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11.
In this paper, a new approach for fault detection and isolation that is based on the possibilistic clustering algorithm is proposed. Fault detection and isolation (FDI) is shown here to be a pattern classification problem, which can be solved using clustering and classification techniques. A possibilistic clustering based approach is proposed here to address some of the shortcomings of the fuzzy c-means (FCM) algorithm. The probabilistic constraint imposed on the membership value in the FCM algorithm is relaxed in the possibilistic clustering algorithm. Because of this relaxation, the possibilistic approach is shown in this paper to give more consistent results in the context of the FDI tasks. The possibilistic clustering approach has also been used to detect novel fault scenarios, for which the data was not available while training. Fault signatures that change as a function of the fault intensities are represented as fault lines, which have been shown to be useful to classify faults that can manifest with different intensities. The proposed approach has been validated here through simulations involving a benchmark quadruple tank process and also through experimental case studies on the same setup. For large scale systems, it is proposed to use the possibilistic clustering based approach in the lower dimensional approximations generated by algorithms such as PCA. Towards this end, finally, we also demonstrate the key merits of the algorithm for plant wide monitoring study using a simulation of the benchmark Tennessee Eastman problem. 相似文献
12.
In this article, a subtractive clustering-based fuzzy identification method and a Sugeno-type fuzzy inference system are used for modeling in metal cutting. This approach is considered with its application on the experimental study of Boring and Trepanning Association (BTA) deep-hole drilling. The model for the surface roughness is identified by using the cutting speed and feed as input data and roughness as the output data. Using subtractive clustering in both input and output spaces performs the model-building process. Minimum error model is obtained through enumerative search of clustering parameters. The fuzzy model obtained is capable of predicting the surface roughness for a given set of inputs (speed and feed). Therefore, the operator can predict the quality of the surface for a given set of working parameters and will then be able to set the machining parameters to achieve a certain surface quality. The fuzzy model is verified experimentally by further experimentation using different sets of inputs. The tool life is also investigated using the same approach. The fuzzy inference system obtained is capable of predicting the tool life for a given set of cutting parameters. Therefore, the operator will be able to predict how many minutes the cutting tool is going to last and will set the time for the next tool change. 相似文献
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J. L. Díez J. L. Navarro A. Sala 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2007,11(10):973-983
In this work, simple modifications on the cost index of particular local-model fuzzy clustering algorithms are proposed in
order to improve the readability of the resulting models. The final goal is simultaneously providing local linear models (reasonably
close to the plant’s Jacobian) and clustering in the input space so that desirable characteristics (regarding final model
accuracy, and convexity and smoothness of the cluster membership functions) are improved with respect to other proposals in
literature. Some examples illustrate the proposed approach. 相似文献
15.
H原子对固态合金化颗粒表面成份分布影响的研究 总被引:1,自引:0,他引:1
本文研究了氢化物TiH2对Fe+Ti机械合金化非晶储氢合金的表面成份偏聚和性能的影响。应用X射线光电子谱(XPS)和俄歇电子谱(AES)对样品表面由表及里逐层测量成份分布。同时应用热重分析方法研究了样品氧化性能和产物。研究结果表明,在机械合金化过程中,H原子不仅能使FeTi非晶化,同时促使表面产生明显的Fe原子偏聚。表面Fe/Ti原子比接近7。非晶FeTi(H)相的氧化分两个阶段进行,Ti原子首先氧化(833K),随后Fe原子氧化(890K)。 相似文献
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The conventional way of solving the group technology (GT) problem is to start from an assignment of parts to machines and try to find a partitioning of machine cells and part families. The similarity between parts is measured based on commonality of the machines assigned to them. However, parts are assigned to machines based on their operation requirements and the operation capabilities of machines. Similarity between parts should be based on their required operations. In this paper, the authors attempt to solve or facilitate solving the GT problem at the assignment level. An algorithm for assigning parts to machines is provided which utilizes the types of operations required by parts and applies GT principles in producing the assignment. This leads to better partitioning of machine cells and part-families. Furthermore, operation sequences required by parts in determining the similarity between parts have been considered. An algorithm to form part-families based on the operation sequence similarity coefficient has been developed. The resulting families are then used by the assignment algorithm to produce machine assignments to part-families. The use of the algorithm is demonstrated by examples. 相似文献
18.
Thomas L. Landers Melinda K. Beavers Malik Sadiq Don E. Stuart 《Computers & Industrial Engineering》1994,27(1-4):245-248
This paper describes an approach, conceptual framework, and software architecture for dynamic reconfiguration of the order picking system. The research and development project was sponsored by the Material Handling Research Center (MHRC), a National Science Foundation sponsored Cooperative Industry/University Research Center. The storage configuration is assumed to be an in-the-aisle order picking system in which stockkeeping units (SKUs) can occupy variable capacity storage locations and stock-splitting is allowed among zones (clusters). The product mix may include multiple product families with different life cycles, correlated demand within families and commonality of demand across families. 相似文献
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20.
We address the problems of noise and huge data sizes in microarray images. First, we propose a mixture model for describing the statistical and structural properties of microarray images. Then, based on the microarray image model, we present methods for denoising and for compressing microarray images. The denoising method is based on a variant of the translation-invariant wavelet transform. The compression method introduces the notion of approximate contexts (rather than traditional exact contexts) in modeling the symbol probabilities in a microarray image. This inexact context modeling approach is important in dealing with the noisy nature of microarray images. Using the proposed denoising and compression methods, we describe a near-lossless compression scheme suitable for microarray images. Results on both denoising and compression are included, which show the performance of the proposed methods. Further experiments using the results of the proposed near-lossless compression scheme in gene clustering using cell-cycle microarray data for S. cerevisiae showed a general improvement in the clustering performance, when compared with using the original data. This provides an indirect validation of the effectiveness of the proposed denoising method. 相似文献