66 research outputs found
A dynamic prescriptive maintenance model considering system aging and degradation
This paper develops a dynamic maintenance strategy for a system subject to aging and degradation. The influence of degradation level and aging on system failure rate is modeled in an additive way. Based on the observed degradation level at the inspection, repair or replacement is carried out upon the system. Previous researches assume that repair will always lead to an improvement in the health condition of the system. However, in our study, repair reduces the system age but on the other hand, increases the degradation level. Considering the two-fold influence of maintenance actions, we perform reliability analysis on system reliability as a first step. The evolution of system reliability serves as a foundation for establishing the maintenance model. The optimal maintenance strategy is achieved by minimizing the long-run cost rate in terms of the repair cycle. At each inspection, the parameters of the degradation processes are updated with maximum a posteriori estimation when a new observation arrives. The effectiveness of the proposed model is illustrated through a case study of locomotive wheel-sets. The maintenance model considers the influence of degradation and aging on system failure and dynamically determines the optimal inspection time, which is more flexible than traditional stationary maintenance strategies and can provide better performance in the field
30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)
Proceedings of COMADEM 201
A Stochastic Approach to Measurement-Driven Damage Detection And Prognosis in Structural Health Monitoring
Damage detection and prognosis are integral to asset management of critical mechanical and civil engineering infrastructure. In practice, these two aspects are often decoupled, where the former is carried out independently using sensor data (e.g., vibrations), while the latter is undertaken based on reliability principles using life time failure data of the system or the component of interest. Only in a few studies damage detection results are extended to remaining useful life estimation, which is achieved by modeling the underlying degradation process using a surrogate measure of degradation. However, an integrated framework which undertakes damage detection, prognosis, and maintenance planning in a systematic way is lacking in the literature. Furthermore, the parameters of degradation model which are utilized for prognosis are often solely estimated using the degradation data obtained from the monitored unit, which represents the degradation of a specific unit, but ignores the general population trend.
The main objectives of this thesis are three-fold: first, a mathematical framework using surrogate measure of degradation is developed to undertake the damage detection and prognosis in a single framework; next, the prior knowledge obtained from
the historical failed units are integrated in model parameter estimation and residual useful life (RUL) updating of a monitored unit using a Bayesian approach; finally, the proposed degradation modeling framework is applied for maintenance planning of civil and industrial systems, specifically, for reinforced concrete beams and rolling element bearings. The initiation of a fault in these applications is often followed by a sudden change in the degradation path.
The location of a change-point can be associated with a sudden loss of stiffness in the case of structural members, or fault initiation in the case of bearings. Hence, in this thesis, the task of change point location identification is thought of as being synonymous with damage or fault detection in the context of structural health monitoring. Furthermore, the change point results are used for two-phase degradation modeling, future degradation level prediction and subsequent RUL estimation.
The model parameters are updated using a Bayesian approach, which systematically integrates the prior knowledge obtained from historical failure-time data with monitored data obtained from an in-situ unit. Once such a model is established, it is projected to a failure threshold, thereby allowing for RUL estimation and maintenance planning.
Results from the numerical as well as actual field data shows that the proposed degradation modeling framework is good in performing these two tasks. It was also found that as more degradation data is utilized from the monitoring unit, the
progressing fault is detected in a timely manner and the model parameters estimates and the end life predictions become more accurate
Advanced assessment methods for elderly bridges. State-of-the-art and justification based on LCA
[ANGLÈS] Many of the existing bridges do not satisfy the structural requirements specified in design codes for
new bridges. However, many of these bridges must remain in service and therefore decisions must be
made in order to maintain their safety.
In the design of new bridges, it is accepted “to be on the safe side” inherent in the standards; but
for an assessment of an elderly bridge this procedure should be removed in order to have a more
realistic understanding of the state of the structure. Otherwise, the decisions made, being too
conservative, can result in unnecessary costs.
The main problem is that many existing bridges near to the end of their live under conventional
evaluation methods give results which imply or the replacement of the bridge or a high investment
for repair it to bring it back to the performance level stipulated in the current standards. The existing
advanced methods of structural assessment allow evaluating the actual state of the structure. It has
been shown in many cases that better results can be obtained with advanced methods than with
conventional methods because the advanced ones evaluate the structure decreasing as much as
possible the existing uncertainties. This implies to move from a structure that initially seemed to
require heavy investments or to be replaced, to a structure which would have acceptable conditions
of behaviour at least for a certain period of time, with a much lower investment and an optimized
repair and maintenance.
