Junhong Zhang

A fault diagnosis approach for diesel engines based on self-adaptive WVD, improved FCBF and PECOC-RVM

Targeting the cross-term interference of Wigner-Ville distribution (WVD) and the redundancy control problem of fast correlation-based filter (FCBF), a novel approach based on self-adaptive WVD, improved FCBF and relevance vector machine (RVM) is proposed for the identification of diesel engine fault in this study. The approach primarily consists of three stages. Firstly, the self-adaptive WVD method is used to generate the time-frequency images of the vibration signals of the diesel engine. Secondly, four types of commonly used image features, including moment invariants, gray statistical characteristics, textural features and the differential box-counting fractal dimension, are calculated for all of the time-frequency images. Next, the improved FCBF method is proposed and used to select the relevant but non-redundant fault features. Finally, the probability-based error correcting output codes (PECOC) method is used to determine the structure of the multi-RVM model, and the fault diagnosis results can be obtained by inputting the selected fault features into the PECOC-RVM classifier. The simulation and experimental results demonstrate that the proposed fault diagnosis approach can overcome the cross-term interference of WVD, effectively extract the relevant but non-redundant fault features and accurately identify the fault types of diesel engines.

Aero-engine Blade Fatigue Analysis Based on Nonlinear Continuum Damage Model Using Neural Networks

Fatigue life and reliability of aero-engine blade are always of important significance to flight safety. The establishment of damage model is one of the key factors in blade fatigue research. Conventional linear Miner’s sum method is not suitable for aero-engine because of its low accuracy. A back propagation neutral network (BPNN) based on the combination of Levenberg-Marquardt (LM) and finite element method (FEM) is used to describe process of nonlinear damage accumulation behavior in material and predict fatigue life of the blade. Fatigue tests of standard specimen made from TC4 are carried out to obtain material fatigue parameters and S-N curve. A nonlinear continuum damage model (CDM), based on the BPNN with one hidden layer and ten neurons, is built to investigate the nonlinear damage accumulation behavior, in which the results from the tests are used as training set. Comparing with linear models and previous nonlinear models, BPNN has the lowest calculation error in full load range. It has significant accuracy when the load is below 500 MPa. Especially, when the load is 350 MPa, the calculation error of the BPNN is only 0.4%. The accurate model of the blade is built by using 3D coordinate measurement technology. The loading cycle in fatigue analysis is defined from takeoff to cruise in 10 min, and the load history is obtained from finite element analysis (FEA). Then the fatigue life of the compressor blade is predicted by using the BPNN model. The final fatigue life of the aero-engine blade is 6.55×104 cycles (10 916 h) based on the BPNN model, which is effective for the virtual design of aero-engine blade.

Source Separation of Diesel Engine Vibration Based on the Empirical Mode Decomposition and Independent Component Analysis

Vibration signals from diesel engine contain many different components mainly caused by combustion and mechanism operations, several blind source separation techniques are available for decomposing the signal into its components in the case of multichannel measurements, such as independent component analysis (ICA). However, the source separation of vibration signal from single-channel is impossible. In order to study the source separation from single-channel signal for the purpose of source extraction, the combination method of empirical mode decomposition (EMD) and ICA is proposed in diesel engine signal processing. The performance of the described methods of EMD-wavelet and EMD-ICA in vibration signal application is compared, and the results show that EMD-ICA method outperforms the other, and overcomes the drawback of ICA in the case of single-channel measurement. The independent source signal components can be separated and identified effectively from one-channel measurement by EMD-ICA. Hence, EMD-ICA improves the extraction and identification abilities of source signals from diesel engine vibration measurements.

