manufacturers.

  • 9.
    Leturiondo, Urko
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan Technology Research centre, Control and Monitoring Area, Arrasate-Mondragon, Spain.
    Salgado, Oscar
    IK4-Ikerlan Technology Research centre, Control and Monitoring Area, Arrasate-Mondragon, Spain.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Methodology for the physics-based modelling of multiple rolling element bearing configurations2017Ingår i: Proceedings of the Institution of mechanical engineers. Proceedings part K, journal of multi-body dynamics, ISSN 1464-4193, E-ISSN 2041-3068, Vol. 231, nr 1, s. 194-212Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Condition-based maintenance is a maintenance strategy which can be employed for monitoring the condition of rolling element bearings (REBs). For that purpose, the physics-based modelling of these machine elements is an interesting approach. There is a wide range of REBs regarding their internal configuration, dimensions and operating conditions. In this paper, a methodology to create a physics-based mathematical model to reproduce the dynamics of multiple kinds of REB is presented. Following a multi-body modelling, the proposed methodology takes advantage of the reusability of models to cover a wide range of bearing configurations, as well as to generalise the dimensioning of the bearing and the application of the operating conditions. The methodology is proved to be valid by its application to two case studies. Simulations of a deep-groove ball bearing and a cylindrical roller bearing are carried out, analysing their dynamic response as well as analysing the effects of damage in their parts. Results of the two case studies show good agreement with experimental data and results of other models in literature.

  • 10.
    Leturiondo, Urko
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan.
    Salgado, Oscar
    IK4-Ikerlan.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Multi-body modelling of rolling element bearings and performance evaluation with localised damage2016Ingår i: Eksploatacja i Niezawodnosc - Maintenance and Reliability, ISSN 1507-2711, Vol. 18, nr 4, s. 638-648Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Condition-based maintenance is an extended maintenance approach for many systems, including rolling element bearings. Forthat purpose, the physics-based modelling of these machine elements is an interesting method. The use of rolling element bearingsis extended to many fields, what implies a variety of the configurations that they can take regarding the kind of rolling elements,the internal configuration and the number of rows. Moreover, the differences of the applications make rolling element bearingsto take different sizes and to be operating at different conditions regarding both speed and loads. In this work, a methodology tocreate a physics-based mathematical model to reproduce the dynamics of multiple kinds of rolling element bearings is presented.Following a multi-body modelling, the proposed strategy takes advantage of the reusability of models to cover a wide range ofbearing configurations, as well as to generalise the dimensioning of the bearing and the application of the operating conditions.Simulations of two bearing configurations are presented in this paper, analysing their dynamic response as well as analysing theeffects of damage in their parts. Results of the two case studies show good agreement with experimental data and results of othermodels in literature.

  • 11.
    Leturiondo, Urko
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan.
    Salgado, Oscar
    IK4-Ikerlan.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Test rig model development and validation for the diagnosis of rolling element bearings2016Ingår i: 14th IMEKO TC10 Workshop on Technical Diagnostics 2016: New Perspectives in Measurements, Tools and Techniques for Systems Reliability, Maintainability and Safety, Milan, Italy, 27-28 June 2016, 2016, s. 46-49Konferensbidrag (Refereegranskat)
    Abstract [en]

    In the context of condition based maintenance, carrying out diagnosis and prognosis processes is a key. For that purpose the evaluation of the condition of a machine is necessary, for which the development of physical models is useful as the response of the modelled system can be obtained in different operating conditions. In this paper, an electromechanical model for a rotary machine is presented, making special emphasis on the modelling of rolling element bearings. Thus, the response to different damaged conditions is evaluated. The proposed model is validated by comparing the simulation results with experimental signals acquired by tests carried out at different operating conditions. This comparison shows a good agreement as differences less than 0.6 % for the model of the bearing and differences up to the 10 % for the modelling of the rest of the elements are obtained.

  • 12.
    Leturiondo, Urko
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan.
    Salgado, Oscar
    IK4-Ikerlan.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Validation of a physics-based model for rolling element bearings with diagnosis purposes2016Ingår i: Proceedings from the 8th European Workshop on Structural Health Monitoring (EWSHM 2016), 2016, Vol. 21, s. 461-470Konferensbidrag (Refereegranskat)
    Abstract [en]

