5,539 research outputs found
Learning models of plant behavior for anomaly detection and condition monitoring
Providing engineers and asset managers with a too] which can diagnose faults within transformers can greatly assist decision making on such issues as maintenance, performance and safety. However, the onus has always been on personnel to accurately decide how serious a problem is and how urgently maintenance is required. In dealing with the large volumes of data involved, it is possible that faults may not be noticed until serious damage has occurred. This paper proposes the integration of a newly developed anomaly detection technique with an existing diagnosis system. By learning a Hidden Markov Model of healthy transformer behavior, unexpected operation, such as when a fault develops, can be flagged for attention. Faults can then be diagnosed using the existing system and maintenance scheduled as required, all at a much earlier stage than would previously have been possible
International conference on software engineering and knowledge engineering: Session chair
The Thirtieth International Conference on Software Engineering and Knowledge Engineering (SEKE 2018) will be held at the Hotel Pullman, San Francisco Bay, USA, from July 1 to July 3, 2018. SEKE2018 will also be dedicated in memory of Professor Lofti Zadeh, a great scholar, pioneer and leader in fuzzy sets theory and soft computing.
The conference aims at bringing together experts in software engineering and knowledge engineering to discuss on relevant results in either software engineering or knowledge engineering or both. Special emphasis will be put on the transference of methods between both domains. The theme this year is soft computing in software engineering & knowledge engineering. Submission of papers and demos are both welcome
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State-of-the-art on research and applications of machine learning in the building life cycle
Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science
Bayesian Belief Network Model Quantification Using Distribution-Based Node Probability and Experienced Data Updates for Software Reliability Assessment
Since digital instrumentation and control systems are expected to play an essential role in safety systems in nuclear power plants (NPPs), the need to incorporate software failures into NPP probabilistic risk assessment has arisen. Based on a Bayesian belief network (BBN) model developed to estimate the number of software faults considering the software development lifecycle, we performed a pilot study of software reliability quantification using the BBN model by aggregating different experts' opinions. In this paper, we suggest the distribution-based node probability table (D-NPT) development method which can efficiently represent diverse expert elicitation in the form of statistical distributions and provides mathematical quantification scheme. Besides, the handbook data on U.S. software development and V&V and testing results for two nuclear safety software were used for a Bayesian update of the D-NPTs in order to reduce the BBN parameter uncertainty due to experts' different background or levels of experience. To analyze the effect of diverse expert opinions on the BBN parameter uncertainties, the sensitivity studies were conducted by eliminating the significantly different NPT estimates among expert opinions. The proposed approach demonstrates a framework that can effectively and systematically integrate different kinds of available source information to quantify BBN NPTs for NPP software reliability assessment
Search for transient ultralight dark matter signatures with networks of precision measurement devices using a Bayesian statistics method
We analyze the prospects of employing a distributed global network of
precision measurement devices as a dark matter and exotic physics observatory.
In particular, we consider the atomic clocks of the Global Positioning System
(GPS), consisting of a constellation of 32 medium-Earth orbit satellites
equipped with either Cs or Rb microwave clocks and a number of Earth-based
receiver stations, some of which employ highly-stable H-maser atomic clocks.
High-accuracy timing data is available for almost two decades. By analyzing the
satellite and terrestrial atomic clock data, it is possible to search for
transient signatures of exotic physics, such as "clumpy" dark matter and dark
energy, effectively transforming the GPS constellation into a 50,000km aperture
sensor array. Here we characterize the noise of the GPS satellite atomic
clocks, describe the search method based on Bayesian statistics, and test the
method using simulated clock data. We present the projected discovery reach
using our method, and demonstrate that it can surpass the existing constrains
by several order of magnitude for certain models. Our method is not limited in
scope to GPS or atomic clock networks, and can also be applied to other
networks of precision measurement devices.Comment: See also Supplementary Information located in ancillary file
An investigation into hazard-centric analysis of complex autonomous systems
This thesis proposes a hypothesis that a conventional, and essentially manual, HAZOP process can be
improved with information obtained with model-based dynamic simulation, using a Monte Carlo
approach, to update a Bayesian Belief model representing the expected relations between cause and
effects – and thereby produce an enhanced HAZOP. The work considers how the expertise of a
hazard and operability study team might be augmented with access to behavioural models,
simulations and belief inference models. This incorporates models of dynamically complex system
behaviour, considering where these might contribute to the expertise of a hazard and operability study
team, and how these might bolster trust in the portrayal of system behaviour. With a questionnaire
containing behavioural outputs from a representative systems model, responses were collected from a
group with relevant domain expertise. From this it is argued that the quality of analysis is dependent
upon the experience and expertise of the participants but this might be artificially augmented using
probabilistic data derived from a system dynamics model. Consequently, Monte Carlo simulations of
an improved exemplar system dynamics model are used to condition a behavioural inference model
and also to generate measures of emergence associated with the deviation parameter used in the study.
