Search Results (8)

The valve, armature, and armature pin are critical factors influencing the hydraulic pressure differences in diesel injectors, and are essential for injection and backflow quantity control. These components play crucial roles in enhancing energy efficiency and reducing engine emissions. This experimental study investigated the effects of clearance between the valve, armature, and armature pin guidance. Forty-nine 2000 bar common-rail injectors (Bosch) were tested in calibrated stations. Injection quantities were assessed at both minimum and maximum operational pressures. Backflow rates were specifically examined at maximum pressure. A correlation matrix was created using Python to analyze the relationship between inputs and outputs, identifying dominant characteristics that define injector behavior. Increased injector precision correlated with reduced fuel consumption and enhanced energy efficiency. The study found that the effect of clearance between the armature and armature pins was more significant than that between the valve and armature. Injection quantities were observed to increase with pressure, and no critical difference in injection quantities was noted among different diameter groups at the minimum pressure point. Backflow quantities were consistent within groups when the armature–armature pin and valve–armature clearances were minimized. Full article

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. Full article

In order to improve the accuracy of engine valve clearance fault diagnosis, in this study, a fault identification algorithm based on wavelet packet decomposition and an artificial neural network is proposed. Firstly, the vibration signals of the engine cylinder head were collected, and different levels of noise were superimposed on the extended data sets. Then, the test data were decomposed into wavelet packets, and the power spectrum of the sub-band signal was analyzed using the autoregressive power spectrum density estimation method. A group of values were obtained from the power spectrum integration to form the fault eigenvalue. Finally, a neural network model was designed to classify the fault eigenvalues. In the training process, the test data set was divided into three parts, the training set, the verification set, and the test set, and the dropout layer was added to avoid the overfitting phenomenon of the neural network. The experimental results show that the wavelet packet neural network model in this paper has a good diagnostic accuracy for data with different levels of noise. Full article

Regarding the problem of the valve gap health status being difficult to assess due to the complex composition of the condition monitoring signal during the operation of the diesel engine, this paper proposes an adaptive noise reduction and multi-channel information fusion method for the health status assessment of diesel engine valve clearance. For the problem of missing fault information of single-channel sensors in condition monitoring, we built a diesel engine valve clearance preset simulation test bench and constructed a multi-sensor acquisition system to realize the acquisition of diesel engine multi-dimensional cylinder head signals. At the same time, for the problem of poor adaptability of most signal analysis methods, the improved butterfly optimization algorithm by the bacterial foraging algorithm was adopted to adaptively optimize the key parameter for variational mode decomposition, with discrete entropy as the fitness value. Then, to reduce the uncertainty of artificially selecting fault characteristics, the characteristic parameters with a higher recognition degree of diesel engine signal were selected through characteristic sensitivity analysis. To achieve an effective dimensionality reduction integration of multi-channel features, a stacked sparse autoencoder was used to achieve deep fusion of the multi-dimensional feature values. Finally, the feature samples were entered into the constructed one-dimensional convolutional neural network with a four-layer parameter space for training to realize the health status assessment of the diesel engine. In addition, we verified the effectiveness of the method by carrying out valve degradation simulation experiments on the diesel engine test bench. Experimental results show that, compared with other common evaluation methods, the method used in this paper has a better health state evaluation effect. Full article

Diesel engines, as power equipment, are widely used in the fields of the automobile industry, ship industry, and power equipment. Due to wear or faulty adjustment, the valve train clearance abnormal fault is a typical failure of diesel engines, which may result in the performance degradation, even valve fracture and cylinder hit fault. However, the failure mechanism features mainly in the time domain and angular domain, on which the current diagnosis methods are based, are easily affected by working conditions or are hard to extract accurate enough from, as the diesel engine keeps running in transient and non-stationary processes. This work aimed at diagnosing this fault mainly based on frequency band features, which would change when the valve clearance fault occurs. For the purpose of extracting a series of frequency band features adaptively, a decomposition technique based on improved variational mode decomposition was investigated in this work. As the connection between the features and the fault was fuzzy, the random forest algorithm was used to analyze the correspondence between features and faults. In addition, the feature dimension was reduced to improve the operation efficiency according to importance score. The experimental results under variable speed condition showed that the method based on variational mode decomposition and random forest was capable to detect the valve clearance fault effectively. Full article

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. Full article

The diesel engine has been a significant component of large-scale mechanical systems for the intelligent manufacturing industry. Because of its complex structure and poor working environment, it has trouble effectively acquiring the representative fault features. Further, fault diagnosis of the diesel engine faces great challenges. This paper presents a new fault diagnosis method for the detection of diesel engine faults under multiple operation conditions instead of conventional methods confined to a single condition. First, an adaptive correlation threshold process is designed as a preprocessing unit to enhance data quality by weakening non-impact region characteristics. Next, a feature extraction method for sound signals based on the Mel frequency cepstrum (MFC) is improved and introduced into the machinery fault diagnosis. Then, the combination of the improved feature and vibrational mode decomposition (VMD) is proposed to incorporate VMD into an effective adaptive decomposition of non-stationary signals to combine it with an excellent feature representation of the vibration signal. Finally, the vector quantization algorithm is adopted to reduce the feature dimensions and generate codebook model bases, which trains the K-Nearest Neighbor classifiers. Five comparative methods were carried out, and the experimental results show that the proposed method offers a good effect of the common valve clearance fault of diesel engines under different conditions. Full article

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. Full article