    The use of rolling element bearings is widely extended to many fields such as wind energy systems, transportation and machine tools, among others. This broad use makes their performance analysis an interesting field of research. There are techniques to determine the life of a bearing and the on-going failure, if any, under some assumptions with some values of reliability. However, the unfulfilment of those hypothesis or other effects that affect the standard operation of rolling element bearings (e.g. current leakage, overloading, corrosion, etc.) leads to a higher probability of the appearance of failure. The monitoring of the condition of rolling element bearings has two main goals, the diagnosis and the prognosis of the item. Indeed, diagnosis, i.e. damage detection, localization and identification, has a great interest on the knowledge of the state of rolling element bearings in order to prevent faulty situations that may cause risky or costly situations, identifying those adverse situations and trying to mitigate the undesired effects.Therefore, risky situations due to failures need additional knowledge about the dynamics of a system (rolling element bearings in this case) and physics-based models can be used in order to represent it. They have an interesting potential due to the fact that they are able to simulate situations that may arise in some damaged conditions that might be either difficult, costly or insecure to reproduce in a real system. However, there is a need to validate the physics-based models to assure that it follows the real response of the system.This work presents the validation process of a model already developed by the authors. Experimental tests have been done in a test rig and the vibration measurements taken from these tests have been used to validate the model. Damage on the surface of the outer race has been induced to one of the rolling element bearings of the test rig. Thus, frequency-domain and order-domain analysis have been performed and the experimental results have been compared to the results obtained from the simulations. Differences lower than 2.5 % have been found for a wide range of constant and variable speeds and, hence, the model is validated.

  • 13.
    Leturiondo, Urko
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan Technology Research Centre.
    Salgado, Oscar
    IK4-Ikerlan Technology Research Centre.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Validation of a physics-based model of a rotating machine for synthetic data generation in hybrid diagnosis2017Ingår i: Structural Health Monitoring, ISSN 1475-9217, E-ISSN 1741-3168, Vol. 16, nr 4, s. 458-470Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Diagnosis and prognosis are key issues in the application of condition based maintenance. Thus, there is a need to evaluate the condition of a machine. Physics-based models are of great interest as they give the response of a modelled system in different operating conditions. This strategy allows for the generation of synthetic data that can be used in combination with real data acquired by sensors to improve maintenance. The article presents an electromechanical model for a rotating machine, with special emphasis on the modelling of rolling element bearings. The proposed model is validated by comparing the simulation results and the experimental results in different operating conditions and different damaged states. This comparison shows good agreement, obtaining differences of up to 10% for the modelling of the whole rotating machine and less than 0.6% for the model of the bearing.

  • 14.
    Leturiondo, Urko
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan.
    Salgado, Oscar
    IK4-Ikerlan.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Mishra, Madhav
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Methodology for the Estimation of the Fatigue Life of Rolling Element Bearings in Non-stationary Conditions2016Ingår i: Advances in Condition Monitoring of Machinery in Non-Stationary Operations: Proceedings of the Fourth International Conference on Condition Monitoring of Machinery in Non-Stationary Operations, CMMNO'2014, Lyon, France December 15-17 / [ed] Fakher Chaari; Radozlaw Zimroz; Walter Bertelmus; Mahamed Haddar, Cham: Encyclopedia of Global Archaeology/Springer Verlag, 2016, Vol. 4, s. 413-423Konferensbidrag (Refereegranskat)
    Abstract [en]

    The estimation of the life of rolling element bearings (REBs) is crucial to determine when maintenance is required. This paper presents a methodology to calculate the fatigue life of REBs considering non-stationary conditions. Instead of taking a constant value, the paper considers cyclic loading and unloading processes, as well as increasing and decreasing values of the speed of rotation. It employs a model-based approach to calculate contact loads between the different elements of the bearing, with a finite element model (FEM) used to calculate the contact stresses. Using this information, it then performs a fatigue analysis to study overloading in faulty bearings.

  • 15.
    Mishra, Madhav
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Leturiondo, Urko
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Synthetic data for hybrid prognosis2014Ingår i: Proceedings of the European Conference of the Prognostics and Health Management Society 2014, 2014, s. 796-801Konferensbidrag (Refereegranskat)
    Abstract [en]

    Using condition-based maintenance (CBM) to assess machinery health is a popular technique in many industries, especially those using rotating machines. CBM is relevant in environments where the prediction of a failure and the prevention and mitigation of its consequences increase both profit and safety. Prognosis is the most critical part of this process and the estimation of Remaining Useful Life (RUL) is essential once failure is identified. This paper presents a method of synthetic data generation for hybrid model-based prognosis. In this approach, physical and data-driven models are combined to relate process features to damage accumulation in time-varying service equipment. It uses parametric models and observer-based approaches to Fault Detection and Identification (FDI). A nominal set of parameters is chosen for the simulated system, and a sensitivity analysis is performed using a general-purpose simulation package. Synthetic data sets are then generated to compensate for information missing in the acquired data sets. Information fusion techniques areproposed to merge real and synthetic data to create training data sets which reproduce all identified failure modes, even those that do not occur in the asset, such as Reliability Centered Maintenance (RCM), Failure Mode and Effect Analysis(FMEA). This new technology can lead to better prediction of remaining useful life of rotating machinery and minimizing and mitigating the costly effects of unplanned maintenance actions.

