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Dual-injection engines, which allow real-time control and injection of two different fuels, are capable of varying the ratio of biofuel blends at different engine operating conditions for optimal engine performance. However, while many experiments have been carried out on these engines to demonstrate their advantages, very few studies have focused on the corresponding air–fuel ratio (AFR) control strategy. In order to achieve stable engine operation, it is essential to maintain transient AFR during the change of fuel blend ratio. Therefore, this study proposes an adaptive controller for AFR control of dual-injection engines. The proposed controller is designed based on a recently developed machine learning method called extreme learning machine, and its stability is verified with Lyapunov analysis. Simulations have been performed on an industry-level engine simulation software to verify the controller. Since dual-injection engines are not available in the market, a spark-ignition engine has been retrofitted for dual-injection operation so that the proposed controller can be implemented and evaluated experimentally. Both simulation and experiment results show that the proposed controller can effectively regulate the AFR to desired level. The results also show that the proposed controller outperforms the engine built-in AFR controller, indicating its significance for dual-injection engines. [ABSTRACT FROM AUTHOR]

Abstract: Real-time fault diagnostic system is very important to maintain the operation of the gas turbine generator system (GTGS) in power plants, where any abnormal situation will interrupt the electricity supply. The GTGS is complicated and has many types of component faults. To prevent from interruption of electricity supply, a reliable and quick response framework for real-time fault diagnosis of the GTGS is necessary. As the architecture and the learning algorithm of extreme learning machine (ELM) are simple and effective respectively, ELM can identify faults quickly and precisely as compared with traditional identification techniques such as support vector machines (SVM). This paper therefore proposes a new application of ELM for building a real-time fault diagnostic system in which data pre-processing techniques are integrated. In terms of data pre-processing, wavelet packet transform and time-domain statistical features are proposed for extraction of vibration signal features. Kernel principal component analysis is then applied to further reduce the redundant features in order to shorten the fault identification time and improve accuracy. To evaluate the system performance, a comparison between ELM and the prevailing SVM on the fault detection was conducted. Experimental results show that the proposed diagnostic framework can detect component faults much faster than SVM, while ELM is competitive with SVM in accuracy. This paper is also the first in the literature that explores the superiority of the fault identification time of ELM. [Copyright &y& Elsevier]

Biofuels are important for the reduction of engine exhaust emissions and fossil fuel consumption. To use different blends of biofuels, the electronic control unit (ECU) of the engine must be modified and calibrated. However, the calibration process of ECU is very costly and time-consuming. Therefore, most of the engines can only use one specific biofuel blend; otherwise the engines cannot run properly. To alleviate this problem, a mathematical engine model can be used for predicting the engine performance at different ECU settings and biofuel blends so that the ECU can be re-calibrated in real-time via some controllers. The prediction of the engine model must be very fast and accurate for such online control purpose. It must also be very compact due to the limited memory size of the ECU. As a result, a new method called sparse Bayesian extreme learning machine (SBELM) is proposed in this paper to fulfill these requirements of the mathematical engine model for fast engine performance prediction and ECU online re-calibration. Experiments were conducted to compare SBELM with conventional ELM, Bayesian ELM (BELM) and back-propagated neural network (BPNN). Evaluation results show that SBELM can perform at least similar to, but mostly better than, ELM, BELM and BPNN, in terms of prediction accuracy. In terms of execution time, model size, and insensitivity to hidden neuron number, SBELM completely outperforms the other three methods. By these results, SBELM is verified to better fulfill the practical requirements of mathematical engine model for online engine performance prediction. [ABSTRACT FROM AUTHOR]