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

Deep partial transfer learning network: A method to selectively transfer diagnostic knowledge across related machines

To diagnose compound faults of locomotive roller bearings accurately, a novel hybrid intelligent diagnosis method is proposed in this paper. First of all, vibration signals are preprocessed to mine valid fault characteristic information. They are filtered and at the same time, they are decomposed by the empirical mode decomposition method and eight intrinsic mode functions (IMFs) are acquired. The filtered signals and IMFs are further demodulated to obtain their Hilbert envelope spectrums. Second, six feature sets are extracted, and they are time- and frequency-domain statistical features of the raw and preprocessed signals. Then, each feature set is evaluated and a few salient features are selected from it by applying the improved distance evaluation technique. Correspondingly, six salient feature sets are obtained. Finally, the six salient feature sets are, respectively, input into six classifiers based on adaptive neurofuzzy inference system (ANFIS), and genetic algorithm is employed to combine the outputs of the six ANFISs and to attain the final diagnosis result. The diagnosis results of the compound faults of the locomotive roller bearings verify that the proposed hybrid intelligent method may accurately recognize not only a single fault and fault severities but also compound faults.

Application of a Novel Hybrid Intelligent Method to Compound Fault Diagnosis of Locomotive Roller Bearings

Adaptive multiwavelets via two-scale similarity transforms for rotating machinery fault diagnosis

This chapter focuses on the remaining useful life (RUL) prediction of rotating machinery. It first gives an explicit description to the concept of RUL prediction and illustrates the major processes of the RUL prediction. Then, two categories of RUL prediction methods, that is, data-driven methods and model-based methods are discussed in details, respectively. In the section of data-driven methods, the commonly strategy of this kind of methods is explained. And a relevance vector machine based prediction method is described as an example. In the section of model-based methods, the classification of the models is first introduced. Then, three prediction methods are described in details, which are based on the exponential model, polynomial model, and Paris–Erdogan model, respectively. By the end of the chapter, the prospects of the RUL prediction for rotating machinery are provided.

Remaining useful life prediction

A new hybrid clustering algorithm based on a three-layer feed forward neural network (FFNN), a distribution density function, and a cluster validity index, is presented in this paper. In this algorithm, both feature weighting and sample weighting are considered, and an optimal cluster number is automatically determined by the cluster validity index. Feature weights are learnt via FFNN based on the gradient descent technique, and sample weights are computed by using the distribution density function of a sample. Feature weighting and sample weighting highlight the importance of sensitive features and representative samples, and simultaneously weaken the interference of insensitive features and vague samples. The presented algorithm is described and applied to the incipient fault diagnosis of locomotive roller bearings. The diagnosis result demonstrates the superior effectiveness and practicability of the algorithm, and shows that it is a promising approach to the fault diagnosis of rotating machinery.

Yaguo Lei

Papers

Deep partial transfer learning network: A method to selectively transfer diagnostic knowledge across related machines

Application of a Novel Hybrid Intelligent Method to Compound Fault Diagnosis of Locomotive Roller Bearings

Adaptive multiwavelets via two-scale similarity transforms for rotating machinery fault diagnosis

Remaining useful life prediction

Fault diagnosis of rotating machinery based on a new hybrid clustering algorithm