A review on empirical mode decomposition in fault diagnosis of rotating machinery

This paper presents a novel signal processing scheme, bandwidth empirical mode decomposition, and adaptive multiscale morphological analysis (BEMD-AMMA) for early fault diagnosis of rolling bearings. In this scheme, we propose a bandwidth based method to select the best envelope interpolation method. First, multiple envelope algorithms are defined and separately subtracted from the original data to obtain the preintrinsic mode functions (PIMFs). Second, an IMF with the smallest frequency bandwidth is selected to be the optimal IMF (OIMF). Third, this OIMF is subtracted from the original signal, and then repeat the sifting process until the residual is a constant or monotonic. Since the OIMF has the smallest frequency bandwidth, the mode mixing phenomenon can be significantly weakened. After that the OIMFs with clear fault information are used to construct the main component of the original signal. Then, the AMMA is introduced to demodulate the constructed main component. Simulation and experimental vibration signals are employed to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms EMD-AMMA, ensemble empirical mode decomposition-AMMA, and generalized empirical mode decomposition-empirical envelope demodulation in detecting early inner race fault.

Application of Bandwidth EMD and Adaptive Multiscale Morphology Analysis for Incipient Fault Diagnosis of Rolling Bearings

Recently, research on data-driven bearing fault diagnosis methods has attracted increasing attention due to the availability of massive condition monitoring data. However, most existing methods still have difficulties in learning representative features from the raw data. In addition, they assume that the feature distribution of training data in source domain is the same as that of testing data in target domain, which is invalid in many real-world bearing fault diagnosis problems. Since deep learning has the automatic feature extraction ability and ensemble learning can improve the accuracy and generalization performance of classifiers, this paper proposes a novel bearing fault diagnosis method based on deep convolutional neural network (CNN) and random forest (RF) ensemble learning. Firstly, time domain vibration signals are converted into two dimensional (2D) gray-scale images containing abundant fault information by continuous wavelet transform (CWT). Secondly, a CNN model based on LeNet-5 is built to automatically extract multi-level features that are sensitive to the detection of faults from the images. Finally, the multi-level features containing both local and global information are utilized to diagnose bearing faults by the ensemble of multiple RF classifiers. In particular, low-level features containing local characteristics and accurate details in the hidden layers are combined to improve the diagnostic performance. The effectiveness of the proposed method is validated by two sets of bearing data collected from reliance electric motor and rolling mill, respectively. The experimental results indicate that the proposed method achieves high accuracy in bearing fault diagnosis under complex operational conditions and is superior to traditional methods and standard deep learning methods.

Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning.

Robust Cyber-Physical Systems

Hilbert marginal spectrum analysis for automatic seizure detection in EEG signals

Signal feature extraction based on an improved EMD method

Particle filtering shows great promise in addressing a wide variety of non-linear and /or non-Gaussian problem. A crucial issue in particle filtering is the selection of the importance proposal distribution. In this paper, the iterated extended Kalman filter (IEKF) is used to generate the proposal distribution. The proposal distribution integrates the latest measurements into system state transition density, so it can match the posteriori density well. The simulation results show that the new particle filter superiors to the standard particle filter and the other filters such as the unscented particle filter (UPF), the extended Kalman particle filter (PF-EKF), the EKF.

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The iterated extended Kalman particle filter

Maximum entropy fuzzy clustering with application to real-time target tracking

A robust D–S fusion algorithm for multi-target multi-sensor with higher reliability

The detection and parameter estimation of spread spectrum signals under non-cooperative conditions are key problems to which more attentions are paid in the field of signal processing. A cyclic-cumulant based DS-SS (direct sequence spread spectrum) signal detection scheme is developed in this paper by extending the asymptotically optimal chi-squared test for the presence of cyclostationarity to DS-SS signal detection. The explicit formula of the second-order cyclic-cumulant is derived, and the influence of time interval on the cycle-frequency estimation from the cyclic-cumulant is analyzed in detail. On the base of this, a new cyclic-cumulant based DS-SS signal parameter estimation method is proposed, which can perform blind detection and blind parameter estimation without a priori knowledge of the signal such as pseudorandom sequence and frequencies. Moreover, the method also avoids multi-dimensional searching in parameter estimation. Simulation results have proved the validity of the method.

Ji Hongbing

Papers

Signal feature extraction based on an improved EMD method

The iterated extended Kalman particle filter

Maximum entropy fuzzy clustering with application to real-time target tracking

A robust D–S fusion algorithm for multi-target multi-sensor with higher reliability

A cyclic-cumulant based method for DS-SS signal detection and parameter estimation