To improve the performance of vehicle suspension, this paper proposes a semi-active vehicle suspension with a magnetorheological fluid (MRF) damper. We designed an optimized fuzzy skyhook controller with grey wolf optimizer (GWO) algorithm base on a new neuro-inverse model of the MRF damper. Because the inverse model of the MRF damper is difficult to establish directly, the Elman neural network was applied. The novelty of this study is the application of the new inverse model for semi-active vibration control and optimization of the semi-active suspension control method. The calculation results showed that the new inverse model can accurately calculate the required control current. The fuzzy skyhook control method optimized by the grey wolf optimizer (GWO) algorithm was established based on the inverse model to control the suspension vibration. The simulation results showed that the optimized fuzzy skyhook control method can simultaneously reduce the amplitude of vertical acceleration, suspension deflection, and tire dynamic load.

An echo state network with improved topology (IESN) is proposed for accurate and efficient time series prediction. In this approach, a tighter bound of the echo state property related to the Lipshitz constant of reservoir activation function and the maximum structured singular value of reservoir is firstly researched to run the model at the edge of chaos. A smooth composite reservoir activation function is then designed to enhance the ESN. The exact echo state property bound is solved by computing the Lipshitz constant of the composite function. Finally, a decoupling matrix with eigenvalues distributing uniformly in the complex plane is built as the reservoir for abundant dynamic characteristics. Six classical benchmarks are employed to test the IESN. Besides, combined with amplitude-frequency separation based on the Hilbert transform, the IESN predicts a set of engine vibration signals in knock. Compared with several popular models, the proposed IESN shows the best performance.

This review focuses on studying passive vibration isolation techniques and their applications. Advantages and disadvantages of different vibration isolation techniques will be compared in order to find an innovative method to reduce seating suspension system vibration and improve the ride comfort of vehicles’ drivers. Quasi-zero stiffness (QZS) isolators of high-static–low-dynamic stiffness are found to have good application prospects in low-frequency vibration isolation. In order to improve the isolation performance of the seating suspension system, a specially made seat cushion will be studied where a quasi-zero stiffness (QZS) isolator such as a double-diamond isolator is selected as one of the cells/units/elements of the seat cushion mattress. The double-diamond isolator structure does not represent the whole seating suspension system. The transmissibility ratio of the double-diamond isolator is evaluated by analytical and simulation models and compared to that of a conventional linear spring isolator. Although the performance of the passive vibration isolation/control system is usually worse than that of the active or semi-active control system, the cost of the passive vibration isolation/control system is lower than that of the active and semi-active control system, and the relative simplicity of adding a vibration isolation cushion mattress in an existing passive seating suspension system makes it low in cost, easy to implement, and more attractive than the active and semi-active vibration control systems.

Accurate and timely fault diagnosis for the diesel engine is crucial to guarantee it works safely and reliably, and reduces the maintenance costs. A novel diagnosis method based on variational mode decomposition (VMD) and kernel-based fuzzy c-means clustering (KFCM) is proposed in this paper. Firstly, the VMD algorithm is optimized to select the most suitable K value adaptively. Then KFCM is employed to classify the feature parameters of intrinsic mode functions (IMFs). Through the comparison of many different parameters, the singular value is selected finally because of the good classification effect. In this paper, the diesel engine fault simulation experiment was carried out to simulate various faults including valve clearance fault, fuel supply fault and common rail pressure fault. Each kind of machine fault varies in different degrees. To prove the effectiveness of VMD-KFCM, the proposed method is compared with empirical mode decomposition (EMD)-KFCM, ensemble empirical mode decomposition (EEMD)-KFCM, VMD-back propagation neural network (BPNN), and VMD-deep belief network (DBN). Results show that VMD-KFCM has advantages in accuracy, simplicity, and efficiency. Therefore, the method proposed in this paper can be used for diesel engine fault diagnosis, and has good application prospects.

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.