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

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Table Of Integrals Series And Products

Recently, to ensure the reliability and safety of modern large-scale industrial processes, data-driven methods have been receiving considerably increasing attention, particularly for the purpose of process monitoring. However, great challenges are also met under different real operating conditions by using the basic data-driven methods. In this paper, widely applied data-driven methodologies suggested in the literature for process monitoring and fault diagnosis are surveyed from the application point of view. The major task of this paper is to sketch a basic data-driven design framework with necessary modifications under various industrial operating conditions, aiming to offer a reference for industrial process monitoring on large-scale industrial processes.

A Review on Basic Data-Driven Approaches for Industrial Process Monitoring

This paper provides an overview of the recent developments in data-based techniques focused on modern industrial applications. As one of the hottest research topics for complicated processes, the data-based techniques have been rapidly developed over the past two decades and widely used in numerous industrial sectors nowadays. The core of data-based techniques is to take full advantage of the huge amounts of available process data, aiming to acquire the useful information within. Compared with the well-developed model-based approaches, data-based techniques provide efficient alternative solutions for different industrial issues under various operating conditions. The main objective of this paper is to review and summarize the recent achievements in data-based techniques, especially for complicated industrial applications, thus providing a referee for further study on the related topics both from academic and practical points of view. This paper begins with a brief evolutionary overview of data-based techniques in the last two decades. Then, the methodologies only based on process measurements and the model-data integrated techniques will be further introduced. The recent developments for modern industrial applications are, respectively, presented mainly from perspectives of monitoring and control. The new trends of data-based technique as well as potential application fields are finally discussed.

Data-Based Techniques Focused on Modern Industry: An Overview

Space-Time Block Coding for Wireless Communications

This paper focuses on rolling elements bearing faults detection in induction machine based on stator currents monitoring Specifically, it proposes to process the stator currents using Hilbert-Huang transform This approach is composed of two steps First, the empirical mode decomposition is used in order to estimate the intrinsic mode functions (IMFs), then the Hilbert transform is employed to compute the instantaneous amplitude (IA) and instantaneous frequency (IF) The energy of the instantaneous amplitude of the IMFs is used as fault indicator The proposed approach is used for bearing fault detection in induction machine at several fault degrees The effectiveness of the proposed Hilbert-Huang Transform technique is verified by a series of experimental tests corresponding to different bearing fault conditions

Induction machine bearing faults detection based on Hilbert-Huang transform

Development of large scale offshore wind and marine current turbine farms implies to minimize and predict maintenance operations. In direct- or indirect-drive, fixed- or variable-speed turbine generators, advanced signal processing tools are required to detect and diagnose the generator faults from vibration, acoustic, or generator current signals. The induction generator is traditionally used for wind turbines power generation. Even if induction machines are highly reliable, they are subjected to many types of faults. The aim then, is to detect them at an early stage in order to prevent breakdowns and consequently ensure the continuity of power production. In this context, this paper deals with wind turbines condition monitoring using a fault detection technique based on the generator stator current. The detection algorithm uses a recursive maximum likelihood estimator to track the time-varying fault characteristic frequency and the related energy. Furthermore, a decision-making scheme and a related criterion are proposed. The feasibility of the proposed approach has been demonstrated using simulation data issued from coupled magnetic circuits induction generator model driven by a wind turbine for both electrical asymmetry and mechanical imbalance.

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Non-stationary spectral estimation for wind turbine induction generator faults detection

The blind recognition of communication parameters is an important research topic in both commercial and civilian systems. In this paper, we investigate the blind recognition of the modulation. Currently most part of the existing algorithms assumes that the transmitter uses a single-antenna. This study extends the problem for multiple-antennas (MIMO) systems. We adopt a Maximum Likelihood approach for the blind recognition of the modulation and we consider two different situations. First, we assume the channel knowledge at the receiver side and we expose the optimal solution which is called Average Likelihood Ratio Test (ALRT). Then, we relax this assumption and we propose a second method based on a Hybrid Likelihood Ratio Test (HLRT).

Modulation Recognition for MIMO Communications

Smart grids have urged a radical reappraisal of distribution networks and power quality requirements, which demand to continuously monitor grid conditions. This allows for early classification of power quality degeneration. In this context, this paper proposes an improved algorithm for phasor estimation and fault type classification in smart grid applications. The proposed algorithm is specifically composed of two steps: first, the phasor estimation from the input signals, which can be executed on the Phasor Measurement Unit (PMU). While step two concerns the fault type classification using the pha-sor estimation. Specifically, we propose to estimate the phasor parameters, (frequency, amplitude and initial phase) using least square method. Then, fault type classification is obtained using symmetrical component algorithm. The proposed algorithm can be used for the identification of power quality disturbances. It has been evaluated, compared, and studied with simulated data. Simulation results show accurate phasor estimations and accurate fault type classification in presence of noise.

An improved algorithm for power system fault type classification based on least square phasor estimation

This paper deals with a new classification techniques for power quality analysis. Specifically, the proposed technique aims at discriminating between four classes, where each class depends on the number of non-zero symmetrical components. By reformulating the classification problem as a pure model order selection one, we propose a classifier based on Information Theoretical Criteria. The performances of this proposed classifier are evaluated using Monte Carlo simulations with synthetic three-phase signals. Simulation results illustrate the effectiveness of the proposed classifier for power quality disturbances classification.

/pdf/classification-of-three-phase-power-disturbances-based-on-20jm7km0wz.pdf

Vincent Choqueuse

Papers

Induction machine bearing faults detection based on Hilbert-Huang transform

Non-stationary spectral estimation for wind turbine induction generator faults detection

Modulation Recognition for MIMO Communications

An improved algorithm for power system fault type classification based on least square phasor estimation

Classification of three-phase power disturbances based on model order selection in smart grid applications