  • 16.
    Mishra, Madhav
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Leturiondo, Urko
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Salgado, Oscar
    IK4-Ikerlan.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Hybrid modelling for failure diagnosis and prognosis in the transport sector: Acquired data and synthetic data2015Ingår i: Dyna, ISSN 0012-7361, Vol. 90, nr 2, s. 139-145Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Safety in transport is a key. Railway and aerospace sectors have a need for ways to predict the behaviour of trains and aircraft, respectively. With this information, maintenance tasks for the correct operation of the assets can be carried out, reducing the number of failures that can cause an accident. However, the lack of enough data of the faulty state of those systems makes this to be difficult. Because of that either hidden faults or unknown faults can occur. As regulations in transport are very restrictive, components are usually substituted in early states of their degradation, which implies a loss of useful life of those components.In this article a methodology to overcome this limitation is presented. This methodology consists in the fusion of data obtained from two sources: data acquired from the real system, and synthetic data generated using physical models of the system. These physical models should be constructed in such a way that they can reproduce the main failure modes that can occur in the modelled system. This data fusion, that creates a hybrid model, not only allows to classify the condition of the system according to the aforementioned failure modes, but also to define new data that do not belong to any of those failure modes as a new failure mode, improving diagnosis and prognosis processes.

  • 17.
    Mishra, Madhav
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Saari, Juhamatti
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Leturiondo, Urko
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Hybrid Models for Rotating Machinery Diagnosis and Prognosis: Estimation of Remaining Useful Life2014Rapport (Övrigt vetenskapligt)
    Abstract [en]

    The purpose of this literature review is to summarise the various technologies that can be used for machinery diagnosis and prognosis. The review focuses on Condition Based Maintenance (CBM) in machinery systems, with a short description of the theory behind each technology; it also includes references to state-of-the-art research into each theory. When we compare technologies, especially with respect to cost, complexity, and robustness, we find varied abilities across technologies. The machinery health assessment for CBM deployment is accepted worldwide; it is very popular in industries using rotating machines involved. These techniques are relevant in environments where predicting a failure and preventing or mitigating its consequences will increase both profit and safety. Prognosis is the most critical part of this process and is now recognised as a key feature in maintenance strategies; the estimation of Remaining Useful Life (RUL) is essential when a failure is identified. The literature review identifies three basic ways to model the fault development process: with symbols, data, or mathematical formulations based on physical principles. The review discusses hybrid approaches to machinery diagnosis and prognosis; it notes some typical approaches and discusses their advantages and disadvantages.

  • 18.
    Villarejo, Roberto
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Johansson, Carl-anders
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Urko, Leturiondo
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4 Ikerlan, P J Ma Arizmendiarrieta 2, Arrasate Mondragon 20500, Spain.
    Simon, Victor
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Seneviratne, Dammika
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Bottom to Top Approach for Railway KPI Generation2017Ingår i: Management Systems in Production Engineering, ISSN 2299-0461, Vol. 25, nr 3, s. 191-198, artikel-id 28Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Railway maintenance especially on infrastructure produces a vast amount of data. However, having data is not synonymous with having information; rather, data must be processed to extract information. In railway maintenance, the development of key performance indicators (KPIs) linked to punctuality or capacity can help planned and scheduled maintenance, thus aligning the maintenance department with corporate objectives. There is a need for an improved method to analyse railway data to find the relevant KPIs. The system should support maintainers, answering such questions as what maintenance should be done, where and when. The system should equip the user with the knowledge of the infrastructure's condition and configuration, and the traffic situation so maintenance resources can be targeted to only those areas needing work. The amount of information is vast, so it must be hierarchized and aggregated; users must filter out the useless indicators. Data are fused by compiling several individual indicators into a single index; the resulting composite indicators measure multidimensional concepts which cannot be captured by a single index. The paper describes a method of monitoring a complex entity. In this scenario, a plurality of use indices and weighting values are used to create a composite and aggregated use index from a combination of lower level use indices and weighting values. The resulting composite and aggregated indicators can be a decision-making tool for asset managers at different hierarchical levels.

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