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

RUL prediction of machinery using convolutional-vector fusion network through multi-feature dynamic weighting

Clustering algorithms can automatically recognize the pattern inside the data so as to analyze the collected data without their labels. Using this advantage, three clustering-based fault diagnosis methods are presented to deal with some diagnosis cases of rotating machinery in which the labeled data are limited. In the first method, compensation distance evaluation technique and the weight K nearest neighbor are used to recognize the mechanical faults, harnessing the merits that the computation of feature weights is simpler and the weights are easier to understand. The second method is presented based on weight fuzzy c -means, which is robust to the local structure of the data and reflects the level of uncertainty over the most appropriate assignment. Finally, a Hybrid clustering algorithm–based fault diagnosis method is introduced, considering the problems like the sample influence for clustering and the automatic setting of the cluster number. The results of the diagnosis cases verify that these diagnosis methods take full advantage of unlabeled data and reduce the human labor in fault diagnosis.

Clustering algorithm–based fault diagnosis

Time domain averaging(TDA) is essentially a comb filter, it cannot extract the specified harmonics which may be caused by some faults, such as gear eccentric Meanwhile, TDA always suffers from period cutting error(PCE) to different extent Several improved TDA methods have been proposed, however they cannot completely eliminate the waveform reconstruction error caused by PCE In order to overcome the shortcomings of conventional methods, a flexible time domain averaging(FTDA) technique is established, which adapts to the analyzed signal through adjusting each harmonic of the comb filter In this technique, the explicit form of FTDA is first constructed by frequency domain sampling Subsequently, chirp Z-transform(CZT) is employed in the algorithm of FTDA, which can improve the calculating efficiency significantly Since the signal is reconstructed in the continuous time domain, there is no PCE in the FTDA To validate the effectiveness of FTDA in the signal de-noising, interpolation and harmonic reconstruction, a simulated multi-components periodic signal that corrupted by noise is processed by FTDA The simulation results show that the FTDA is capable of recovering the periodic components from the background noise effectively Moreover, it can improve the signal-to-noise ratio by 79 dB compared with conventional ones Experiments are also carried out on gearbox test rigs with chipped tooth and eccentricity gear, respectively It is shown that the FTDA can identify the direction and severity of the eccentricity gear, and further enhances the amplitudes of impulses by 35% The proposed technique not only solves the problem of PCE, but also provides a useful tool for the fault symptom extraction of rotating machinery

Flexible time domain averaging technique

Event-based cameras are the emerging bio-inspired technology in vision sensing. Different from the traditional standard cameras, the event-based cameras asynchronously record the brightness change per pixel, and have the great merits of high temporal resolution, low energy consumption, high dynamic range, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc</i> . While the event-based cameras have been initially exploited in several common vision-based tasks in the recent years, the investigation on machine condition monitoring problem is quite limited. This paper offers the first attempt in the current literature on exploring the contactless event vision data for machine fault diagnosis. A vibration event representation is proposed to transform the event records into typical data samples, and a deep convolutional neural network model is used for processing the event information. To enhance the model robustness against environmental noisy vision events, an event data augmentation method is proposed to introduce variations of the event patterns. A deep representation clustering method is further proposed to improve the pattern recognition performance with respect to different machine health conditions. Experiments on the event vision-based rotating machine fault diagnosis problem are carried out. It is extensively validated that high fault diagnosis accuracies can be obtained using the vision data from the event-based cameras, which are competitive with the popular accelerometer data. Considering the properties of flexibility, portability and data recognizability, the event-based cameras thus provide a promising new tool for contactless machine health condition monitoring and fault diagnosis. 

Intelligent Machinery Fault Diagnosis With Event-Based Camera

In intelligent fault diagnosis, unsupervised feature learning is a potential tool to replace the manual feature extraction in big data era. Therefore, we first develop a locally connected restricted Boltzmann machine (LCRBM) from the traditional RBM in order to handle the periodic appearance of fault characteristics in the raw signals of rotating machinery. Then, using LCRBM, we propose a method for intelligent fault diagnosis of rotating machinery. In the method, LCRBM is used to obtain features directly from raw signals. Based on the features learned by LCRBM, the method uses softmax regression to recognize faults. The proposed method is verified by the dataset of locomotive bearings and its superiority is demonstrated by the comparison with methods using the traditional RBM and eighteen widely used manual features. Results indicate that the proposed method is able to automatically learn fine features from raw signals of rotating machinery and achieves higher diagnosis accuracies.

Yaguo Lei

Papers

RUL prediction of machinery using convolutional-vector fusion network through multi-feature dynamic weighting

Clustering algorithm–based fault diagnosis

Flexible time domain averaging technique

Intelligent Machinery Fault Diagnosis With Event-Based Camera

Intelligent fault diagnosis of rotating machinery using locally connected restricted boltzmann machine in big data era