1,967 research outputs found
Perpetual Learning Framework based on Type-2 Fuzzy Logic System for a Complex Manufacturing Process
This paper introduces a perpetual type-2 Neuro-Fuzzy modelling structure for continuous learning and its application to the complex thermo-mechanical metal process of steel Friction Stir Welding (FSW). The ‘perpetual’ property refers to the capability of the proposed system to continuously learn from new process data, in an incremental learning fashion. This is particularly important in industrial/manufacturing processes, as it eliminates the need to retrain the model in the presence of new data, or in the case of any process drift. The proposed structure evolves through incremental, hybrid (supervised/unsupervised) learning, and accommodates new sample data in a continuous fashion. The human-like information capture paradigm of granular computing is used along with an interval type-2 neural-fuzzy system to develop a modelling structure that is tolerant to the uncertainty in the manufacturing data (common challenge in industrial/manufacturing data). The proposed method relies on the creation of new fuzzy rules which are updated and optimised during the incremental learning process. An iterative pruning strategy in the model is then employed to remove any redundant rules, as a result of the incremental learning process. The rule growing/pruning strategy is used to guarantee that the proposed structure can be used in a perpetual learning mode. It is demonstrated that the proposed structure can effectively learn complex dynamics of input-output data in an adaptive way and maintain good predictive performance in the metal processing case study of steel FSW using real manufacturing dat
Deep Stacked Stochastic Configuration Networks for Lifelong Learning of Non-Stationary Data Streams
The concept of SCN offers a fast framework with universal approximation
guarantee for lifelong learning of non-stationary data streams. Its adaptive
scope selection property enables for proper random generation of hidden unit
parameters advancing conventional randomized approaches constrained with a
fixed scope of random parameters. This paper proposes deep stacked stochastic
configuration network (DSSCN) for continual learning of non-stationary data
streams which contributes two major aspects: 1) DSSCN features a
self-constructing methodology of deep stacked network structure where hidden
unit and hidden layer are extracted automatically from continuously generated
data streams; 2) the concept of SCN is developed to randomly assign inverse
covariance matrix of multivariate Gaussian function in the hidden node addition
step bypassing its computationally prohibitive tuning phase. Numerical
evaluation and comparison with prominent data stream algorithms under two
procedures: periodic hold-out and prequential test-then-train processes
demonstrate the advantage of proposed methodology.Comment: This paper has been published in Information Science
Evolving Clustering Algorithms And Their Application For Condition Monitoring, Diagnostics, & Prognostics
Applications of Condition-Based Maintenance (CBM) technology requires effective yet generic data driven methods capable of carrying out diagnostics and prognostics tasks without detailed domain knowledge and human intervention. Improved system availability, operational safety, and enhanced logistics and supply chain performance could be achieved, with the widespread deployment of CBM, at a lower cost level. This dissertation focuses on the development of a Mutual Information based Recursive Gustafson-Kessel-Like (MIRGKL) clustering algorithm which operates recursively to identify underlying model structure and parameters from stream type data. Inspired by the Evolving Gustafson-Kessel-like Clustering (eGKL) algorithm, we applied the notion of mutual information to the well-known Mahalanobis distance as the governing similarity measure throughout. This is also a special case of the Kullback-Leibler (KL) Divergence where between-cluster shape information (governed by the determinant and trace of the covariance matrix) is omitted and is only applicable in the case of normally distributed data. In the cluster assignment and consolidation process, we proposed the use of the Chi-square statistic with the provision of having different probability thresholds. Due to the symmetry and boundedness property brought in by the mutual information formulation, we have shown with real-world data that the algorithm’s performance becomes less sensitive to the same range of probability thresholds which makes system tuning a simpler task in practice. As a result, improvement demonstrated by the proposed algorithm has implications in improving generic data driven methods for diagnostics, prognostics, generic function approximations and knowledge extractions for stream type of data.
The work in this dissertation demonstrates MIRGKL’s effectiveness in clustering and knowledge representation and shows promising results in diagnostics and prognostics applications
Artificial cognitive architecture with self-learning and self-optimization capabilities. Case studies in micromachining processes
Tesis doctoral inédita leÃda en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de IngenierÃa Informática. Fecha de lectura : 22-09-201
Vehicle level health assessment through integrated operational scalable prognostic reasoners
Today’s aircraft are very complex in design and need constant monitoring of the
systems to establish the overall health status. Integrated Vehicle Health
Management (IVHM) is a major component in a new future asset management
paradigm where a conscious effort is made to shift asset maintenance from a
scheduled based approach to a more proactive and predictive approach. Its goal is
to maximize asset operational availability while minimising downtime and the
logistics footprint through monitoring deterioration of component conditions.
IVHM involves data processing which comprehensively consists of capturing data
related to assets, monitoring parameters, assessing current or future health
conditions through prognostics and diagnostics engine and providing
recommended maintenance actions.
The data driven prognostics methods usually use a large amount of data to learn
the degradation pattern (nominal model) and predict the future health. Usually
the data which is run-to-failure used are accelerated data produced in lab
environments, which is hardly the case in real life. Therefore, the nominal model
is far from the present condition of the vehicle, hence the predictions will not be
very accurate. The prediction model will try to follow the nominal models which
mean more errors in the prediction, this is a major drawback of the data driven
techniques.
This research primarily presents the two novel techniques of adaptive data driven
prognostics to capture the vehicle operational scalability degradation. Secondary
the degradation information has been used as a Health index and in the Vehicle
Level Reasoning System (VLRS). Novel VLRS are also presented in this research
study. The research described here proposes a condition adaptive prognostics
reasoning along with VLRS
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