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Prognostics and Health Management of Engineering Systems for Operation and Maintenance Optimisation
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0001-8278-8601
2018 (English)Doctoral thesis, comprehensive summary (Other academic)Alternative title
Prognostik ochtillståndskontroll av tekniska system för optimering av drift och underhåll (Swedish)
Abstract [en]

Prognostics and health management (PHM) is an engineering discipline that aims to maintain system behaviour and function and ensure mission success, safety and effectiveness. Prognostics is defined as the estimation of remaining useful life. It is the most critical part of this process and is a key feature of maintenance strategies since the estimation of the remaining useful life (RUL) is essential to avoiding unscheduled maintenance. Prognostics is relatively immature compared to diagnostics, and a challenging task facing the research community is to overcome some of the major barriers to the application of PHM technologies to real-world industrial systems. This thesis presents research into methods for addressing these challenges for industrial applications. The thesis work focuses on prognostic approaches for three different engineering systems with different characteristics in terms of the prognostics of operation and maintenance aspects. The aim of this thesis is to facilitate better operation and maintenance decision making. The main benefits of prognostics are in anticipating future failures to increase uptime, implementing dynamic maintenance planning toward decreasing total costs and decreasing energy consumption. Therefore, there is a need for methods that can be used in these cases to classify the health states and predict the remaining useful life of assets. The studied engineered systems in this thesis are railway tracks, batteries and rolling element bearings.

In a railway system, the track geometry has to be maintained to provide a safe and functional track. Therefore, track degradation of ballasted railway track systems has to be measured on a regular basis to determine when to maintain the track by tamping. Tamping aims to restore the geometry to its original state to ensure an efficient, comfortable and safe transportation system. To minimise the disruption introduced by tamping, this action has to be planned in advance. Track degradation forecasts derived from regression methods are used to predict when the standard deviation of a specific track section will exceed a predefined maintenance or safety limit. In this thesis, a particle-filter-based prognostic approach for railway track degradation for railway switches is proposed. The particle-filter-based prognostic will generate a probabilistic prediction result that can facilitate risk-based decision making.

Li-ion batteries are another important components in engineering system and battery life prediction matters. Li-ion batteries are commonly used in a wide range of consumer electronic devices, electric vehicles of all types, military electronics,  maritime applications, astronaut suits, and space systems. Many critical operations depend on such batteries as a reliable power source. It is therefore important for the user to get an accurate estimate of the battery end of discharge because an unforeseen discharge of a battery could have catastrophic consequences. To address this issue, a Bayesian hierarchical model (BHM)-based prognostics approach was applied to Li-ion batteries, where the goal was to analyse and predict the discharge behaviour of such batteries with variable load profiles and variable amounts of available discharge data. The BHM approach enables inferences for both individual batteries and groups of batteries. Estimates of the hierarchical model parameters and the individual battery parameters are presented, and dependencies on load cycles are inferred. The operational and reliability aspects, end of life (EoD) and end of life (EoL), are studied; it is shown that predictions of the EoD can be made accurately with a variable amount of battery data. Without access to measurements, e.g., predicting performance of a new battery, the predictions are based only on the prior distributions describing the similarity within a group of batteries and their dependency on the load cycle. A discharge cycle dependency is identified helping with estimation of battery reliability.

Batteries have become a very important engineering system, rotating machines have played an important role, possibly the most important role, in the field of engineering. They have been used to drive the industrialisation of the world.

For rotating machinery, rolling element bearings are a vital component and have several failure modes. Hence, there is  significant need to monitor the health of bearings and detect degraded  states and  upcoming  failures  as  early  as  possible  to avoid serious accidents and equipment failure. For  rolling element bearings, an investigation in using FEM models for estimating bearing forces from acceleration measurements was conducted. This study was performed at a paper mill where a bearing monitoring system was installed. The purpose of the study was to feed the bearing rating life L10 (a bearing life length calculation) with estimations of the dynamic bearing forces  to continuously update the L10 calculation by generating a dynamic L10. In a second study for bearing lifetime prediction, a Bayesian hierarchical modelling (BHM) approach , which includes different data sources, such as enveloped acceleration data, in combination with degradation models and prior distributions of other parameters, was developed, in which the bearing rating life calculation can be included. The proposed prognostics methodology can be used in cases where there is less  or noisy data. The above approach can even be used in cases whereby there is no prior knowledge of the system or little measurement data on the conditions. The presented BHM approach can also be used to predict the remaining useful life (RUL) of bearings both in situations in which the bearing is considered to be in a healthy state and in situations after a defect has been detected.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2018.
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Keywords [en]
Prognostics and Health Management (PHM), Diagnostics, Prognostics, Bayesian, Hierarchical, L10, Prediction, Bearing, Li-ion battery, RUL, Particle filter, Model-based, Data-driven, Algorithms, Railway track geometry
National Category
Engineering and Technology Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-68345ISBN: 978-91-7790-106-8 (print)ISBN: 978-91-7790-107-5 (electronic)OAI: oai:DiVA.org:ltu-68345DiVA, id: diva2:1197741
Public defence
2018-05-31, C305, Luleå, 10:00 (English)
Opponent
Supervisors
Available from: 2018-04-16 Created: 2018-04-13 Last updated: 2018-05-31Bibliographically approved
List of papers
1. Hybrid modelling for failure diagnosis and prognosis in the transport sector: Acquired data and synthetic data
Open this publication in new window or tab >>Hybrid modelling for failure diagnosis and prognosis in the transport sector: Acquired data and synthetic data
2015 (English)In: Dyna, ISSN 0012-7361, Vol. 90, no 2, p. 139-145Article in journal (Refereed) Published
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.