Tribilogical performances of connecting rod and by using orthogonal experiment, regression method and response surface methodology

Different from tradition analysis method, the statistics method with suitable design of experiment is used to gain more information.The identification of the factors dominating the bearing behaviors is obtained.The new regression models without insignificant components are established through the stepwise regression.The SVM model and POS-SVM model are established to identify the asperity contact. Dynamic lubrication analysis of connecting rod is a very complex problem. Some factors have great effect on lubrication, such as clearance, oil viscosity, oil supplying hole, bearing elastic modulus, surface roughness, oil supplying pressure and engine speed and bearing width. In this paper, ten indexes are used as the input parameters to evaluate the bearing performances: minimum oil film thickness (MOFT), friction loss, the maximum oil film pressure (MOFP) and average of the oil leakages (OLK). Two orthogonal experiments are combined to identify the factors dominating the bearing behavior. The stepwise regression is used to establish the regression model without insignificant variables, and two most important variables are used as the input to carry out the surface response analysis for each model. At last, the support vector machine (SVM) is used to identify the asperity contact. Compared with SVM model, the particle swarm optimization-support vector machines (PSO-SVM) can predict the asperity contact more precise, especially to the samples near dividing line. In future work, more soft computing methods with statistical characteristic are used to the tribology analyses.

Diesel engine noise source identification based on EEMD, coherent power spectrum analysis and improved AHP

As the essential foundation of noise reduction, many noise source identification methods have been developed and applied to engineering practice. To identify the noise source in the board-band frequency of different engine parts at various typical speeds, this paper presents an integrated noise source identification method based on the ensemble empirical mode decomposition (EEMD), the coherent power spectrum analysis, and the improved analytic hierarchy process (AHP). The measured noise is decomposed into several IMFs with physical meaning, which ensures the coherence analysis of the IMFs and the vibration signals are meaningful. An improved AHP is developed by introducing an objective weighting function to replace the traditional subjective evaluation, which makes the results no longer dependent on the subject performances and provides a better consistency in the meantime. The proposed noise identification model is applied to identifying a diesel engine surface radiated noise. As a result, the frequency-dependent contributions of different engine parts to different test points at different speeds are obtained, and an overall weight order is obtained as oil pan > left body > valve chamber cover > gear chamber casing > right body > flywheel housing, which provides an effectual guidance for the noise reduction.

Prediction and control of the structure-borne noise of a vehicle based on an optimization technique of the vehicle-body model:

A novel optimization approach is proposed for the development of the body-in-white model of a hatchback car based on the effective independence–weighted average acceleration amplitude method and the modal assurance criterion. By employing this optimization method, not only are the modal frequency and the mode shape identified more accurately, but also the experimental efficiency is significantly improved. According to the level of concern of the vibration mode, weighted factors are given to the modal features in order to make the sensor arrangement more efficient and reasonable. The target parameters are obtained by a sensitivity analysis of the design parameters of the hatchback car and an optimization is carried out. An acoustic–structural coupled model is developed on the basis of the optimized body-in-white model and used for prediction of the interior structure-borne noise of the hatchback car. The predicted results of the sound pressure level show good agreement with the experimental data, particularly at the frequencies concerned. By using the multi-position multi-frequency method, the mean acoustic contribution coefficient is proposed, and the panels with significant influence on the interior noise are identified. Accordingly, some modifications of the car body are made by using damping material. As a result, the interior noise decreases totally in the range from 10 Hz to 60 Hz. Some significant reductions are obtained at 23.34 Hz and 46.68 Hz, namely 4.1 dB(A) and 2.5 dB(A) at the position of the driver’s right ear and 4.6 dB(A) and 3.5 dB(A) at the middle of the rear seat.

Fatigue crack growth rate of Ti-6Al-4V considering the effects of fracture toughness and crack closure

