Abstract
Vibration signal, as an important means for diesel engine condition detection and fault diagnosis, has attracted attention for many years. In traditional vibration signal analysis, most processing methods are for single-channel data. However, single-channel vibration signal cannot reflect the operating information of the diesel engine comprehensively because diesel engine vibration is coupled by multiple source signals. This paper proposes the MVMD band energy method for fault diagnosis by four channels of vibration signals. First, the original multivariate signals are decomposed adaptively by MVMD, which obtains a series of components with modal alignment. Then, the band energy values of each measuring point are calculated as the fault characteristics. Finally, SVM is used to realize the diagnosis and identification of diesel engine misfire. The working conditions have a great influence on the vibration signal of the cylinder. In order to obtain the best diagnostic working conditions, six working conditions are set for testing. The result shows that the fault identification rate is highest under the 1500 rpm and 50% load working condition. The fault recognition rate of this method reaches more than 99%, which is superior to the other four common methods.
Highlights
As the main power source of existing armored vehicles, the efficient and stable operation of diesel engines is an important guarantee for vehicle mobility
Total that the method proposed in this paper shows a high accuracy rate for fire faults under six operating conditions, of which the accuracy rate is the highest under the 1500 rpm and 50% load operating condition. e vibration signals generated by a diesel engine at low speed and low load are small, which causes its band energy characteristics to be overwhelmed by noise
Multivariate Variational Mode Decomposition is introduced into the field of mechanical fault diagnosis, and a novel fault diagnosis method for diesel engine based on MVMD and band energy is presented. e simulation test and bench test were used to study the MVMD adaptive decomposition diesel engine multivariate vibration signals, and the following conclusions were obtained: (1) Characteristic frequency in multicomponent modulation signals can be extracted by MVMD effectively
Summary
As the main power source of existing armored vehicles, the efficient and stable operation of diesel engines is an important guarantee for vehicle mobility. In order to solve the shortcomings of traditional signal processing methods, Huang proposed a signal adaptive decomposition method based on the characteristics of the signal itself-empirical mode decomposition (EMD), which can analyze multicomponent coupled signals with nonstationary and nonlinear characteristics [6]. EMD can perform adaptive signal decomposition unsupervised, it has many problems such as endpoint effects and modal aliasing, underdecomposition, and overdecomposition. On one hand, aiming at the endpoint effects and modal aliasing shortcomings of EMD, Wu proposed ensemble empirical mode decomposition (EEMD) by adding white noise to the original signal [8], which suppressed the modal aliasing problem to some extent. On the other hand, aiming at the underdecomposition and overdecomposition problems of EMD, Smith proposed a new iterative method for demodulating amplitude and frequency modulation signals, namely, local mean decomposition (LMD) [10], but it has endpoint effects and modal aliasing. 15 types of misfires are diagnosed and identified by MVMD and band energy method. e organization of this paper is arranged as follows: Section 1 introduces the research background and significance; Section 2 studies the MVMD algorithm theory; Section 3 analyzes the simulation signals and compares the advantages and disadvantages of the three methods (MVMD, MEMD, and NAMEMD); Section 4 uses test signals to identify the type of misfire; Section 5 gives conclusions and necessary discusses
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