Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the proper intrinsic mode functions (IMF) dictated by the dyadic filter banks. As EEMD is a time–space analysis method, the added white noise is averaged out with sufficient number of trials; the only persistent part that survives the averaging process is the component of the signal (original data), which is then treated as the true and more physical meaningful answer. The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF. With this ensemble mean, one can separate scales naturall...

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Ensemble empirical mode decomposition: a noise-assisted data analysis method

Deep learning and its applications to machine health monitoring

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

This book presents systematic overviews and bright insights into big data-driven intelligent fault diagnosis and prognosis for mechanical systems 

Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems

Optimal Hilbert transform parameter identification of bistable structures

Halbach high negative stiffness isolator: Modeling and experiments

In the studies of intelligent fault diagnosis of machines, lots of effort goes into designing effective feature extraction algorithms. Such processes would consume plenty of human labor, especially when dealing with massive vibration signals. So it is interesting to automatically extract features using machine learning techniques, instead of manually extracting them. To deal with the problem, this paper presents a new automatic feature extraction method of machines. The proposed method first learns features from the vibration signals by K-means, and then maps the learned features into a salient low-dimensional feature space using t-distributed stochastic neighbor embedding (t-SNE). Through the feature extraction results of a bearing dataset, it is verified that the proposed method is able to effectively learn the features from the raw vibration signals and is superior to the manual features like time-domain features and wavelet features. Therefore, the proposed method has potential to be a tool in the automatic data mining of intelligent fault diagnosis.

Yaguo Lei

Papers

Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems

Optimal Hilbert transform parameter identification of bistable structures

Halbach high negative stiffness isolator: Modeling and experiments

A method of automatic feature extraction from massive vibration signals of machines

Deep multisource parallel bilinear-fusion network for remaining useful life prediction of machinery