450 research outputs found
Gearbox Condition Monitoring Using Advanced Classifiers
New efficient and reliable methods for gearbox diagnostics are needed in automotive industry because of growing demand for production quality. This paper presents the application of two different classifiers for gearbox diagnostics – Kohonen Neural Networks and the Adaptive-Network-based Fuzzy Interface System (ANFIS). Two different practical applications are presented. In the first application, the tested gearboxes are separated into two classes according to their condition indicators. In the second example, ANFIS is applied to label the tested gearboxes with a Quality Index according to the condition indicators. In both applications, the condition indicators were computed from the vibration of the gearbox housing.
Health assessment of rotary machinery based on integrated feature selection and Gaussian mixed model
Bearing failure is the most common failure mode of all rotary machinery failures, and can interrupt the production in a plant causing unscheduled downtime and production losses. A bearing failure also has the potential to damage machinery causing soaring machinery repair and/or replacement costs. In order to prevent unexpected bearing failure, a health assessment method is proposed in this paper. It employs an integrated feature selection approach and Gaussian mixture model (GMM). Firstly, the integrated feature selection approach, which combines empirical mode decomposition (EMD), singular value decomposition (SVD) and Principal Component Analysis (PCA), processes nonlinear and non-stationary vibration signals of a bearing and extracts features for health assessment. Then, GMM is utilized to evaluate and track the health degradation of the bearing in terms of confidence values (CV). This method, which is notable for bearing health tracking and detect the defect at its incipient stage, can be used without the need for failure datasets in applications. Finally, the feasibility and efficiency of this method was validated by two datasets of different bearing experiments
The 1991 3rd NASA Symposium on VLSI Design
Papers from the symposium are presented from the following sessions: (1) featured presentations 1; (2) very large scale integration (VLSI) circuit design; (3) VLSI architecture 1; (4) featured presentations 2; (5) neural networks; (6) VLSI architectures 2; (7) featured presentations 3; (8) verification 1; (9) analog design; (10) verification 2; (11) design innovations 1; (12) asynchronous design; and (13) design innovations 2
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
Fuzzy Logic
Fuzzy Logic is becoming an essential method of solving problems in all domains. It gives tremendous impact on the design of autonomous intelligent systems. The purpose of this book is to introduce Hybrid Algorithms, Techniques, and Implementations of Fuzzy Logic. The book consists of thirteen chapters highlighting models and principles of fuzzy logic and issues on its techniques and implementations. The intended readers of this book are engineers, researchers, and graduate students interested in fuzzy logic systems
Novel pattern recognition methods for classification and detection in remote sensing and power generation applications
Novel pattern recognition methods for classification and detection in remote sensing and power generation application
Fault Diagnosis Of Sensor And Actuator Faults In Multi-Zone Hvac Systems
Globally, the buildings sector accounts for 30% of the energy consumption and
more than 55% of the electricity demand. Specifically, the Heating, Ventilation, and
Air Conditioning (HVAC) system is the most extensively operated component and it is
responsible alone for 40% of the final building energy usage. HVAC systems are used
to provide healthy and comfortable indoor conditions, and their main objective is to
maintain the thermal comfort of occupants with minimum energy usage.
HVAC systems include a considerable number of sensors, controlled actuators, and
other components. They are at risk of malfunctioning or failure resulting in reduced efficiency,
potential interference with the execution of supervision schemes, and equipment
deterioration. Hence, Fault Diagnosis (FD) of HVAC systems is essential to improve
their reliability, efficiency, and performance, and to provide preventive maintenance.
In this thesis work, two neural network-based methods are proposed for sensor and
actuator faults in a 3-zone HVAC system. For sensor faults, an online semi-supervised
sensor data validation and fault diagnosis method using an Auto-Associative Neural
Network (AANN) is developed. The method is based on the implementation of Nonlinear
Principal Component Analysis (NPCA) using a Back-Propagation Neural Network
(BPNN) and it demonstrates notable capability in sensor fault and inaccuracy
correction, measurement noise reduction, missing sensor data replacement, and in both
single and multiple sensor faults diagnosis. In addition, a novel on-line supervised multi-model approach for actuator fault diagnosis using Convolutional Neural Networks
(CNNs) is developed for single actuator faults. It is based a data transformation in
which the 1-dimensional data are configured into a 2-dimensional representation without
the use of advanced signal processing techniques. The CNN-based actuator fault
diagnosis approach demonstrates improved performance capability compared with the
commonly used Machine Learning-based algorithms (i.e., Support Vector Machine and
standard Neural Networks).
The presented schemes are compared with other commonly used HVAC fault diagnosis
methods for benchmarking and they are proven to be superior, effective, accurate,
and reliable. The proposed approaches can be applied to large-scale buildings with
additional zones
A Literature Review of Fault Diagnosis Based on Ensemble Learning
The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance
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