Abstract [es]

La seguridad en el campo del transporte es un punto crítico. Así, el sector ferroviario y el de la aeronáutica precisan de formas para predecir el comportamiento de trenes y aeronaves, respectivamente. Con esta información se pueden llevar a cabo las gestiones de mantenimiento necesarias para el correcto funcionamiento de los activos y reducir el número de fallos que puedan causar un accidente.Sin embargo, la falta de datos suficientes sobre estados con fallo de dichos sistemas hace que esta tarea sea complicada.Esta carencia de información hace que se puedan producir fallos ocultos o fallos desconocidos. Al tratarse la normativa del sector del transporte muy restrictivaen este aspecto, se tiende a reemplazar los componentes en estados tempranos de su degradación, lo que supone un desaprovechamiento de la vida de dichos componentes.En el presente artículo se propone una metodología para abordar esa limitación. Dicha metodología consiste en la fusión de datos de dos fuentes: por un lado, los datos adquiridos del sistema real; y, por otro lado, datos sintéticos generados a través de modelos físicos. Dichos modelos físicos han de estar construidos de forma que sean capaces de reproducir los principales modos de fallo que pueden ocurrir en dichos sistemas.Esta fusión de datos, que formaun modelo híbrido, permite no sólo clasificar el estado del sistema según los modos de fallo previamente estipulados, sino también definirnuevos modos de fallo que no concuerden con ninguno de los modos de fallo anteriores, mejorando los procesos de diagnosis y prognosis.

National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-4760 (URN)10.6036/7252 (DOI)000357051600012 ()2bfd6141-3c2e-4fd0-8e23-6ca21e2f7807 (Local ID)2bfd6141-3c2e-4fd0-8e23-6ca21e2f7807 (Archive number)2bfd6141-3c2e-4fd0-8e23-6ca21e2f7807 (OAI)
Note

Validerad; 2015; Nivå 2; 20150304 (urklet);

Spanish title: Modelización híbrida para el diagnóstico y pronóstico de fallos en el sector del transporte : Datos adquiridos y datos sintéticoshybrid modelling for failure diagnosis and prognosis in the transport sector

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2019-04-23Bibliographically approved
2. Particle filter-based prognostic approach for railway track geometry
Open this publication in new window or tab >>Particle filter-based prognostic approach for railway track geometry
Show others...
2017 (English)In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 96, p. 226-238Article in journal (Refereed) Published
Abstract [en]

Track degradation of ballasted railway track systems has to be measured on a regular basis, and these tracks must be maintained by tamping. Tamping aims to restore the geometry to its original shape to ensure an efficient, comfortable and safe transportation system. To minimize the disturbance introduced by tamping, this action has to be planned in advance. Track degradation forecasts derived from regression methods are used to predict when the standard deviation of a specific track section will exceed a predefined maintenance or safety limit. This paper proposes a particle filter-based prognostic approach for railway track degradation; this approach is demonstrated by examining different railway switches. The standard deviation of the longitudinal track degradation is studied, and forecasts of the maintenance limit intersection are derived. The particle filter-based prognostic results are compared with the standard regression method results for four railway switches, and the particle filter method shows similar or better result for the four cases. For longer prediction times, the error of the proposed method is equal to or smaller than that of the regression method. The main advantage of the particle filter-based prognostic approach is its ability to generate a probabilistic result based on input parameters with uncertainties. The distributions of the input parameters propagate through the filter, and the remaining useful life is presented using a particle distribution.

