Combined fault detection and classification of internal combustion engine using neural network

Different faults in internal combustion engines leads to excessive fuel consumption, pollution, acoustic emission and wear of engine components. Detection of fault is also difficult for maintenance technicians due to broad range of faults and combination of the faults. In this research the faults due to malfunction of manifold absolute pressure, knock sensor and misfire are detected and classified by analyzing vibration signals. The vibration signals acquired from engine block were preprocessed by wavelet analysis, and signal energy is considered as a distinguishing property to classify these faults by a Multi-Layer Perceptron Neural Network (MLPNN). The designed MLPNN can classify these faults with almost 100 % efficiency

Time-frequency feature extraction from multiple impulse source signal of reciprocating compressor based on local frequency

Vibration signals generated by reciprocating compressor present a multiple impulse source property, which is typical non-stationary. For this kind of signal, time-frequency analysis techniques, such as STFT, WVT, WT and HHT, represent some limitations. To alleviate this problem, a novel concept of local frequency (LF) is proposed in the paper. Based on the concept, a time-frequency distribution algorithm is established. Some non-stationary simulation signals, including multi-harmonic signal, FM signal and multiple impulse source signal, are investigated to identify the feasibility and effectiveness of the novel time-frequency analysis technique. Compared with WVT, WT and HHT, time-frequency analysis based on LF represents a higher resolution and more useful information. Moreover, the proposed approach is applied to the fault feature extraction of reciprocating compressor gas valve vibration signal in normal valve state and gap valve state. The results indicate the superiority of proposed approach in extracting time-frequency features from multiple impulse source signal of reciprocating compressor, which obtains a more precise result than WVT, WT and HHT. So it can provide an effective basis to fault diagnosis of reciprocating compressor

A fault diagnosis approach of reciprocating compressor gas valve based on local mean decomposition and autoregressive-generalized autoregressive conditional heteroscedasticity model

Vibration signal of reciprocating compressor gas valve represents nonlinear, non-stationary and multiple impulse characteristics. To extract the features of the signal, an integration approach based on local mean decomposition (LMD) method and autoregressive-generalized autoregressive conditional heteroscedasticity (AR-GARCH) model is proposed. First, the property of LMD is investigated in the paper, which indicates that LMD method not only can effectively alleviate non-stationary feature and restrain the end effect of non-stationary signal, but also can accurately improve the resolution of time-frequency analysis. Then, the first five PF components of gas valve signal are modeled by AR(1)-GARCH(1, 1) model as the feature vectors without any prior knowledge about the fault mechanism. Finally, the BP neural networks are applied to diagnose the faults of reciprocating compressor gas valve based on the feature vectors above. The results indicate that high diagnosis accuracy can be obtained by integrating LMD method and AR-GARCH model. So the approach proposed in the paper provides an effective measure to extract the fault feature of reciprocating compressor gas valve

A fault diagnosis approach of reciprocating compressor gas valve based on local mean decomposition and autoregressive-generalized autoregressive conditional heteroscedasticity model

Vibration signal of reciprocating compressor gas valve represents nonlinear, non-stationary and multiple impulse characteristics. To extract the features of the signal, an integration approach based on local mean decomposition (LMD) method and autoregressive-generalized autoregressive conditional heteroscedasticity (AR-GARCH) model is proposed. First, the property of LMD is investigated in the paper, which indicates that LMD method not only can effectively alleviate non-stationary feature and restrain the end effect of non-stationary signal, but also can accurately improve the resolution of time-frequency analysis. Then, the first five PF components of gas valve signal are modeled by AR(1)-GARCH(1, 1) model as the feature vectors without any prior knowledge about the fault mechanism. Finally, the BP neural networks are applied to diagnose the faults of reciprocating compressor gas valve based on the feature vectors above. The results indicate that high diagnosis accuracy can be obtained by integrating LMD method and AR-GARCH model. So the approach proposed in the paper provides an effective measure to extract the fault feature of reciprocating compressor gas valve

A fault diagnosis approach of reciprocating compressor gas valve based on local mean decomposition and autoregressive-generalized autoregressive conditional heteroscedasticity model

Vibration signal of reciprocating compressor gas valve represents nonlinear, non-stationary and multiple impulse characteristics. To extract the features of the signal, an integration approach based on local mean decomposition (LMD) method and autoregressive-generalized autoregressive conditional heteroscedasticity (AR-GARCH) model is proposed. First, the property of LMD is investigated in the paper, which indicates that LMD method not only can effectively alleviate non-stationary feature and restrain the end effect of non-stationary signal, but also can accurately improve the resolution of time-frequency analysis. Then, the first five PF components of gas valve signal are modeled by AR(1)-GARCH(1, 1) model as the feature vectors without any prior knowledge about the fault mechanism. Finally, the BP neural networks are applied to diagnose the faults of reciprocating compressor gas valve based on the feature vectors above. The results indicate that high diagnosis accuracy can be obtained by integrating LMD method and AR-GARCH model. So the approach proposed in the paper provides an effective measure to extract the fault feature of reciprocating compressor gas valve

Application of EMD-WVD and particle filter for gearbox fault feature extraction and remaining useful life prediction

Fault feature extraction and remaining useful life (RUL) prediction are important to condition based maintenance (CBM). In order to realize the fault feature extraction of gearbox vibration signal presenting nonlinear and non-Gaussian, the integration of empirical mode decomposition (EMD) and Wigner-Ville distribution (WVD) are proposed in this paper. Taking the kurtosis as standard, the WVD is applied to some IMFs with larger kurtosis to calculate the time-frequency distribution, with an effective suppress on mode mixing and the cross-term interference. Afterwards, particle filter (PF) with the state space model based on Wiener process is proposed to predict the RUL of gearbox considering degradation feature, gearbox teeth wear and nonlinear and non-Gaussian system. The gearbox life cycle test shows that the EMD-WVD method can extract the valued characteristics of vibration signal accurately, and the particle filter can provide an effective way to predict the RUL of gearbox

An Engine Fault Diagnosis System Using Intake Manifold Pressure Signal and Wigner-Ville Distribution Technique

[[abstract]]This paper proposed an engine fault diagnosis system based on intake manifold pressure signal and artificial neural network with the Wigner–Ville distribution technique. Traditionally, the engine diagnostic method depends on the experience of the technician, but some faults might be inaccurately judged by the technician’s experience when the engine is operating. In the present study, an engine platform diagnosis system using intake manifold pressure was developed. The algorithm of the proposed system consisted of Wigner–Ville distribution (WVD) for feature extraction and the neural network technique for fault classification. In previous work, the Wigner–Ville distribution was often used to analyze the non-stationary signal, because it provides a simple and clear energy spectrum diagram both in the time and frequency domains. This instantaneous energy diagram presented the magnitude of each engine fault under various operating conditions. The Wigner–Ville distribution extracts these features as database input to a neural network and the neural network is used to develop the training and testing modules. To prove the efficiency of the neural network, both the radial basis function neural network and generalized regression neural network are used and compared. The experimental results demonstrated the proposed system is effective and the performance is satisfactory