Application of entropy production theory for energy losses and other investigation in pumps and turbines: A review

The invention relates to an adaptive extraction and diagnosis method for degree features of a mechanical fault through a stack-type sparse automatic coding depth neural network, and belongs to the technical field of mechanical equipment state monitoring and reliability evaluation. The method aims at a problem of intelligent diagnosis of the degree of the mechanical fault, and comprises the steps: carrying out the stacking of sparse automatic coding, adding a classification layer, and constructing the stack-type sparse automatic coding depth neural network which integrates the adaptive learning and extraction of the degree features of the fault and fault recognition; employing the advantage that the sparse automatic coding can automatically learn the internal features of data, and adding noise coding to be integrated in the sparse automatic coding for improving the robustness of feature learning; carrying out the layer-by-layer no-supervision adaptive learning and supervision fine tuning of the original input complex data through multilayer sparse automatic coding, completing the automatic extraction and expression of the degree features of the mechanical fault and achieving the intelligent diagnosis of the degree of the fault. The method is used for the diagnosis of the degree of faults of rolling bearings under different work conditions, and obtains a good effect of feature extraction and diagnosis.

Adaptive extraction and diagnosis method for degree features of mechanical fault through stack-type sparse automatic coding depth neural network

A fault diagnosis method for loose slipper failure of piston pump in construction machinery under changing load

Fault diagnosis in a hydraulic directional valve using a two-stage multi-sensor information fusion

With the rapid development in technology, computer aided detection or diagnosis has become an indispensable part of the medical industry. Automatic detection of epileptic events is one of the important subjects that have aroused wide interest from more and more investigators. This paper proposes a new model in classification of multi-category electroencephalogram (EEG) signals using time-frequency image and block texture features. The one-dimensional EEG is first mapped to time-frequency domain by means of short-time Fourier transform (STFT), which is adapted to obtain a two-dimensional time-frequency image (2D-TFI). With the idea of multi-scale blocking, the obtained phase images and amplitude images are divided into several sub-blocks corresponding to different frequency ranges and time periods. Then the texture features are calculated to describe the behaviour of EEG signals. Particularly, a novel quadratic feature selection method based on kernel entropy component analysis (KECA) and Kruskal-Wallis test (KW) has been proposed for dimension reduction, by which the features that contained most distinctive information were provided. Eventually, the optimal KECA-based features are fed to support vector machine (SVM) for deciding the class of corresponding EEG. The proposed model is found to achieve at least 99.30% accuracy, 98.0% sensitivity and 100% specificity for each of the eight clinical problems. Our scheme is proven to be effective for seizure detection, which can help doctors optimize the diagnosis workflow, reduce workload and improve detection precision.

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Classification Epileptic Seizures in EEG Using Time-Frequency Image and Block Texture Features

The invention discloses a rolling bearing sound signal fault diagnosis method based on short-time Fourier transform and a sparse laminated automatic encoder. According to the method of the invention, firstly a smart mobile phone is used for acquiring a sound signal of the rolling bearing fault; then short-time Fourier analysis is performed on the sound signal for obtaining a spectrogram matrix; then the modulus value of the matrix is acquired and gray scale normalization processing is performed; then the normalized data are selected and input into a deep studying network for automatically extracting characteristics; and finally the characteristic which is extracted by a neural net is input into a Softmax classifier for identifying the fault mode. The invention provides the rolling bearing sound signal fault diagnosis method based on smart mobile phone sound signal short-time Fourier transform (STFT) and the sparse laminated automatic encoder (SAE). Through testing result analysis, the rolling bearing sound signal fault diagnosis method can accurately determine the fault mode of the rolling bearing.

Rolling bearing sound signal fault diagnosis method based on short-time Fourier transform and sparse laminated automatic encoder

The invention provides a deep-learning rolling bearing fault diagnosis method based on SDA (stacked denoising autoencoder) and Softmax regression. On the basis of analysis of rolling bearing faults and aiming at the problem of limitations on precision and robustness of current sorting algorithms, knowledge in related fields of image mode recognition is used for reference, a deep-learning autonomous cognitive method based on a multilayer neural network is adopted, self expression of original data under the condition that an input portion is shielded is realized by the aid of a stacked denoising autoencoder model, and reconstructed data are inputted to a Softmax regression model to judge operating conditions and fault types of rolling bearings. According to analysis of test results, the deep-learning rolling bearing fault diagnosis method based on SDA and Softmax regression can be effectively applied to fault diagnosis of the rolling bearings.

Deep-learning rolling bearing fault diagnosis method based on SDA (stacked denoising autoencoder) and Softmax regression

The fault diagnosis of hydraulic pumps is currently important and significant to ensure the normal operation of the entire hydraulic system. Considering the nonlinear characteristics of hydraulic-pump vibration signals and the mode mixing problem of the original Empirical Mode Decomposition (EMD) method, first, we use the Complete Ensemble EMD (CEEMD) method to decompose the signals. Second, the time-frequency analysis methods, which include the Short-Time Fourier Transform (STFT) and time-frequency entropy calculation, are applied to realize the robust feature extraction. Third, the multiclass Support Vector Machine (SVM) classifier is introduced to automatically classify the fault mode in this paper. An actual hydraulic-pump experiment demonstrates the procedure with a complete feature extraction and accurate mode classification.

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Fault Diagnosis of a Hydraulic Pump Based on the CEEMD-STFT Time-Frequency Entropy Method and Multiclass SVM Classifier

The invention discloses a rolling bearing sound signal fault feature extraction method based on an STFT and a rotation inertia entropy. A common rolling bearing fault feature extraction method is based on a rolling bearing vibration signal, however the requirement of a sensor by the collection of rolling bearing vibration data is very high, the equipment cost is increased, an intelligent mobile phone is taken as an important part of daily life, and the recording function of the mobile phone can collect a rolling bearing sound signal. The invention provides the rolling bearing sound signal fault feature extraction method based on short time Fourier transform (STFT) and a rotation inertia entropy, firstly the intelligent mobile phone is used to collect a rolling bearing fault sound signal, then the sound signal is subjected to Fourier analysis, a spectrogram matrix is obtained, then the modulus of the matrix is obtained, and the rotation inertia entropy of the spectrogram of the rotation inertia entropy is calculated. A test result analysis shows that the fault feature obtained by the method has an excellent classification characteristic and can support the fault diagnosis of a rolling bearing.

Li Lianfeng

Papers

Rolling bearing sound signal fault diagnosis method based on short-time Fourier transform and sparse laminated automatic encoder

Deep-learning rolling bearing fault diagnosis method based on SDA (stacked denoising autoencoder) and Softmax regression

Fault Diagnosis of a Hydraulic Pump Based on the CEEMD-STFT Time-Frequency Entropy Method and Multiclass SVM Classifier

Rolling bearing sound signal fault feature extraction method based on STFT and rotation inertia entropy