A Bayesian approach towards probability is adopted where particular events and combinations of
circumstances are effectively unique or hypothetical, and perhaps irreproducible in practice.
Therefore, it is shown that a Bayesian model, representing beliefs expressed in a hazard and
operability study, conditioned by the likely occurrence of flaw events causing specific deviant
behaviour from evidence observed in the system dynamical behaviour, may combine intuitive
estimates based upon experience and expertise, with quantitative statistical information representing
plausible evidence of safety constraint violation. A further behavioural measure identifies potential
emergent behaviour by way of a Lyapunov Exponent. Together these improvements enhance the
awareness of potential hazard cases
Deep learning in automated ultrasonic NDE -- developments, axioms and opportunities
The analysis of ultrasonic NDE data has traditionally been addressed by a
trained operator manually interpreting data with the support of rudimentary
automation tools. Recently, many demonstrations of deep learning (DL)
techniques that address individual NDE tasks (data pre-processing, defect
detection, defect characterisation, and property measurement) have started to
emerge in the research community. These methods have the potential to offer
high flexibility, efficiency, and accuracy subject to the availability of
sufficient training data. Moreover, they enable the automation of complex
processes that span one or more NDE steps (e.g. detection, characterisation,
and sizing). There is, however, a lack of consensus on the direction and
requirements that these new methods should follow. These elements are critical
to help achieve automation of ultrasonic NDE driven by artificial intelligence
such that the research community, industry, and regulatory bodies embrace it.
This paper reviews the state-of-the-art of autonomous ultrasonic NDE enabled by
DL methodologies. The review is organised by the NDE tasks that are addressed
by means of DL approaches. Key remaining challenges for each task are noted.
Basic axiomatic principles for DL methods in NDE are identified based on the
literature review, relevant international regulations, and current industrial
needs. By placing DL methods in the context of general NDE automation levels,
this paper aims to provide a roadmap for future research and development in the
area.Comment: Accepted version to be published in NDT & E Internationa
Critical Infrastructures: Enhancing Preparedness & Resilience for the Security of Citizens and Services Supply Continuity: Proceedings of the 52nd ESReDA Seminar Hosted by the Lithuanian Energy Institute & Vytautas Magnus University
Critical Infrastructures Preparedness and Resilience is a major societal security issue in modern society. Critical Infrastructures (CIs) provide vital services to modern societies. Some CIs’ disruptions may endanger the security of the citizen, the safety of the strategic assets and even the governance continuity. The European Safety, Reliability and Data Association (ESReDA) as one of the most active EU networks in the field has initiated a project group on the “Critical Infrastructure/Modelling, Simulation and Analysis – Data”. The main focus of the project group is to report on the state of progress in MS&A of the CIs preparedness & resilience with a specific focus on the corresponding data availability and relevance.
In order to report on the most recent developments in the field of the CIs preparedness & resilience MS&A and the availability of the relevant data, ESReDA held its 52nd Seminar on the following thematic: “Critical Infrastructures: Enhancing Preparedness & Resilience for the security of citizens and services supply continuity”.
The 52nd ESReDA Seminar was a very successful event, which attracted about 50 participants from industry, authorities, operators, research centres, academia and consultancy companies.JRC.G.10-Knowledge for Nuclear Security and Safet
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