In the field of electric motor maintenance, this study introduces a transformative approach by integrating entropy-based algorithms with machine learning for enhanced multi-class fault detection. Employing Shannon, Re...
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In the field of electric motor maintenance, this study introduces a transformative approach by integrating entropy-based algorithms with machine learning for enhanced multi-class fault detection. Employing Shannon, Renyi, and Tsallis entropyalgorithms on standard fault detection measurements, the research significantly advances predictive maintenance strategies through a robust, early-indication, system-agnostic analysis. Detailed examination is conducted, comparing results derived from datasets that include statistical features (excluding entropy) against the proposed entropy-based datasets, when applied to a multi-layer perceptron classifier (MLPC). Optimization of the MLPC and all compared algorithms' hyperparameters is done using the state-of-the-art Optuna tool to dynamically explore each search space, ensuring that each methodology performs adequately in a timely fashion while allowing for adaptation. The results showcase significant enhancement in classification accuracy of diverse electric motor operational states, facilitating the differentiation between healthy and various levels of fault conditions under assorted load scenarios. Computational analyses reveal favorable results related to execution time and memory overhead, thereby supporting the practicality in operations constrained by memory resources. Validation of the approach is achieved through laboratory experiments on a purpose-built test bench. Versatility of entropy-based measures through their proposed utilization in diverse fault indications is achieved by a demonstration in the field of mechanical fault detection with a focus on bearing faults through well-respected datasets.
Fuzzy clustering is useful to mine complex and multi-dimensional data sets, where the members have partial or fuzzy relations. Among the various developed techniques, fuzzy-C-means (FCM) algorithm is the most popular ...
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Fuzzy clustering is useful to mine complex and multi-dimensional data sets, where the members have partial or fuzzy relations. Among the various developed techniques, fuzzy-C-means (FCM) algorithm is the most popular one, where a piece of data has partial membership with each of the pre-defined cluster centers. Moreover, in FCM, the cluster centers are virtual, that is, they are chosen at random and thus might be out of the data set. The cluster centers and membership values of the data points with them are updated through some iterations. On the other hand, entropy-based fuzzy clustering (EFC) algorithm works based on a similarity-threshold value. Contrary to FCM, in EFC, the cluster centers are real, that is, they are chosen from the data points. In the present paper, the performances of these algorithms have been compared on four data sets, such as IRIS, WINES, OLITOS and psychosis (collected with the help of forty doctors), in terms of the quality of the clusters (that is, discrepancy factor, compactness, distinctness) obtained and their computational time. Moreover, the best set of clusters has been mapped into 2-D for visualization using a self-organizing map (SOM).
Wireless sensor networks (WSNs) in recent years shown abrupt growth in technological applications. The main research goals of WSN in the area of heterogeneity are to achieve various matrix performances such as high en...
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ISBN:
(纸本)9788132222057;9788132222040
Wireless sensor networks (WSNs) in recent years shown abrupt growth in technological applications. The main research goals of WSN in the area of heterogeneity are to achieve various matrix performances such as high energy efficiency, lifetime and packet delivery nodes. Most proposed clustering algorithms do not consider the situation causes hot spot problems in multi-hop WSNs. To achieve such network the two soft computing techniques applied to energy efficient clustered heterogeneous sensor node network. In this paper proposed the implementation of the real time energy efficient clustering using a Genetic Dual Fuzzy entropy Clustering (GDFEC) algorithm. Various matrixes of simulation carried out using MATLAB to study the performance under setup conditions. This creates a standardized power distribution among disseminated cluster nodes in the heterogeneous network. The protocol realization carried out on software simulation by different empirical test. The empirical analysis of GDFEC protocol compared with different traditional protocol to evaluate the level of resultant matrix. The protocol evaluation studies have shown that GDFEC protocol able to improve the network performance matrix under the heterogeneous distribution of network nodes.
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