Urko Leturiondo

Methodology for the physics-based modelling of multiple rolling element bearing configurations:

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.

Validation of a physics-based model of a rotating machine for synthetic data generation in hybrid diagnosis

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.

Bottom to top approach for railway KPI generation

Abstract 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.

Estimation of the Reliability of Rolling Element Bearings Using a Synthetic Failure Rate

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

Methodology for the Estimation of the Fatigue Life of Rolling Element Bearings in Non-stationary Conditions

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.