The Experts below are selected from a list of 288 Experts worldwide ranked by ideXlab platform

S W Khobragade - One of the best experts on this subject based on the ideXlab platform.

David G Lewicki - One of the best experts on this subject based on the ideXlab platform.

  • experimental evaluation of a structure based Connectionist Network for fault diagnosis of helicopter gearboxes
    Journal of Mechanical Design, 1998
    Co-Authors: Vinay B Jammu, Kourosh Danai, David G Lewicki
    Abstract:

    This paper presents the experimental evaluation of the Structure-Based Connectionist Network (SBCN) fault diagnostic system introduced in the preceding article (Jammu et al., 1998). For this, vibration data from two different helicopter gearboxes: OH-58A and S-61, are used. A salient feature of SBCN is its reliance on the knowledge of the gearbox structure and the type of features obtained from processed vibration signals as a substitute to training. To formulate this knowledge, approximate vibration transfer models are developed for the two gearboxes and utilized to derive the connection weights representing the influence of component faults on vibration features. The validity of the structural influences is evaluated by comparing them with those obtained from experimental RMS values. These influences are also evaluated by comparing them with the weights of a Connectionist Network trained through supervised leaning. The results indicate general agreement between the modeled and experimentally obtained influences. The vibration data from the two gearboxes are also used to evaluate the performance of SBCN in fault diagnosis. The diagnostic results indicate that the SBCN is effective in detecting the presence of faults and isolating them within gearbox subsystems based on structural influences, but its performance is not as good in isolating faulty components, mainly due to lack of appropriate vibration features.

  • structure based Connectionist Network for fault diagnosis of helicopter gearboxes
    Journal of Mechanical Design, 1998
    Co-Authors: Vinay B Jammu, Kourosh Danai, David G Lewicki
    Abstract:

    A new method of diagnosis is introduced for helicopter gearboxes that relies on the knowledge of the gearbox ''structure'' and characteristics of the ' features'' of vibration for component fault isolation. Both the structural knowledge and featural knowledge in this method are defined as the fuzzy weights of a Connectionist Network that maps each sampled set of vibration features, obtained from a signal analyzer, into fault possibility values associated with individual gearbox components. The structural weights in this Network are defined to represent the influence of gearbox component faults on the overall vibration sensed by each accelerometer, and the featural weights are defined to denote the influence of components faults on individual vibration features. Given the extremely complex structure of helicopter gearboxes which prohibits accurate modeling of the effect of faults on their vibration, the structural weights in this method are defined based on the root mean square value of the frequency response of a simplified lumped-mass model of the gearbox. The experimental evaluation of the method based on vibration data from two different gearboxes is included in a separate paper.

  • improving the performance of the structure based Connectionist Network for diagnosis of helicopter gearboxes
    1996
    Co-Authors: Vinay B Jammu, Kourosh Danai, David G Lewicki
    Abstract:

    Abstract : A diagnostic method is introduced for helicopter gearboxes that uses knowledge of the gearbox structure and characteristics of the features' of vibration to define the influences of faults on features. The structural influences' in this method are defined based on the root mean square value of vibration obtained from a simplified lumped-mass model of the gearbox. The structural influences are then converted to fuzzy variables, to account for the approximate nature of the lumped-mass model, and used as the weights of a Connectionist Network. Diagnosis in this Structure-Based Connectionist Network (SBCN) is performed by propagating the abnormal vibration features through the weights of SBCN to obtain fault possibility values for each component in the gearbox. Upon occurrence of misdiagnoses, the SBCN also has the ability to improve its diagnostic performance. For this, a supervised training method is presented which adapts the weights of SBCN to minimize the number of misdiagnoses. For experimental evaluation of the SBCN, vibration data from a OH-58A helicopter gearbox collected at NASA Lewis Research Center is used. Diagnostic results indicate that the SBCN is able to diagnose about 80% of the faults without training, and is able to improve its performance to nearly 100% after training.

Frédéric Alexandre - One of the best experts on this subject based on the ideXlab platform.