Fatigue fracture is one of the main failure modes of Ti-6Al-4V alloy, fracture toughness and crack closure have strong effects on the fatigue crack growth(FCG) rate of Ti-6Al-4V alloy. The FCG rate of Ti-6Al-4V is investigated by using experimental and analytical methods. The effects of stress ratio, crack closure and fracture toughness on the FCG rate are studied and discussed. A modified prediction model of the FCG rate is proposed, and the relationship between the fracture toughness and the stress intensity factor(SIF) range is redefined by introducing a correcting coefficient. Notched plate fatigue tests (including the fracture toughness test and the FCG rate test) are conducted to investigate the influence of affecting factors on the FCG rate. Comparisons between the predicted results of the proposed model, the Paris model, the Walker model, the Sadananda model, and the experimental data show that the proposed model gives the best agreement with the test data particularly in the near-threshold region and the Paris region, and the corresponding calculated fatigue life is also accurate in the same regions. By considering the effects of fracture toughness and crack closure, the novel FCG rate prediction model not only improves the estimating accuracy, but also extends the adaptability of the FCG rate prediction model in engineering.

A Concurrent Reynolds BC Algorithm for Piston Ring Cavitation Lubrication Problems with Surface Roughness

Piston ring dynamics play an important role in the lubricant characteristics of reciprocating engines that lead to engine wear and high consumption of lubricating oil. Due to the complexity of realistic test and working conditions, a study of cavitation with surface roughness and its effect on piston ring lubrication was conducted in a simulation program based on mass-conserving theory that is solved with the Newton-Raphson method. Lubrication models such as mixed lubrication, asperity contact, blow-by/blow-back flow, and cavitation were used in this study. The simulation algorithm consists of four processes: establishment of the three different lubrication models, dealing with the finite difference method, numerical stability treatment for the cavitation zone, and the addition of surface roughness and the shear factor to the model. Results calculated with this model were compared with two other models, and an analysis of the results indicates that the developed simulation program can illustrate problems of piston ring lubrication in accordance with the state of art of lubrication theory.

Analysis of hydrodynamic loads on performance characteristics of engine main bearings

This article aims to present a numerical study of the behavior of engine main bearings under dynamic load. The coupling performance characteristics of oil film between crankshaft and engine block are analyzed. The film pressure is solved numerically with Reynolds boundary conditions when various bearing characteristics are calculated. Hydrodynamic lubrication is analyzed with finite difference method for solving the Reynolds equation for thermo effect. The other models, such as engine block and crankshaft-connecting rod system, are processed by utilizing the finite element method. Above this system, model applied in this paper is simulated by employing multi-body dynamic method to capture the dynamic characteristics of engine dynamic simulation. Through the methods mentioned above, the performance characteristics of engine main bearings under hydrodynamic loads in a six-cylinder in-line diesel engine were received. And the orbit movement, minimum oil film thickness, and power loss in the bearing were estimated over the wide range of engine operation.

Psychoacoustic study on contribution of fan noise to engine noise

There are more researches on engine fan noise control focusing on reducing fan noise level through optimizing fan structure, and a lot of research achievements have been obtained. However, researches on the effect of fan noise to engine noise quality are lacking. The influences of the effects of fan structure optimization on the engine noise quality are unclear. Thus, there will be a decline in fan noise level, but the deterioration of engine noise quality. Aiming at the above problems, in consideration of fan structure design and engine noise quality, an innovative method to analyze the contribution of fan noise to engine noise quality using psychoacoustic theory is proposed. The noises of diesel engine installing different cooling fans are measured by using the acoustic pressure method. The experiment results are regarded as analysis samples. The model of sensory pleasantness is used to analyze the sound quality of a diesel engine with different cooling fans. Results show that after installing 10-blade fan in medium diameter the sensory pleasantness at each test point is increased, and the increase is 13.53% on average, which indicate the improvement of the engine noise quality. In order to verify the psychoacoustical analysis, the subjective assessment is carried out. The test result shows the noise quality of engine installed 10-blade fan in medium diameter is most superior. 1/3 octave frequency spectrum analysis is used to study the reason of the improvement of engine noise quality. It is found that after installing proper cooling fan the sound pressure level below 400 Hz are obviously increased, the frequency assignment and spectral envelope are more reasonable and a proper cooling fan can optimize the spectrum structure of the engine noise. The psychoacoustic study is applied in the contribution of fan noise to engine noise, and the idea of engine sound quality improvement through the structure optimization is proposed.