Abstract
For a diesel engine, operating conditions have extreme importance in fault detection and diagnosis. Limited to various special circumstances, the multi-factor operating conditions of a diesel engine are difficult to measure, and the demand of automatic condition recognition based on vibration signals is urgent. In this paper, multi-factor operating condition recognition using a one-dimensional (1D) convolutional long short-term network (1D-CLSTM) is proposed. Firstly, a deep neural network framework is proposed based on a 1D convolutional neural network (CNN) and long short-Term network (LSTM). According to the characteristics of vibration signals of a diesel engine, batch normalization is introduced to regulate the input of each convolutional layer by fixing the mean value and variance. Subsequently, adaptive dropout is proposed to improve the model sparsity and prevent overfitting in model training. Moreover, the vibration signals measured under 12 operating conditions were used to verify the performance of the trained 1D-CLSTM classifier. Lastly, the vibration signals measured from another kind of diesel engine were applied to verify the generalizability of the proposed approach. Experimental results show that the proposed method is an effective approach for multi-factor operating condition recognition. In addition, the adaptive dropout can achieve better training performance than the constant dropout ratio. Compared with some state-of-the-art methods, the trained 1D-CLSTM classifier can predict new data with higher generalization accuracy.
Highlights
A diesel engine is a kind of internal combustion engine that converts thermal energy into mechanical energy
The results of the proposed approach for multi‐factor operating to other classification algorithms to verify that the designed 1D-CLSTM
An effective approach was proposed for multi-factor operating condition recognition using a 1D convolutional long short-term network
Summary
A diesel engine is a kind of internal combustion engine that converts thermal energy into mechanical energy. It plays an important role in the field of national defense, in the chemical industry, in the marine industry, for nuclear power, and so on. The detection of faults and the diagnosis of diesel engines [3] are not simple tasks due to the complex structure and fickle working conditions. With the information of operating conditions, the engineering applicability of a fault detection and diagnosis method [6,7,8] can be improved to avoid fatal performance degradation and huge economic losses at an early stage. Most fault detection methods are carried out under stable operating condition to avoiding variable
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