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Engine ignition patterns can be analyzed to identify the engine fault according to both the specific prior domain knowledge and the shape features of the patterns. One of the challenges in ignition system diagnosis is that more than one fault may appear at a time. This kind of problem refers to simultaneous-fault diagnosis. Another challenge is the acquisition of a large amount of costly simultaneous-fault ignition patterns for constructing the diagnostic system because the number of the training patterns depends on the combination of different single faults. The above problems could be resolved by the proposed framework combining feature extraction, probabilistic classification, and decision threshold optimization. With the proposed framework, the features of the single faults in a simultaneous-fault pattern are extracted and then detected using a new probabilistic classifier, namely, pairwise coupling relevance vector machine, which is trained with single-fault patterns only. Therefore, the training dataset of simultaneous-fault patterns is not necessary. Experimental results show that the proposed framework performs well for both single-fault and simultaneous-fault diagnoses and is superior to the existing approach.

Many time series problems such as air pollution index forecast require online sequential learning rather than batch learning. One of the major obstacles for air pollution index forecast is the data imbalance problem so that forecast model biases to the majority class. This paper proposes a new method called meta-cognitive online sequential extreme learning machine (MCOS-ELM) that aims to alleviate data imbalance problem and sequential learning at the same time. Under a real application of air pollution index forecast, the proposed MCOS-ELM was compared with retrained ELM and online sequential extreme learning machine in terms of accuracy and computational time. Experimental results show that MCOS-ELM has the highest efficiency and best accuracy for predicting the minority class (i.e., the most important but with fewest training samples) of air pollution level.

The dynamic optimization of polymer binder burnout processes was evaluated for a 3D cubic ceramic body in different sample porosities and atmospheres. Optimal heating trajectories of the binder removal processes to minimize the burnout time were estimated by the proposed algorithm. The process model can be constructed by the chemical kinetics of the polymer burnout and the mass transport of the volatile gas evolved from polymer burnout inside the ceramic body. A numerical simulation was used to calculate the buildup pressure distribution formed by the volatile gas which affects the generation of the ceramic defects. The results show that the maximum pressure was found at the body center through the binder burnout period. The process needs to be well-controlled to avoid the formation of a large buildup pressure, especially for the samples with small porosities, during the initial binder burnout stages. In addition, the effects of the operating atmospheres and the sample porosities on the optimal heating trajectories were discussed.