Current methods of advanced evaluation are based on probabilistic methods of reliability through
the updating of the variables that which contains uncertainty (traffic solicitations, etc.). This updating
is carried out by site‐specific data taken with Structural Health Monitoring systems. Load tests also
can be included within the advanced methodologies of evaluation.
These evaluation methods have a great impact on the life‐cycle assessment of a bridge because
apart from reducing the maintenance and repair costs allow, with a more accurate assessment,
extend the lifespan of a structure while maintaining adequate levels of performance and safety.
The present thesis aims to synthesize the state‐of‐the‐art of the mentioned advanced assessment
methods used in bridges. It also highlights the involvement, influence and direct relationship of these
methods with the different aspects which are currently considered in Life‐Cycle Assessment of
existing bridges.[CASTELLÀ] Muchos de los puentes existentes no satisfacen los requerimientos estructurales especificados en
los códigos de diseño para nuevos puentes. Sin embargo, muchos de estos puentes deben
mantenerse en servicio y, por tanto, deben tomarse decisiones respecto a mantener su nivel de
seguridad.
Para el diseño de puentes nuevos, se acepta el “estar del lado seguro” inherente en las normativas;
pero para una evaluación de un puente de avanzada edad dicho proceder debe eliminarse para poder
tener un conocimiento más real del estado de la estructura. De lo contrario, las decisiones que se
tomen, por demasiado conservadoras, pueden dar lugar a gastos innecesarios.
El problema principal reside en que muchos puentes existentes cercanos al fin de su vida útil bajo
métodos de evaluación convencional arrojan resultados que implicarían o la sustitución del puente o
una inversión de reparación muy elevada para llevarlo de nuevo al nivel de comportamiento
estipulado en las normativas actuales. Los métodos avanzados de evaluación estructural existentes,
permiten evaluar el estado real de la estructura. Se ha demostrado en muchos casos que con
métodos avanzados se obtienen mejores resultados que con los métodos convencionales ya que se
evalúa la estructura disminuyendo al máximo posible las incertidumbres existentes. Ello conlleva
pasar de una estructura que en un principio parecía requerir una inversión muy elevada o ser
sustituida, a una estructura que volvería a estar en condiciones aceptables de comportamiento al
menos durante un determinado periodo de tiempo, con una inversión mucho menor y optimizada de
reparación y mantenimiento.
Los métodos actuales de evaluación avanzada se basan en métodos probabilísticos de fiabilidad a
través de la actualización de las variables que encierran incertidumbre (solicitación del tráfico, etc.).
Dicha actualización se lleva a cabo con datos tomados in situ mediante sistemas “Structural Health
Monitoring”. Las pruebas de carga también pueden englobarse dentro de las metodologías avanzadas
de evaluación.
Estos métodos de evaluación tienen un gran impacto en la evaluación del ciclo de vida de un
puente puesto que aparte de reducir los costes de mantenimiento y reparación permiten, mediante
una evaluación más precisa, alargar la vida útil de una estructura manteniendo unos niveles de
comportamiento y seguridad adecuados.
La presente tesis presente sintetizar el estado del arte de los métodos avanzados de evaluación
estructural mencionados utilizados en puentes. También incide directamente en la implicación,
influencia y relación directa de dichos métodos con los distintos aspectos que se consideran
actualmente en la evaluación del Ciclo de Vida de los puentes existentes
A literature review of Artificial Intelligence applications in railway systems
Nowadays it is widely accepted that Artificial Intelligence (AI) is significantly influencing a large number of domains, including railways. In this paper, we present a systematic literature review of the current state-of-the-art of AI in railway transport. In particular, we analysed and discussed papers from a holistic railway perspective, covering sub-domains such as maintenance and inspection, planning and management, safety and security, autonomous driving and control, revenue management, transport policy, and passenger mobility. This review makes an initial step towards shaping the role of AI in future railways and provides a summary of the current focuses of AI research connected to rail transport. We reviewed about 139 scientific papers covering the period from 2010 to December 2020. We found that the major research efforts have been put in AI for rail maintenance and inspection, while very limited or no research has been found on AI for rail transport policy and revenue management. The remaining sub-domains received mild to moderate attention. AI applications are promising and tend to act as a game-changer in tackling multiple railway challenges. However, at the moment, AI research in railways is still mostly at its early stages. Future research can be expected towards developing advanced combined AI applications (e.g. with optimization), using AI in decision making, dealing with uncertainty and tackling newly rising cybersecurity challenges
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