Intelligent Diagnosis of Rotating Machinery Based on Optimized Adaptive Learning Dictionary and 1DCNN

In the intelligent fault diagnosis of rotating machinery, it is difficult to extract early weak fault impact features of rotating machinery under the interference of strong background noise, which makes the accuracy of fault identification low. In order to effectively identify the early faults of rotating machinery, an intelligent fault diagnosis method of rotating machinery based on an optimized adaptive learning dictionary and one-dimensional convolution neural network (1DCNN) is proposed in this paper. First of all, based on the original signal, a redundant dictionary with impact components is constructed by K-singular value decomposition (K-SVD), and the sparse coefficients are solved by an optimized orthogonal matching pursuit (OMP) algorithm. The sparse representation of fault impact features is realized, and the reconstructed signal with a concise fault impact feature structure is obtained. Secondly, the reconstructed signal is normalized, and the experimental dataset is divided into samples. Finally, the training set is input into the 1DCNN model for model training, and the test set is input into the trained model for classification and detection to complete the intelligent fault classification diagnosis of rotating machinery. This method is applied to the fault diagnosis of bearing data of Case Western Reserve University and worm gear reducer data of Shanghai University of Technology. Compared with other methods and models, the results show that the diagnosis method proposed in this paper can achieve higher diagnosis accuracy and better generalization ability than other diagnosis models under different datasets.

Sparse phase retrieval via ℓp (0 < p ≤ 1) minimization

In this paper, the [Formula: see text]-analysis model for the phase retrieval problem of sparse unknown signals in the redundant dictionary is extended to the [Formula: see text]-analysis model, where [Formula: see text]. It’s shown that if the measurement matrix [Formula: see text] satisfies the strong restricted isometry property adapted to D (S-DRIP) condition, the unknown signal [Formula: see text] can be stably recovered by analyzing the [Formula: see text] [Formula: see text] minimization model.

Fast Matching Pursuit with Multi-Gabor Dictionaries

Finding the best K -sparse approximation of a signal in a redundant dictionary is an NP-hard problem. Suboptimal greedy matching pursuit algorithms are generally used for this task. In this work, we present an acceleration technique and an implementation of the matching pursuit algorithm acting on a multi-Gabor dictionary, i.e., a concatenation of several Gabor-type time-frequency dictionaries, each of which consists of translations and modulations of a possibly different window and time and frequency shift parameters. The technique is based on pre-computing and thresholding inner products between atoms and on updating the residual directly in the coefficient domain, i.e., without the round-trip to the signal domain. Since the proposed acceleration technique involves an approximate update step, we provide theoretical and experimental results illustrating the convergence of the resulting algorithm. The implementation is written in C (compatible with C99 and C++11), and we also provide Matlab and GNU Octave interfaces. For some settings, the implementation is up to 70 times faster than the standard Matching Pursuit Toolkit.

Sparse Bayes Tensor and DOA Tracking Inspired Channel Estimation for V2X Millimeter Wave Massive MIMO System

Efficient vehicle-to-everything (V2X) communications improve traffic safety, enable autonomous driving, and help to reduce environmental impacts. To achieve these objectives, accurate channel estimation in highly mobile scenarios becomes necessary. However, in the V2X millimeter-wave massive MIMO system, the high mobility of vehicles leads to the rapid time-varying of the wireless channel and results in the existing static channel estimation algorithms no longer applicable. In this paper, we propose a sparse Bayes tensor and DOA tracking inspired channel estimation for V2X millimeter wave massive MIMO system. Specifically, by exploiting the sparse scattering characteristics of the channel, we transform the channel estimation into a sparse recovery problem. In order to reduce the influence of quantization errors, both the receiving and transmitting angle grids should have super-resolution. We obtain the measurement matrix to increase the resolution of the redundant dictionary. Furthermore, we take the low-rank characteristics of the received signals into consideration rather than singly using the traditional sparse prior. Motivated by the sparse Bayes tensor, a direction of arrival (DOA) tracking method is developed to acquire the DOA at the next moment, which equals the sum of the DOA at the previous moment and the offset. The obtained DOA is expected to provide a significant angle information update for tracking fast time-varying vehicular channels. The proposed approach is evaluated over the different speeds of the vehicle scenarios and compared to the other methods. Simulation results validated the theoretical analysis and demonstrate that the proposed solution outperforms a number of state-of-the-art researches.

