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 paper proposes a nonlinear degradation model based method for remaining useful life (RUL) prediction of rolling element bearings. First, a new nonlinear degradation model is constructed which considers four variable sources of stochastic degradation processes of bearings simultaneously, i.e., the temporal variability, the unit-to-unit variability, the measurement variability and the nonlinear variability. Then a Kalman particle filtering (KPF) algorithm is applied to estimate the state and predict the RUL of bearings. The effectiveness of the nonlinear degradation model based method is demonstrated using simulated degradation processes and accelerated degradation tests of rolling element bearings. The results show that the nonlinear model performs better than the linear model in describing the degradation processes of bearings, and KPF is more effective in the state estimation and RUL prediction of bearings than the Kalman filtering and the particle filtering algorithms.

A nonlinear degradation model based method for remaining useful life prediction of rolling element bearings

Intelligent fault diagnosis has been a research hotspot in recent years. However, most of the works are conducted based on the hypothesis that training and testing data subject to the same distribution. In engineering scenarios, machines usually work under variable operation conditions, which results in the data from different conditions subject to distribution discrepancy. Since transfer learning is able to reuse the related knowledge across different domains, a hybrid transfer learning method (HTLM) is proposed to utilize the diagnosis knowledge obtained from one operation condition to complete the diagnosis tasks under other conditions. In the method, transfer component analysis is firstly used to extract fault features with small distribution discrepancy from the cross-domain samples. After that, the features learned from the source domain help train a classifier by Tradaboost algorithm to improve its diagnosis accuracy on the target domain. The effectiveness of the proposed method is verified by a set of laboratory bearing datasets, in which the data under one operation condition are used to help identify the health states of bearings under another condition. The results indicate that the proposed HTLM is able to achieve a higher diagnosis accuracy than other diagnosis methods.

A Hybrid Transfer Learning Method for Fault Diagnosis of Machinery under Variable Operating Conditions

In this paper, a novel intelligent fault diagnosis method based on empirical mode decomposition (EMD), fuzzy feature extraction and support vector machines (SVM) is proposed. The method consists of two stages. In the first stage, intrinsic mode components are obtained with EMD from original signals and converted into fuzzy feature vectors, and then the mechanical fault can be detected. In the second stage, these extracted fuzzy feature vectors are input into the multi-classification SVM to identify the different abnormal cases. The proposed method is applied to the classification of a turbo-generator set under three different operating conditions. Testing results show that the classification accuracy of the proposed model is greatly improved compared with the multi-classification SVM without feature extraction and the multi-classification SVM with extracting the fuzzy feature from wavelet packets, and the faults of steam turbo-generator set can be correctly and rapidly diagnosed using this model.

Intelligent Fault Diagnosis in Power Plant Using Empirical Mode Decomposition, Fuzzy Feature Extraction and Support Vector Machines

The application of robotic technologies in building construction leads to great convenience and productivity improvement, and construction robots (CRs) bring enormous opportunities for the way we conduct design and construction. To get a better understanding of the trends and track the application of CRs for on‐site conditions, this paper conducts a systematic review of control models and status monitoring of CRs, which are two key aspects that determine construction accuracy and efficiency. Control accuracy and flexibility are primary needs for CRs applied in different scenes, so the control methods based on driving models are vitally important. Status monitoring on CRs contains knowledge in fault detection, intelligence maintenance, and fault‐tolerant control, and multiple objectives need to be met and optimized in the whole drive chain. Moreover, the state‐of‐the‐art is comprehensively summarized, and new insights are also provided to carry on promising researches.

Yaguo Lei

Papers

A nonlinear degradation model based method for remaining useful life prediction of rolling element bearings

A Hybrid Transfer Learning Method for Fault Diagnosis of Machinery under Variable Operating Conditions

Intelligent Fault Diagnosis in Power Plant Using Empirical Mode Decomposition, Fuzzy Feature Extraction and Support Vector Machines

A review for control theory and condition monitoring on construction robots

Accurate identification of Parkinson’s disease by distinctive features and ensemble decision trees