Place, publisher, year, edition, pages
Elsevier, 2017
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-63143 (URN)10.1016/j.ymssp.2017.04.010 (DOI)000401886800015 ()2-s2.0-85019145932 (Scopus ID)
Note

Validerad; 2017; Nivå 2; 2017-04-25 (andbra)

Available from: 2017-04-25 Created: 2017-04-25 Last updated: 2018-09-13Bibliographically approved
3. Bayesian hierarchical model-based prognostics for lithium-ion batteries
Open this publication in new window or tab >>Bayesian hierarchical model-based prognostics for lithium-ion batteries
2018 (English)In: Reliability Engineering & System Safety, ISSN 0951-8320, E-ISSN 1879-0836, Vol. 172, p. 25-35Article in journal (Refereed) Published
Abstract [en]

To optimise operation and maintenance, knowledge of the ability to perform the required functions is vital. The ability is governed by the usage of the system (operational issues) and availability aspects like reliability of different components. This paper proposes a Bayesian hierarchical model (BHM)-based prognostics approach applied to Li-ion batteries, where the goal is to analyse and predict the discharge behaviour of such batteries with variable load profiles and variable amounts of available discharge data. The BHM approach enables inferences for both individual batteries and groups of batteries. Estimates of the hierarchical model parameters and the individual battery parameters are presented, and dependencies on load cycles are inferred. A BHM approach where the operational and reliability aspects end of life (EoD) and end of life (EoL) is studied where its shown that predictions of EoD can be made accurately with a variable amount of battery data. Without access to measurements, e.g. predicting a new battery, the predictions are based only on the prior distributions describing the similarity within the group of batteries and their dependency on the load cycle. A discharge cycle dependency can also be identified in the result giving the opportunity to predict the battery reliability.

Place, publisher, year, edition, pages
Elsevier, 2018
National Category
Other Civil Engineering Probability Theory and Statistics
Research subject
Operation and Maintenance; Mathematical Statistics
Identifiers
urn:nbn:se:ltu:diva-66884 (URN)10.1016/j.ress.2017.11.020 (DOI)000424960000003 ()2-s2.0-85037540599 (Scopus ID)
Note

Validerad;2018;Nivå 2;2017-12-14 (andbra)

Available from: 2017-12-04 Created: 2017-12-04 Last updated: 2018-06-13Bibliographically approved
4. Simulations and measurements of the dynamic response of a paper machine roller
Open this publication in new window or tab >>Simulations and measurements of the dynamic response of a paper machine roller
Show others...
2016 (English)In: Insight (Northampton), ISSN 1354-2575, E-ISSN 1754-4904, Vol. 58, no 4, p. 210-212Article in journal (Refereed) Published
Abstract [en]

The paper industry is a highly automated industry that includes many different production steps, in which a variety of machine components are used. In a paper machine, where the pulp is being transformed into paper, rotating components such as bearing-mounted rollers play an important part in driving the wire with the pulp through the process. In this type of industry with a serial layout, the failure of a single roller or bearing could lead to the stoppage of several production steps, with costly consequences as a result. To ensure and optimise asset availability, a condition-based maintenance (CBM) strategy could be implemented. However, CBM is dependent on an appropriate condition monitoring (CM) technique to detect a physical phenomenon that defines the state of critical components or systems. For the development of CM techniques, it is therefore important to understand and model the physical behaviour of the system in question. In this paper, the behaviour of a roller in a paper machine is analysed using the finite element method (FEM). The physical model was compared with vibration measurements collected from an online monitoring system and an experimental modal analysis.

National Category
Other Civil Engineering Fluid Mechanics and Acoustics
Research subject
Operation and Maintenance; Engineering Acoustics
Identifiers
urn:nbn:se:ltu:diva-12822 (URN)10.1784/insi.2016.58.4.210 (DOI)000373565000008 ()2-s2.0-84963831882 (Scopus ID)bf9500b5-5abe-4388-b1d1-9ec8202757ed (Local ID)bf9500b5-5abe-4388-b1d1-9ec8202757ed (Archive number)bf9500b5-5abe-4388-b1d1-9ec8202757ed (OAI)
Note

Validerad; 2016; Nivå 2; 20160414 (madmis)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
5. Bearing life prediction with informed hyper-prior distribution: A Bayesian hierarchical approach
Open this publication in new window or tab >>Bearing life prediction with informed hyper-prior distribution: A Bayesian hierarchical approach
(English)In: IEEE/ASME transactions on mechatronics, ISSN 1083-4435, E-ISSN 1941-014XArticle in journal (Refereed) Submitted
Place, publisher, year, edition, pages
IEEE Robotics and Automation Society
Keywords
Prognostics, Bayesian Hierarchical, Probability distribution, Remaining useful life, Bearing life prediction, L10
National Category
Engineering and Technology
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-68340 (URN)
Projects
SKF- UTC
Available from: 2018-04-13 Created: 2018-04-13 Last updated: 2018-04-13

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