Diesel engine injection faults' detection and classification utilizing unsupervised fuzzy clustering techniques

By using the unsupervised fuzzy clustering, this study attempts to design a new scheme for the unsupervised detection and classification of two injection faults using the time–frequency analysis of vibration signals of an internal combustion, four-stroke, diesel engine with six cylinders in-line. To reach this objective, two new methods called modified S-transform and two-dimensional non-negative matrix factorization are used. Three fuzzy clustering algorithms and nine cluster validity indices, for a variable number of classes, are also used to detect and classify the fault classes. The implementation of these methods resulted in a high detection rate of the injection faults.

Enhancement of ELM by Clustering Discrimination Manifold Regularization and Multiobjective FOA for Semisupervised Classification

A novel semisupervised extreme learning machine (ELM) with clustering discrimination manifold regularization (CDMR) framework named CDMR-ELM is proposed for semisupervised classification. By using unsupervised fuzzy clustering method, CDMR framework integrates clustering discrimination of both labeled and unlabeled data with twinning constraints regularization. Aiming at further improving the classification accuracy and efficiency, a new multiobjective fruit fly optimization algorithm (MOFOA) is developed to optimize crucial parameters of CDME-ELM. The proposed MOFOA is implemented with two objectives: simultaneously minimizing the number of hidden nodes and mean square error (MSE). The results of experiments on actual datasets show that the proposed semisupervised classifier can obtain better accuracy and efficiency with relatively few hidden nodes compared with other state-of-the-art classifiers.