Health Status Assessment of Diesel Engine Valve Clearance Based on BFA-BOA-VMD Adaptive Noise Reduction and Multi-Channel Information Fusion

Abstract

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.

Research Progress and Development Trend of Prognostics and Health Management Key Technologies for Equipment Diesel Engine

“…A modular approach was used to design the diesel engine embedded machineside control subsystem, the health status online monitoring subsystem, and the health status offline analysis subsystem, and to determine the way of data interaction between the subsystems. Liu et al [120] proposed an adaptive noise reduction and multi-channel information fusion method for diesel engine valve clearance health status assessment. Ke et al [121] investigated the extraction of diesel engine valve clearance degradation features based on the fusion of CEEMDAN and discriminant correlation analysis features, and the fused degradation features were fed into a LS SVM to achieve health status assessment.…”

A Liquid Launch Vehicle Safety Assessment Model Based on Semi-Quantitative Interval Belief Rule Base

“…Therefore, it is necessary to optimize the intervals-BRB model. The objective optimization function of the intervals-BRB model is described as Formula (14). minMSE(a), a = {r, ω, β}…”

“…2 (14) where MSE(•) denotes the mean square error (MSE) function, N is the number of test samples, Ŷn (t) is the actual predicted value for the n th sample, φn (t) represents the expected predicted value of the n th sample and a is the set of parameters in the inference process. Before optimization, it is first necessary to determine the constraints of rule weight ω, rule reliability and belief degree β, which are described as Formulas ( 15)-( 18).…”

“…Zhou et al [13] proposed a onedimensional convolutional neural network to analyze the data in order to evaluate the health state of the aero-engine. Liu et al [14] proposed an evaluation method of multichannel information fusion and adaptive noise reduction for the health condition of diesel valve clearance. Based on the convolutional neural network, Li et al [15] used the data of measurement indicators to evaluate mental health status.…”

Innovative Approach to Dam Deformation Analysis: Integration of VMD, Fractal Theory, and WOA‐DELM

“…Recognizing the complexities in dam deformation data analysis, variational mode decomposition (VMD) emerges as a pivotal method. Its efcacy in processing dam deformation data, retaining and accentuating the trend characteristics, and enhancing the authenticity and stability of information representation is well documented [18][19][20]. Tis approach is notably advanced in the model proposed by Chen et al [21], which integrates VMD with LSTM neural networks.…”