The valve train fault is a common mechanical fault of internal combustion engines (ICEs) due to the valve clearance usually oversized because of the wear of valve mechanism, material deformations, and long continuous ...
详细信息
Due to the influence of various factors during the operation of mechanical equipment, the vibration appears the characteristics of non-stationarity. In order to analyze the correlation of other monitoring indicators t...
详细信息
ISBN:
(数字)9781728159225
ISBN:
(纸本)9781728159232
Due to the influence of various factors during the operation of mechanical equipment, the vibration appears the characteristics of non-stationarity. In order to analyze the correlation of other monitoring indicators that cause vibration non-stationarity, it is necessary to study the vibration stationarity. A research method of vibration stationarity based on correlation coefficient is proposed in this paper. Firstly, the correlation coefficient of vibration data and corresponding time is calculated by sliding to reflect the vibration trend. Secondly, the fluctuation threshold is determined by statistical characteristics. Finally, the vibration stationarity is tested. The test results of vibration data of nuclear main pump show that the method proposed in this paper can better test the vibration stationarity, which lays a foundation for the study of the factors related to the operating states of mechanical equipment.
Abnormal valve clearance is a common fault of diesel engine, and early warning of abnormal valve clearance plays an important role in the condition based maintenance of diesel engine. Although information fusion techn...
详细信息
ISBN:
(数字)9781728101996
ISBN:
(纸本)9781728102009
Abnormal valve clearance is a common fault of diesel engine, and early warning of abnormal valve clearance plays an important role in the condition based maintenance of diesel engine. Although information fusion technology can improve the accuracy of fault diagnosis, it cannot guarantee that the fused features can perfectly represent the required key information. For the incomplete feature set, a method combining multi-domain feature and improved support vector machine is proposed. Firstly, the extraction of multi-domain feature is carried out to deeply explore the state information of valve train contained in the original vibration signal. The statistical characteristics and waveform characteristics are extracted from time domain vibration signals, and the frequency domain feature similar to time-domain feature is extracted after the Fourier transform of the vibration signal. What's more, according to the working principle of diesel engine, the energy characteristics in angular frequency domain are extracted. Then, an improved support vector machine method based on multi-domain feature is proposed to further reduce the diagnostic errors caused by incomplete feature set. Finally, the proposed method is compared with other traditional methods about the fault diagnosis of valve train of diesel engine. The results show that the proposed method is applicable to the fault diagnosis of valve train of diesel engine with good accuracy, and the generalization ability of diagnostic model is greatly improved.
In this paper, the gray level co-occurrence matrix (GLCM) and histogram of oriented gradient (HOG) features fusion of time-frequency image are introduced into the reciprocating compressor fault diagnosis. Firstly, vib...
详细信息
ISBN:
(数字)9781728101996
ISBN:
(纸本)9781728102009
In this paper, the gray level co-occurrence matrix (GLCM) and histogram of oriented gradient (HOG) features fusion of time-frequency image are introduced into the reciprocating compressor fault diagnosis. Firstly, vibration signals are acquired from the reciprocating compressor in different states of head tile and the wavelet transform distributions of vibration signals were displayed in time-frequency images. Secondly, GLCM and HOG methods are used to extract features from time-frequency images, then GLCM feature and HOG feature are fused and input into support vector machine for recognition and classification. By this way, the fault diagnosis of time series signals of reciprocating compressor is transferred to the classification of time-frequency images. The results show that can accurately realize diagnosis of small-head wear fault of reciprocating compressor.
Considering the diesel engine vibration signals have the characteristics of the non-stability and non-linearity due to its compact-complex structure, strong noise and especially unstable operating conditions, we propo...
详细信息
ISBN:
(数字)9781728101996
ISBN:
(纸本)9781728102009
Considering the diesel engine vibration signals have the characteristics of the non-stability and non-linearity due to its compact-complex structure, strong noise and especially unstable operating conditions, we proposes an novel method based on improved wavelet packet-Mel frequency and convolutional neural network (CNN) to extract features and diagnose faults of diesel engine valve. Firstly, the wavelet packet transform is applied with the purpose of decomposing vibration signal and reconstructing each wavelet packet coefficient. Secondly, an improved Mel frequency cepstrum method is used to extract features from the reconstructed vibration signals. MFC algorithm is a well-known feature extraction technique widely used for speech recognition. Then, feature matrixes are constituted to obtain more definite and comprehensive time-frequency distributed representation, of which the row represents the average Mel frequency cepstrum coefficients and the column represents the frequency bands of wavelet packet decomposition in ascending order. Finally, a deep hierarchical CNN structure constructed by convolution layers, max-pooling layers and fully-connected layers is trained using a standard backpropagation, of which the input of first layer with 256 neurons is the above 2D feature matrixes and the output of final layer with 3 neurons is the number of vibration signal states. The experimental results of the fault diagnosis for the diesel engine valves show that the proposed method has the good diagnosis performance for diesel engine valve clearance faults.
The valve train fault is a common mechanical fault of internal combustion engines (ICEs) due to the valve clearance usually oversized because of the wear of valve mechanism, material deformations, and long continuous ...
详细信息
ISBN:
(数字)9781728101996
ISBN:
(纸本)9781728102009
The valve train fault is a common mechanical fault of internal combustion engines (ICEs) due to the valve clearance usually oversized because of the wear of valve mechanism, material deformations, and long continuous running hours. Feature extraction dependent on the expertise and experience too much in traditional fault diagnosis. In this study, a stacked autoencoder (SAE) is proposed for adaptive and hierarchical feature extraction in cylinder vibration signals. The capability of feature mining in SAE is enhanced after unsupervised layer-by-layer pre-training and supervised fine-tuning. Further, the dropout trick and the batch normalization trick are introduced to prevent over-fitting and accelerate model convergence. The harmonic search (HS) algorithm is proposed to obtain the optimal hyper-parameter values in the SAE model, and achieve adaptive adjustment of the model structure. The diesel engine vibration data consisting of seven valve health states is employed to verify the effectiveness of the proposed method, the results demonstrate that the proposed method outperforms original SAE and many conventional fault diagnosis algorithms in terms of the classification accuracy.
暂无评论