5 research outputs found
Accommodating maintenance in prognostics
Error on title page - year of award is 2021Steam turbines are an important asset of nuclear power plants, and are required to
operate reliably and efficiently. Unplanned outages have a significant impact on the
ability of the plant to generate electricity. Therefore, condition-based maintenance (CBM)
can be used for predictive and proactive maintenance to avoid unplanned outages while
reducing operating costs and increasing the reliability and availability of the plant. In
CBM, the information gathered can be interpreted for prognostics (the prediction of
failure time or remaining useful life (RUL)).
The aim of this project was to address two areas of challenges in prognostics, the
selection of predictive technique and accommodation of post-maintenance effects, to
improve the efficacy of prognostics. The selection of an appropriate predictive algorithm
is a key activity for an effective development of prognostics. In this research, a formal
approach for the evaluation and selection of predictive techniques is developed to
facilitate a methodic selection process of predictive techniques by engineering experts.
This approach is then implemented for a case study provided by the engineering experts.
Therefore, as a result of formal evaluation, a probabilistic technique the Bayesian Linear
Regression (BLR) and a non-probabilistic technique the Support Vector Regression (SVR)
were selected for prognostics implementation.
In this project, the knowledge of prognostics implementation is extended by including
post maintenance affects into prognostics. Maintenance aims to restore a machine into a
state where it is safe and reliable to operate while recovering the health of the machine.
However, such activities result in introduction of uncertainties that are associated with
predictions due to deviations in degradation model. Thus, affecting accuracy and efficacy
of predictions. Therefore, such vulnerabilities must be addressed by incorporating the
information from maintenance events for accurate and reliable predictions. This thesis
presents two frameworks which are adapted for probabilistic and non-probabilistic
prognostic techniques to accommodate maintenance. Two case studies: a real-world case
study from a nuclear power plant in the UK and a synthetic case study which was
generated based on the characteristics of a real-world case study are used for the
implementation and validation of the frameworks. The results of the implementation
hold a promise for predicting remaining useful life while accommodating maintenance
repairs. Therefore, ensuring increased asset availability with higher reliability,
maintenance cost effectiveness and operational safety.Steam turbines are an important asset of nuclear power plants, and are required to
operate reliably and efficiently. Unplanned outages have a significant impact on the
ability of the plant to generate electricity. Therefore, condition-based maintenance (CBM)
can be used for predictive and proactive maintenance to avoid unplanned outages while
reducing operating costs and increasing the reliability and availability of the plant. In
CBM, the information gathered can be interpreted for prognostics (the prediction of
failure time or remaining useful life (RUL)).
The aim of this project was to address two areas of challenges in prognostics, the
selection of predictive technique and accommodation of post-maintenance effects, to
improve the efficacy of prognostics. The selection of an appropriate predictive algorithm
is a key activity for an effective development of prognostics. In this research, a formal
approach for the evaluation and selection of predictive techniques is developed to
facilitate a methodic selection process of predictive techniques by engineering experts.
This approach is then implemented for a case study provided by the engineering experts.
Therefore, as a result of formal evaluation, a probabilistic technique the Bayesian Linear
Regression (BLR) and a non-probabilistic technique the Support Vector Regression (SVR)
were selected for prognostics implementation.
In this project, the knowledge of prognostics implementation is extended by including
post maintenance affects into prognostics. Maintenance aims to restore a machine into a
state where it is safe and reliable to operate while recovering the health of the machine.
However, such activities result in introduction of uncertainties that are associated with
predictions due to deviations in degradation model. Thus, affecting accuracy and efficacy
of predictions. Therefore, such vulnerabilities must be addressed by incorporating the
information from maintenance events for accurate and reliable predictions. This thesis
presents two frameworks which are adapted for probabilistic and non-probabilistic
prognostic techniques to accommodate maintenance. Two case studies: a real-world case
study from a nuclear power plant in the UK and a synthetic case study which was
generated based on the characteristics of a real-world case study are used for the
implementation and validation of the frameworks. The results of the implementation
hold a promise for predicting remaining useful life while accommodating maintenance
repairs. Therefore, ensuring increased asset availability with higher reliability,
maintenance cost effectiveness and operational safety
Models and analysis of vocal emissions for biomedical applications
This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies
Smart Monitoring and Control in the Future Internet of Things
The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things
Exploiting Robust Multivariate Statistics and Data Driven Techniques for Prognosis and Health Management
This thesis explores state of the art robust multivariate statistical methods and data driven techniques to holistically perform prognostics and health management (PHM). This provides a means to enable the early detection, diagnosis and prognosis of future asset failures. In this thesis, the developed PHM methodology is applied to wind turbine drive train components, specifically focussed on planetary gearbox bearings and gears.
A novel methodology for the identification of relevant time-domain statistical features based upon robust statistical process control charts is presented for high frequency bearing accelerometer data. In total, 28 time-domain statistical features were evaluated for their capabilities as leading indicators of degradation. The results of this analysis describe the extensible multivariate “Moments’ model” for the encapsulation of bearing operational behaviour. This is presented, enabling the early degradation of detection, predictive diagnostics and estimation of remaining useful life (RUL).
Following this, an extended physics of failure model based upon low frequency SCADA data for the quantification of wind turbine gearbox condition is described. This extends the state of the art, whilst defining robust performance charts for quantifying component condition. Normalisation against loading of the turbine and transient states based upon empirical data is performed in the bivariate domain, with extensibility into the multivariate domain if necessary. Prognosis of asset condition is found to be possible with the assistance of artificial neural networks in order to provide business intelligence to the planning and scheduling of effective maintenance actions.
These multivariate condition models are explored with multivariate distance and similarity metrics for to exploit traditional data mining techniques for tacit knowledge extraction, ensemble diagnosis and prognosis. Estimation of bearing remaining useful life is found to be possible, with the derived technique correlating strongly to bearing life (r = .96