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Condition monitoring of piston pumps has great significance to ensure the reliability and security of hydraulic systems. However, the complex working conditions of the integrated electromechanical systems make the fault mechanism unclear which is difficult for fault diagnosis by feature matching techniques. In this paper, a novel minimum entropy deconvolution (MED)-based convolutional neural network (CNN) is presented to classify faults in axial piston pumps. Firstly, the collected raw signals are preprocessed using the MED technique. Then, the filtered signals are used to construct training samples and testing samples. Finally, the constructed samples are fed into the CNN to classify the multi-faults of axial piston pumps. With the convolution and subsampling operations, the present model can automatically obtain data features via iterative learning processes, which is suitable for the unknown fault mechanism problems. The learned features are visualized by t-distributed stochastic neighbor embedding technique. A benchmark data simulation of mechanical transmission systems and an experimental data investigation of an axial piston pump are performed to manifest the superiority of the present method by comparing with the traditional CNN. [ABSTRACT FROM AUTHOR]

Sensor fault diagnosis is necessary to ensure the normal operation of a gas turbine system. However, the existing methods require too many resources and this need can't be satisfied in some occasions. Since the sensor readings are directly affected by sensor state, sensor fault diagnosis can be performed by extracting features of the measured signals. This paper proposes a novel fault diagnosis method for sensors based on wavelet entropy. Based on the wavelet theory, wavelet decomposition is utilized to decompose the signal in different scales. Then the instantaneous wavelet energy entropy (IWEE) and instantaneous wavelet singular entropy (IWSE) are defined based on the previous wavelet entropy theory. Subsequently, a fault diagnosis method for gas turbine sensors is proposed based on the results of a numerically simulated example. Then, experiments on this method are carried out on a real micro gas turbine engine. In the experiment, four types of faults with different magnitudes are presented. The experimental results show that the proposed method for sensor fault diagnosis is efficient. [ABSTRACT FROM AUTHOR]

The stator current signals of mining asynchronous motor are often non-stationary, making it challenging to extract fault features in the time domain. Therefore, this paper proposes a rotor fault diagnosis method based on the combination of Modified Ensemble Empirical Mode Decomposition (MEEMD) energy entropy and Artificial Neural Network (ANN). Firstly, the stator current signals are decomposed into a series of Intrinsic Mode Function (IMF) components by the MEEMD. Secondly, the IMF components with the most abundant information are selected by the cross-correlation criterion, and their energy entropy is calculated to construct feature vectors. Finally, the feature vectors are input into the ANN for training and state recognition. The faulty motor is modeled by ANSYS Maxwell software to obtain the simulated data. It is verified that the MEEMD-ANN method is feasible for fault diagnosis of mine motors, which can accurately identify the different status of motors, including normal state, broken rotor bars, and air gap eccentricity, the recognition rate can reach 99%. The MEEMD-ANN improves the accuracy by 2% compared with the EEMD-ANN, improves the accuracy by 3.75% compared with the MEEMD-SVM. • A motor fault diagnosis method based on MEEMD energy entropy and ANN was proposed. • Used Ansys Maxwell software to establish a finite element faulty motor model. • Realized the accurate recognition of asynchronous motor faults. • Recognition rate of normal state, rotor bar broken and air gap eccentricity is 99%. [ABSTRACT FROM AUTHOR]