A clustering K-SVD-based sparse representation method for rolling bearing fault diagnosis

It is challenging to extract weak impulse features from vibration signals corrupted by strong noise in mechanical fault diagnosis. Due to its simple calculation, fast convergence and easy implementation, K-singular value decomposition (K-SVD) has been widely used in rolling bearing fault diagnosis. However, it fails to consider the influence of noise and harmonics on atoms learning from impulse characteristics, which results in many irrelevant atoms, and then increases the difficulty of extracting the impulse features in bearing fault signals. Therefore, a clustering K-SVD-based sparse representation method is proposed in this paper and it is combined with the particle swarm optimisation (PSO)-based time-varying filter empirical mode decomposition (TVF-EMD) for rolling bearing fault diagnosis. The PSO-based TVF-EMD is developed to automatically decompose the original signal to eliminate the impact of noise and harmonics on the impulses in the signal. Then, the clustering K-SVD method is applied to perform dictionary learning on the sensitive component containing impulses to obtain a redundant dictionary of atoms with obvious impulse patterns. Finally, the orthogonal matching pursuit (OMP) algorithm is introduced to extract the fault features from rolling bearing vibration signals. The experimental results show that the proposed method can improve the robustness of the dictionary atoms to noise and achieve the extraction of rolling bearing fault features.

Bearing Fault Diagnosis Based on Collaborative Representation Using Projection Dictionary Pair

In state analysis of rolling bearings using collaborative representation theory, how to construct an excellent redundant dictionary to collaboratively represent the acquired normal or abnormal data has been being a significant issue. Thus, a new method for fault detection and classification of rolling bearings is proposed in this paper. The proposed algorithm mainly consists of three components. First, a wavelet transform is employed to extract features, which takes advantage of the observation that vibration signals under different conditions have similar frequency spectra. This similarity ensures that we can collaboratively represent any test sample by using training samples. Second, under the similarity assumption, a dictionary pair learning strategy is employed to build an overcomplete dictionary pair, which is used to realize an optimal representation of the vibration signal. Meanwhile, the sparse constraint is also taken into account during dictionary training to enhance the robustness of the classification. Finally, the learned dictionary combined with collaborative representation is used to intelligently perform pattern classification of rolling bearings. The effectiveness and superiority of the method are verified by applying the proposed algorithm on the simulated and real vibration signals. The results show that, for different fault categories generated from different fault size and motor loads, our method can rapidly and accurately identify the fault category to which the input sample belongs.

A Compressed Equivalent Source Method Based on Equivalent Redundant Dictionary for Sound Field Reconstruction

The equivalent source method (ESM) based on compressive sensing (CS) requires that the source has a sparse or approximately sparse representation in a suitable basis or dictionary. However, in practical applications, it is not easy to find the appropriate basis or dictionary due to the indeterminate characteristics of the source. To solve this problem, an equivalent redundant dictionary is constructed, which contains two core parts: one is the equivalent dictionary used in the CS-based ESMs under the sparse assumption, and the other one is the orthogonal basis obtained by the singular value decomposition (SVD). On this foundation, a method named compressed ESM based on the equivalent redundant dictionary (ERDCESM) is proposed to enhance the performances of source field reconstruction for different types of sources. Moreover, inspired by the idea of functional beamforming (FB), ERDCESM with order v (ERDCESM- v ) can possess a high dynamic range when detecting the source location. The numerical simulations are carried out at different frequencies to evaluate the performance of the proposed method, and the results suggest that the proposed method performs well both for sparse and even spatially extended sources. The validity and practicality of the proposed method are also verified by the experimental results.