Comparison of feature extraction from wavelet packet based on reconstructed signals versus wavelet packet coefficients for fault diagnosis of rotating machinery

Vibration signals from rotating machines are usually nonlinear and nonstationary. Time frequency techniques are suitable for analyzing this type of signals. Wavelet analysis is one of the most powerful methods in this regards. Therefore, wavelet analysis is used extensively for diagnosis of nonlinear and nonstationary signals. Faults in rotating machines show their effects in certain frequency bands. In this research the features extracted from reconstructed signals from wavelet packets were compared to features extracted from wavelet packet coefficients. It is shown that reconstructed signals act better for fault diagnosis than wavelet packet coefficients. To support our claim one example is designed that justifies our hypothesis. To evaluate our hypothesis in real world practical situations, health condition monitoring of a motorcycle gearbox has been considered. In this practical situation wavelet coefficients and reconstructed signals from wavelet packet coefficients extracted from signals acquired from gearbox housing were compared. Mahalanobis distance (MD) is employed to evaluate the significance of the extracted features. It is shown that features extracted from reconstructed signals are more suitable than features extracted from wavelet packet coefficients

Intelligent fault classification of rolling bearings using neural network and discrete wavelet transform

This paper is about diagnosis and classification of bearing faults using Neural Networks (NN), employing nondestructive tests. Vibration signals are acquired by a bearing test machine. The acquired signals are preprocessed using discrete wavelet analysis. Standard deviation of discrete wavelet coefficient is chosen as the distinguishing feature of the faults. This feature vector is given to the design network as inputs. The input vector is normalized prior to be applied to neural network. There are four output neurons each of which corresponds to: 1) bearing with inner race fault, 2) bearing with outer race fault, 3) bearing with ball defect, and 4) normal bearing. The structure of NN is 6:20:4 and with 99 % performance

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

Intelligent fault classification of rolling bearings using neural network and discrete wavelet transform

This paper is about diagnosis and classification of bearing faults using Neural Networks (NN), employing nondestructive tests. Vibration signals are acquired by a bearing test machine. The acquired signals are preprocessed using discrete wavelet analysis. Standard deviation of discrete wavelet coefficient is chosen as the distinguishing feature of the faults. This feature vector is given to the design network as inputs. The input vector is normalized prior to be applied to neural network. There are four output neurons each of which corresponds to: 1) bearing with inner race fault, 2) bearing with outer race fault, 3) bearing with ball defect, and 4) normal bearing. The structure of NN is 6:20:4 and with 99 % performance