Intelligent Diagnosis Method of Diesel Engine based on Fast Online Support Tensor Machine

doi:10.3901/JME.2018.22.030

Abstract

Abstract: It is difficult to establish accurate mathematical models based on vector mode to describe the state of diesel engine due to the non-linear and complex coupling of the monitoring signal sources. Therefore, an intelligent diagnosis method of diesel engine under tensor mode is proposed. First, a fast online support tensor algorithm with the kernel function is developed, which combined the linear support high-order tensor learning framework and the method of online random gradient descent. Second, the diesel engine state samples in the form of third order tensor, "signal type×crank angle×rotate speed", are constructed based on the signal acquired from a diesel engine under different working state. By applying the three algorithms of on-line support vector machine, linear support high order tensor, and fast online support tensor, the diesel engine fire failure samples are analysis to predict whether diesel engine is fire or not. Third, the diagnosed results from the three algorithms are evaluated using the three indicators of test precision, learning time, and storage space. It is found that the fast online support tensor algorithm meets the engineering requirements of the intelligent fault diagnosis for the diesel engine, and it has a higher testing precision, lower learning time and small storage space than the others. This method can solve the classification problem, which involves of super large samples with nonlinear and high dimensions characteristics.

Key words: diesel engine, fast online support tensor machine, intelligent diagnosis, tensor mode

CLC Number:

TG156

XU Xiaowei, ZHANG Nan, YAN Yunbing, QIN Li. Intelligent Diagnosis Method of Diesel Engine based on Fast Online Support Tensor Machine[J]. Journal of Mechanical Engineering, 2018, 54(22): 30-37.

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