  • Plausible Self-Organizing Maps for Speech Recognition
    Artificial Neural Nets and Genetic Algorithms, 1993
    Co-Authors: Lionel Beaugé, Stephane Durand, Frédéric Alexandre
    Abstract:

    A major problem in Connectionist phonetic acoustic decoding is the way to present acoustic signal to the Network. Neurobiological data about the inner ear and the primary auditory cortex can be very helpful but are rare. On the other hand, other biological works have shown that structure and functioning of the visual cortex, which have been extensively studied, are very close to the structure and functioning of the auditory cortex.It has been shown that a simple two-layered Network of linear neurons can organize itself to extract the complete information contained in a set of presented patterns. This model has been applied to visual information and has revealed orientation and spatial frequency selective cells.Such principles have been applied to speech recognition. So we have designed two kinds of maps. The first kind is able to represent frequency characteristics of the signal (e.g.: formantic structure). The second kind takes into account dynamic aspects of the signal (e.g.: formantic transition).First, these results have been analyzed both from a phonetic and a signal processing point of view and show very interesting representation of the signal. Second, these representations can be used as the input map of a dynamic Connectionist Network, for speech recognition. The input maps have a selective activity with regard to phonemic structures, and enable dynamic Networks to differentiate the phonemes.

David S Touretzky - One of the best experts on this subject based on the ideXlab platform.

  • boltzcons dynamic symbol structures in a Connectionist Network
    Artificial Intelligence, 1990
    Co-Authors: David S Touretzky
    Abstract:

    Abstract BoltzCONS is a Connectionist model that dynamically creates and manipulates composite symbol structures. These structures are implemented using a functional analog of linked lists, but BoltzCONS employs distributed representations and associative retrieval in place of a conventional memory organization. Associative retrieval leads to some interesting properties, e.g., the model can instantaneously access any uniquely-named internal node of a tree. But the point of the work is not to reimplement linked lists in some peculiar new way; it is to show how neural Networks can exhibit compositionality and distal access (the ability to reference a complex structure via an abbreviated tag), two properties that distinguish symbol processing from lower-level cognitive functions such as pattern recognition. Unlike certain other neural net models, BoltzCONS represents objects as a collection of superimposed activity patterns rather than as a set of weights. It can therefore create new structured objects dynamically, without reliance on iterative training procedures, without rehearsal of previously-learned patterns, and without resorting to grandmother cells.

Michal Raveh - One of the best experts on this subject based on the ideXlab platform.

  • the influence of morphological regularities on the dynamics of a Connectionist Network
    Brain and Language, 1999
    Co-Authors: Jay G Rueckl, Michal Raveh
    Abstract:

    The effects of morphological regularities on the behavior of Connectionist Networks were studied by training identical Networks on orthographic-semantic mappings that either contained such regularities or did not. Morphological regularities had a substantial impact on both the amount of training needed to learn a mapping and the number of words that could be included in the training set. A variety of analyses demonstrated how morphological regularities structure the organization and componentiality of a Network's internal representations.

  • Morphological Priming, Fragment Completion, and Connectionist Networks
    Journal of Memory and Language, 1997
    Co-Authors: Jay G Rueckl, Michal Raveh, Michelle Mikolinski, Caroline S. Miner, Frank Mars
    Abstract:

    Abstract Long-term morphological priming is a form of repetition priming in which the identification of a word is primed by the prior presentation of a morphologically related word. We investigated morphological priming using a variant of the fragment completion task in which a word is briefly presented with one of its letters replaced by a pattern mask and subjects attempt to identify the letter “hidden” by the mask. In Experiment 1 a levels-of-processing manipulation at study was found to affect free recall but not masked fragment completion, suggesting that repetition priming in the latter task is not the result of explicit memory processes. Subsequent experiments revealed that both masked and standard fragment completion are influenced by morphological priming and that, although this effect cannot be attributed to the orthographic and phonological similarity of morphologically related words, it does vary in magnitude as a function of orthographic similarity. These results are consistent with a Connectionist account of morphological priming in which morphological effects arise from the activation dynamics of a Connectionist Network even though morphological relationships are not explicitly represented in this Network.