Thank you for reading stochastic processes and filtering theory. Maybe you have knowledge that, people have look numerous times for their favorite novels like this stochastic processes and filtering theory, but end up in harmful downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some infectious bugs inside their computer. stochastic processes and filtering theory is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the stochastic processes and filtering theory is universally compatible with any devices to read.

Stochastic Processes And Filtering Theory

Investigating the impact of data normalization on classification performance

This paper presents a convolutional neural network (CNN) based approach for fault diagnosis of rotating machinery. The proposed approach incorporates sensor fusion by taking advantage of the CNN structure to achieve higher and more robust diagnosis accuracy. Both temporal and spatial information of the raw data from multiple sensors is considered during the training process of the CNN. Representative features can be extracted automatically from the raw signals. It avoids manual feature extraction or selection, which relies heavily on prior knowledge of specific machinery and fault types. The effectiveness of the developed method is evaluated by using datasets from two types of typical rotating machinery, roller bearings, and gearboxes. Compared with traditional approaches using manual feature extraction, the results show the superior diagnosis performance of the proposed method. The present approach can be extended to fault diagnosis of other machinery with various types of sensors due to its end to end feature learning capability.

Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks

In this survey paper, we systematically summarize existing literature on bearing fault diagnostics with deep learning (DL) algorithms. While conventional machine learning (ML) methods, including artificial neural network, principal component analysis, support vector machines, etc., have been successfully applied to the detection and categorization of bearing faults for decades, recent developments in DL algorithms in the last five years have sparked renewed interest in both industry and academia for intelligent machine health monitoring. In this paper, we first provide a brief review of conventional ML methods, before taking a deep dive into the state-of-the-art DL algorithms for bearing fault applications. Specifically, the superiority of DL based methods are analyzed in terms of fault feature extraction and classification performances; many new functionalities enabled by DL techniques are also summarized. In addition, to obtain a more intuitive insight, a comparative study is conducted on the classification accuracy of different algorithms utilizing the open source Case Western Reserve University (CWRU) bearing dataset. Finally, to facilitate the transition on applying various DL algorithms to bearing fault diagnostics, detailed recommendations and suggestions are provided for specific application conditions. Future research directions to further enhance the performance of DL algorithms on health monitoring are also discussed.

Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review

http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac2014/media/files/1993.pdf

Stability of consensus extended Kalman filter for distributed state estimation

This paper presents a stand-alone multisensor wireless system for continuous condition monitoring of induction motors. The proposed wireless system provides a low-cost alternative to expensive condition monitoring technology available through dedicated current signature analysis or vibration monitoring equipment. The system employs multiple sensors (acoustic, vibration, and current) mounted on a common wireless platform. The faults of interest are static and dynamic air-gap eccentricity, bearing damage, and their combinations. The Hilbert-Huang transform of vibration data and power spectral density of current and acoustic signals are used as the features in a hierarchical classifier. The proposed wireless system can distinguish a faulty motor from a healthy motor with a probability of 99.9% of correct detection and less than 0.1% likelihood of false alarm. It can also discriminate between different fault categories and severity with an average accuracy of 95%.

https://www.acsu.buffalo.edu/~ehsanesf/TMECH2013.pdf

Multisensor Wireless System for Eccentricity and Bearing Fault Detection in Induction Motors

In this paper, the problem of distributed state estimation using sensor networks is considered for a very general scenario, where: 1) the process model of the target and the sensing models of local agents are assumed to be linear and time varying; 2) the communication topology between agents is modeled as a general directed graph, and is subject to change with time; and 3) there might exist a time-varying set of agents not directly sensing the target. A distributed hybrid information fusion (DHIF) algorithm, which requires no global parameter and only one communication iteration per time instant, is proposed. The DHIF algorithm computes confident but consistent estimates. The convergence of the proposed algorithm is guaranteed with very mild sufficient conditions formulated in such a general scenario. Specifically, the directed switching graphs do not necessarily need to be (jointly) strongly connected nor balanced. Then the conditions are shown to be “almost” necessary. In the special case where the process/sensing models and the topology are both time invariant, the conditions are necessary after certain relaxation. Comparisons with existing algorithms are shown both analytically and numerically. The convergence results are illustrated in simulations. In the end, the proposed algorithm is also extended to the situation with nonlinearities involved, and illustrated in simulation.

On the Convergence Conditions of Distributed Dynamic State Estimation Using Sensor Networks: A Unified Framework

The stealthy attack, as a strategically designed false data injection attack against the power system state estimation mechanism, is able to let the corrupted measurements bypass residue-based bad data detectors with the same probability as that of uncorrupted measurements and is able to fool the system operator with the deviated estimates. While most of the existing articles assume the network topology to be fixed, the effects of switching topologies on such an attack are shown in this paper. A new mechanism is proposed to eliminate the possibility of such an attack via strategically shutting down some preselected transmission lines by turns and therefore switching the network topologies. The necessary and sufficient condition to achieve such elimination and the general form of possible attacks when the elimination is impossible are both formulated. The case where the attack is only stealthy in a subset of the preselected topologies is also studied. The general form of the possible estimate deviations caused by this “partially” stealthy attack is derived. Simulations and case studies are provided using different IEEE bus systems to show the efficiency of the proposed strategy, to discuss the countermeasures in the case when there always exist possible stealthy attacks, and to show how the possible deviations introduced by a “partially” stealthy attack could be affected by the decisions made by both the attacker and the system operator.

Effects of Switching Network Topologies on Stealthy False Data Injection Attacks Against State Estimation in Power Networks

This paper studies the problem of distributed state estimation in the scenario where the process model of interest is not certain to any local agent. By extending the previously proposed distributed hybrid information fusion (DHIF) algorithm to this considered scenario, two algorithms are proposed by following the frameworks of two well-known multiple-model (MM) approaches, namely, the first-order generalized pseudo-Bayesian and interacting MM approaches. The extended algorithms inherit the advantages of the original DHIF algorithm and are hence fully distributed, robust against agents not directly sensing the target, and only require a single communication iteration among agents during each sampling interval. It is also shown in the case when the unknown underlying model/mode is fixed; all local agents are able to asymptotically identify the true underlying model/mode and estimate the state of interest simultaneously. Sufficient conditions are formulated. Simulations are shown to illustrate the analytical results as well as the performances of the proposed algorithms.

Fully Distributed Dynamic State Estimation With Uncertain Process Models

The stealthy false data injection attack against the state estimation in a power network is able to compromise the measurements and fool the system operator without being detected by the residue based detector. While the most of the existing articles assume the network topology to be fixed, we show the effects of unfixed topologies on such attacks. Motivated by such effects, we propose a new mechanism in which the system operator is trying to eliminate the possibility of such attacks via strategically shutting down some of the transmission lines by turns and therefore switching the network topologies. We give the conditions that have to be satisfied in order to achieve this elimination. The vulnerable measurements in the network are formulated in the case when this elimination is impossible. Some simulation analysis are shown as well.

Shaocheng Wang

Papers

Multisensor Wireless System for Eccentricity and Bearing Fault Detection in Induction Motors

On the Convergence Conditions of Distributed Dynamic State Estimation Using Sensor Networks: A Unified Framework

Effects of Switching Network Topologies on Stealthy False Data Injection Attacks Against State Estimation in Power Networks

Fully Distributed Dynamic State Estimation With Uncertain Process Models

Stealthy false data injection attacks against state estimation in power systems: Switching network topologies