35,050 research outputs found
Intelligent Fault Analysis in Electrical Power Grids
Power grids are one of the most important components of infrastructure in
today's world. Every nation is dependent on the security and stability of its
own power grid to provide electricity to the households and industries. A
malfunction of even a small part of a power grid can cause loss of
productivity, revenue and in some cases even life. Thus, it is imperative to
design a system which can detect the health of the power grid and take
protective measures accordingly even before a serious anomaly takes place. To
achieve this objective, we have set out to create an artificially intelligent
system which can analyze the grid information at any given time and determine
the health of the grid through the usage of sophisticated formal models and
novel machine learning techniques like recurrent neural networks. Our system
simulates grid conditions including stimuli like faults, generator output
fluctuations, load fluctuations using Siemens PSS/E software and this data is
trained using various classifiers like SVM, LSTM and subsequently tested. The
results are excellent with our methods giving very high accuracy for the data.
This model can easily be scaled to handle larger and more complex grid
architectures.Comment: In proceedings of the 29th IEEE International Conference on Tools
with Artificial Intelligence (ICTAI) 2017 (full paper); 6 pages; 13 figure
Predictive Maintenance on the Machining Process and Machine Tool
This paper presents the process required to implement a data driven Predictive Maintenance (PdM) not only in the machine decision making, but also in data acquisition and processing. A short review of the different approaches and techniques in maintenance is given. The main contribution of this paper is a solution for the predictive maintenance problem in a real machining process. Several steps are needed to reach the solution, which are carefully explained. The obtained results show that the Preventive Maintenance (PM), which was carried out in a real machining process, could be changed into a PdM approach. A decision making application was developed to provide a visual analysis of the Remaining Useful Life (RUL) of the machining tool. This work is a proof of concept of the methodology presented in one process, but replicable for most of the process for serial productions of pieces
Fault Detection Effectiveness of Metamorphic Relations Developed for Testing Supervised Classifiers
In machine learning, supervised classifiers are used to obtain predictions
for unlabeled data by inferring prediction functions using labeled data.
Supervised classifiers are widely applied in domains such as computational
biology, computational physics and healthcare to make critical decisions.
However, it is often hard to test supervised classifiers since the expected
answers are unknown. This is commonly known as the \emph{oracle problem} and
metamorphic testing (MT) has been used to test such programs. In MT,
metamorphic relations (MRs) are developed from intrinsic characteristics of the
software under test (SUT). These MRs are used to generate test data and to
verify the correctness of the test results without the presence of a test
oracle. Effectiveness of MT heavily depends on the MRs used for testing. In
this paper we have conducted an extensive empirical study to evaluate the fault
detection effectiveness of MRs that have been used in multiple previous studies
to test supervised classifiers. Our study uses a total of 709 reachable mutants
generated by multiple mutation engines and uses data sets with varying
characteristics to test the SUT. Our results reveal that only 14.8\% of these
mutants are detected using the MRs and that the fault detection effectiveness
of these MRs do not scale with the increased number of mutants when compared to
what was reported in previous studies.Comment: 8 pages, AITesting 201
Data-driven Soft Sensors in the Process Industry
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work
Autonomous Fault Detection in Self-Healing Systems using Restricted Boltzmann Machines
Autonomously detecting and recovering from faults is one approach for
reducing the operational complexity and costs associated with managing
computing environments. We present a novel methodology for autonomously
generating investigation leads that help identify systems faults, and extends
our previous work in this area by leveraging Restricted Boltzmann Machines
(RBMs) and contrastive divergence learning to analyse changes in historical
feature data. This allows us to heuristically identify the root cause of a
fault, and demonstrate an improvement to the state of the art by showing
feature data can be predicted heuristically beyond a single instance to include
entire sequences of information.Comment: Published and presented in the 11th IEEE International Conference and
Workshops on Engineering of Autonomic and Autonomous Systems (EASe 2014
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