The evaluation and fault diagnosis of a diesel engine’s health conditions without disassembly are very important for diesel engine safe operation. Currently, the research on fault diagnosis has focused on the time domain or frequency domain processing of vibration signals. However, early fault signals are mostly weak energy signals, and the fault information cannot be completely extracted by time domain and frequency domain analysis. Thus, in this article, a novel fault diagnosis method of diesel engine valve clearance using the improved variational mode decomposition (VMD) and bispectrum algorithm is proposed. First, the experimental study was designed to obtain fault vibration signals. The improved VMD method by choosing the optimal decomposition layers is applied to denoise vibration signals. Then the bispectrum analysis of the reconstructed signal after VMD decomposition is carried out. The results show that bispectrum image under different working conditions exhibits obviously different characteristics respectively. At last, the diagonal projection method proposed in this paper was used to process the bispectrum image, and the fourth order cumulant is calculated. The calculation results show that three states of the valve clearance are successfully distinguished.

Fault diagnosis can improve the safety and reliability of diesel engines. An end-to-end method based on a multi-attention convolutional neural network (MACNN) is proposed for accurate and efficient diesel engine fault diagnosis. By optimizing the arrangement and kernel size of the channel and spatial attention modules, the feature extraction capability is improved, and an improved convolutional block attention module (ICBAM) is obtained. Vibration signal features are acquired using a feature extraction model alternating between the convolutional neural network (CNN) and ICBAM. The feature map is recombined to reconstruct the sequence order information. Next, the self-attention mechanism (SAM) is applied to learn the recombined sequence features directly. A Swish activation function is introduced to solve “Dead ReLU” and improve the accuracy. A dynamic learning rate curve is designed to improve the convergence ability of the model. The diesel engine fault simulation experiment is carried out to simulate three kinds of fault types (abnormal valve clearance, abnormal rail pressure, and insufficient fuel supply), and each kind of fault varies in different degrees. The comparison results show that the accuracy of MACNN on the eight-class fault dataset at different speeds is more than 97%. The testing time of the MACNN is much less than the machine running time (for one work cycle). Therefore, the proposed end-to-end fault diagnosis method has a good application prospect.

Engine fault detection is critical to enhancing the reliability of modern equipment. However, it is challenging to obtain a large number of high-quality labeled data for engines, which is not conducive to improving the training accuracy of deep learning methods. Therefore, this article proposes a fault detection method combining adaptive recursive variational mode decomposition (ARVMD) and component energy distribution spectrum (CEDS). The article first introduces recursive mode into VMD. Then, the mode number is dynamically selected according to the energy distribution of the power spectral density (PSD) to extract the intrinsic mode functions (IMFs) continuously. The quadratic penalty term is optimized correspondingly using SNR. The decomposition results of artificial and real signals demonstrated that ARVMD has higher SNR and efficiency than VMD. Next, the center frequency and unit bandwidth energy of IMF are used to construct CEDS. Fault diagnosis is realized by the CEDS correlation ranking of various faults. Finally, two case studies are performed to illustrate the effectiveness of the proposed method. The results show that the proposed ARVMD-CEDS method provides an efficient and effective solution for single-channel engine fault diagnosis.

As one kind of current main power sources, internal combustion engines require high-reliability to ensure mechanical systems working well in normal operation. This paper studies vibration signals in order to detect multiple types of faults by a single channel signal. First, for the decomposition level of variational mode decomposition (VMD) which needs to be chosen non-automatically, this paper analyzes the features of various faults and optimizes the iteration initial values of center frequency so as to reduce the adverse effect of inappropriate decomposition level. Moreover, considering that VMD cannot decompose different signal sources in the same frequency, robust independent component analysis (ICA) is an excellent method to overcome this challenge. Then, the fourth-order cumulant of restructured signals from VMD and robust ICA is taken as fault indexes. Finally, because of the high error rate of original fuzzy C-Mean clustering, Euclidean distance between test points and cluster center of fuel injection failure is taken as a fault detection threshold in order to achieve high-recognition rate. In conclusion, through optimizing several algorithms, the purpose of detecting multiple types of faults by single channel signal is achieved in the current research.

Transfer learning (TL) is a powerful approach that enhances the generalizability of cross-domain fault diagnosis. However, the challenge of acquiring high-quality mechanical fault signals limits its application. This article introduces the extreme class imbalance problem in the cross-domain diagnosis, restricting the label space of the target domain while relaxing the restrictions of unsupervised learning. The study proposes a novel generative TL method called fast sparse neural style, which employs sparse representation to capture the domain-invariant fault features as well as the Gram matrix to measure the domain-specific features. Fault features and domain features are proven to be separable in mechanical signals and are fused in the data generation process. Compared to other methods through various cross-domain diagnostic tasks on a piston aero-engine, the proposed method has obvious advantages in tasks with substantial inter-domain differences, demonstrating the potential and research value of generative TL.