Browsing by Autor "Mariela Cerrada"

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A Reliability-Based Failure Management Application Using Intelligent Hybrid Systems
(Elsevier BV, 2000) José Aguilar; Mariela Cerrada; Katiuska Morillo
A Systematic Review of Fuzzy Formalisms for Bearing Fault Diagnosis
(Institute of Electrical and Electronics Engineers, 2018) Chuan Li; José Valente de Oliveira; Mariela Cerrada; Diego Cabrera; René–Vinicio Sánchez; Grover Zurita
Bearings are fundamental mechanical components in rotary machines (engines, gearboxes, generators, radars, turbines, etc.) that have been identified as one of the primary causes of failure in these machines. This makes bearing fault diagnosis (detection, classification, and prognosis) an economic very relevant topic, as well as a technically challenging one as evaluated by the extensive research literature on the subject. This paper employs a systematic methodology to identify, summarize, analyze, and interpret the primary literature on fuzzy formalisms for bearing fault diagnosis from 2000 to 2017 (March). The main contribution is an updated, unbiased, and (to a higher extend) repeatable search, review, and analysis (summary, classification, and critique) of the available approaches resorting to fuzzy formalisms in this trendy topic. A discussion on a new promising future research direction is provided. A comprehensive list of references is also included.
Collective Learning in Multi-Agent Systems Based on Cultural Algorithms
(Latin American Center for Computer Studies, 2014) Juan Terán; José Aguilar; Mariela Cerrada
   This paper aims to present a learning model for coordination schemes in Multi-Agent Systems (MAS) based on Cultural Algorithms (CA). In this model, the individuals (one of the CA components) are the different conversations that may occur in any multi-agent systems, and the coordination scheme learned is at the level of the way to perform the communication protocols into the conversation. A conversation can has sub-conversations, and the sub-conversations and/or conversations are identified with a particular type of conversation associated with a certain interaction patterns. The interaction patterns use the coordination mechanisms existing in the literature. In order to simulate the proposed learning model, we develop a computational tool called CLEMAS, which has been used to apply the model to a case of study in industrial automation, related to a Faults Management System based on Agents.  
DESIGN OF AN INDUSTRIAL AUTOMATION ARCHITECTURE BASED ON MULTI-AGENTS SYSTEMS
(Elsevier BV, 2005) César Bravo; José Aguilar; Mariela Cerrada; Francklin Rivas
DYNAMICAL ADAPTIVE FUZZY SYSTEMS: AN APPLICATION ON SYSTEM IDENTIFICATION
(Elsevier BV, 2002) José Aguilar; Mariela Cerrada; Eliezer Colina-Morles; Α. Τitli
Fault diagnosis in spur gears based on genetic algorithm and random forest
(Elsevier BV, 2015) Mariela Cerrada; Grover Zurita; Diego Cabrera; René–Vinicio Sánchez; Mariano Artés; Chuan Li
Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition
(Higher Education Press, 2015) Diego Cabrera; Fernando Sancho; René–Vinicio Sánchez; Grover Zurita; Mariela Cerrada; Chuan Li; Rafael E. Vásquez
Mathematical Models of Coordination Mechanisms in Multi-Agent Systems
(Latin American Center for Computer Studies, 2013) Juan Terán; José Aguilar; Mariela Cerrada
   The good performance of a set of computer systems based on agents depends on the coherence degree and coordination between their activities. The study of coordination problem is an important topic for designers and researchers in the multi-agents systems field. There are several coordination mechanisms in the current literature, being the auction and the contract net the most popular ones. These mechanisms allow the agents to allocate resources and tasks to achieve their objectives. This paper aims to present formal models of the auction and the contract net as coordination mechanisms in multi-agents systems based on FIPA (Foundation for Intelligent Physical Agents) Protocols. Mathematical equations describe the different parameters characterizing the auction and the contract net mechanisms; they allow define a generic structure of each mechanism and groups of agents can create several instances of them to coordinate their needs.  
Observer-biased bearing condition monitoring: From fault detection to multi-fault classification
(Elsevier BV, 2016) Chuan Li; José Valente de Oliveira; Mariela Cerrada; Fannia Pacheco; Diego Cabrera; René–Vinicio Sánchez; Grover Zurita
Reinforcement Learning in System Identification
(2008) Mariela Cerrada; José Aguilar
The Reinforcement Learning (RL) problem has been widely researched an applied in several areas (Sutton & Barto, 1998; Sutton, 1988; Singh & Sutton, 1996; Schapire & Warmuth, 1996; Tesauro, 1995; Si & Wang, 2001; Van Buijtenen et al., 1998). In dynamical environments, a learning agent gets rewards or penalties, according to its performance for learning good actions. In identification problems, information from the environment is needed in order to propose an approximate system model, thus, RL can be used for taking the on-line information taking. Off-line learning algorithms have reported suitable results in system identification (Ljung, 1997); however these results are bounded on the available data, their quality and quantity. In this way, the development of on-line learning algorithms for system identification is an important contribution. In this work, it is presented an on-line learning algorithm based on RL using the Temporal Difference (TD) method, for identification purposes. Here, the basic propositions of RL with TD are used and, as a consequence, the linear TD(λ) algorithm proposed in (Sutton & Barto, 1998) is modified and adapted for systems identification and the reinforcement signal is generically defined according to the temporal difference and the identification error. Thus, the main contribution of this paper is the proposition of a generic on-line identification algorithm based on RL. The proposed algorithm is applied in the parameters adjustment of a Dynamical Adaptive Fuzzy Model (DAFM) (Cerrada et al., 2002; Cerrada et al., 2005). In this case, the prediction function is a non-linear function of the fuzzy model parameters and a non-linear TD(λ) algorithm is obtained for the on-line adjustment of the DAFM parameters. In the next section the basic aspects about the RL problem and the DAFM are revised. Third section is devoted to the proposed on-line learning algorithm for identification purposes. The algorithm performance for time-varying non-linear systems identification is showed with an illustrative example in section fourth. Finally, conclusions are presented.