Diesel Engine Fault Diagnosis Method Based on Optimized VMD and Improved CNN

Abstract

The safe operation of diesel engines performs a vital function in industrial production and life. Because diesel engines often work in harsh environmental conditions, they are prone to failure. Therefore, this paper proposes a fault analysis method based on a combination of optimized variational mode decomposition (VMD) and improved convolutional neural networks (CNN) to address the necessary need for preventive maintenance of diesel engines. The authentic vibration sign is first decomposed by using the (VMD) algorithm, then the greatest range of decomposition layers is decided by using scattering entropy and the useful components are preferentially chosen for reconstruction. The continuous wavelet transform (CWT) records preprocessing method is then delivered to radically change the noise-reduced vibration sign into a time-frequency map, which is fed into the CNN for model coaching and extraction of fault features. Finally, fault classification is realized by support vector machine (SVM) with excellent classification performance. Through preset fault experiments on diesel engines, it is established that the technique proposed in this paper can successfully identify fault states, and the classification accuracy is higher than alternative methods.

Fault Identification of Direct-Shift Gearbox Using Variational Mode Decomposition and Convolutional Neural Network

“…It provides superior performance for recognizing different fault states and can be efficiently trained with fewer iterations, making it a promising approach for practical applications in machinery condition monitoring and maintenance. Zhan et al [16] proposed a method that is a combination of optimized VMD, CNN, CWT, and SVM that provides a robust and effective fault analysis method for diesel engines. Xu et al [17] introduced the VMD-DCNNs method, which offers an efficient and effective solution for the fault diagnosis of rolling bearings, addressing the limitations posed by varying industrial environments.…”

A new belief rule base based distributed online fault diagnosis method for multi‐agent systems

“…• To handle the defects of the traditional centralized fault diagnosis method [27,28], where the global information is needed, this paper constructs a distributed fault diagnosis model based on BRB for each agent by using the relative information between an agent and its neighbors to realize fault diagnosis of MASs. • Compared with some general fault diagnosis methods [7,24], this paper proposes a topology switching strategy.…”

“…The main contributions of this paper are as follows: To handle the defects of the traditional centralized fault diagnosis method [27, 28], where the global information is needed, this paper constructs a distributed fault diagnosis model based on BRB for each agent by using the relative information between an agent and its neighbors to realize fault diagnosis of MASs. Compared with some general fault diagnosis methods [7, 24], this paper proposes a topology switching strategy. By adjusting the communication topology to change the neighboring information used for fault diagnosis of agents in real time, the problem of inaccurate fault diagnosis results and excessive model computation caused by the insufficient relative information and the large amount of neighboring agents respectively can be overcome. In addition, compared with the offline fault diagnosis method [29, 30], which uses historical data for fault diagnosis, the fault diagnosis method with topology switching strategy proposed in this paper can perform fault diagnosis in real time when the system is in operation and can improve the accuracy and reduce the computation of the fault diagnosis model in real‐time by adjusting the topology in time. …”

Research on Diesel Engine Fault Status Identification Method Based on Synchro Squeezing S-Transform and Vision Transformer

“…Jiang et al [ 19 ] addresses the problem that diesel engine faults are difficult to identify accurately under complex operating conditions, and the diesel engine vibration signals are fed into a one-dimensional CNN and a deep neural network of a long short-term network (LSTM) for training, which can be effective for status identification. Zhan et al [ 20 ] proposed a fault identification method based on the combination of optimal variational mode decomposition (VMD) and an improved CNN. However, when classifying and recognizing images, the initial status parameters for the CNN can have a great impact on the network training, and a poor choice can cause the network to not work or potentially fall into local minima, underfitting, and overfitting.…”