Fault detection and diagnosis for non-Gaussian stochastic distribution systems with time delays via RBF neural networks

Systems Science & Control Engineering

2019

Probability density function (PDF) control strategy investigates the controller design approaches where the random variables for the stochastic processes were adjusted to follow the desirable distributions. In other words, the shape of the system PDF can be regulated by controller design.Different from the existing stochastic optimization and control methods, the most important problem of PDF control is to establish the evolution of the PDF expressions of the system variables. Once the relationship between the control input and the output PDF is formulated, the control objective can be described as obtaining the control input signals which would adjust the system output PDFs to follow the pre-specified target PDFs. Motivated by the development of data-driven control and the state of the art PDF-based applications, this paper summarizes the recent research results of the PDF control while the controller design approaches can be categorized into three groups: (1) system model-based direct evolution PDF control; (2) model-based distribution-transformation PDF control methods and (3) data-based PDF control. In addition, minimum entropy control, PDF-based filter design, fault diagnosis and probabilistic decoupling design are also introduced briefly as extended applications in theory sense.

Section: Rational B-spline Modelmentioning

confidence: 99%

Section: Extensions: Minimum Entropy Control Filtering Fault Diagnomentioning

confidence: 99%

An introductory survey of probability density function control

Ren

Systems Science & Control Engineering

2019

“…Filtering design and fault diagnosis are two important problems in non-linear and non-Gaussian systems. Recently, the SDC concept has been applied to investigate these problems [42], [48], [62], [64]- [67].…”

Section: Extensions: Minimum Entropy Control Filtering Fault Diamentioning

confidence: 99%

A Survey of the Probability Density Function Control for Stochastic Dynamic Systems

Ren

2018 24th International Conference on Automation and Computing (ICAC)

2018

Probability density function (PDF) control strategy investigates the controller design approaches in order to to realise a desirable distributions shape control of the random variables for the stochastic processes. Different from the existing stochastic optimisation and control methods, the most important problem of PDF control is to establish the evolution of the PDF expressions of the system variables. Once the relationship between the control input and the output PDF is formulated, the control objective can be described as obtaining the control input signals which would adjust the system output PDFs to follow the pre-specified target PDFs. This paper summarises the recent research results of the PDF control while the controller design approaches can be categorised into three groups: 1) system model-based direct evolution PDF control; 2) model-based distribution-transformation PDF control methods and 3) databased PDF control. In addition, minimum entropy control, PDFbased filter design, fault diagnosis and probabilistic decoupling design are also introduced briefly as extended applications in theory sense.

“…The most important characteristics of the RBF network lie in the fact that its hidden layer neurons have only local reactions of input function, which is in the middle of the basis function. RBF neural network is characterized by simple structure, concise training, and fast learning convergence with the ability to approximate any nonlinear function [24]. Classifier ensemble gives a final result by combining the output of each member classifier through certain fusion algorithm.…”

Section: Lifting Wavelet Packet Transformmentioning

confidence: 99%

Bearing Fault Diagnosis Using a Novel Classifier Ensemble Based on Lifting Wavelet Packet Transforms and Sample Entropy

et al. 2016

Shock and Vibration

In order to improve the fault detection accuracy for rolling bearings, an automated fault diagnosis system is presented based on lifting wavelet packet transform (LWPT), sample entropy (SampEn), and classifier ensemble. Bearing vibration signals are firstly decomposed into different frequency subbands through a three-level LWPT, resulting in a total of 8 frequency-band signals throughout the third layers of the LWPT decomposition tree. The SampEns of all the 8 components are then calculated as feature vectors. Such a feature extraction paradigm is expected to depict complexity, irregularity, and nonstationarity of bearing vibrations. Moreover, a novel classifier ensemble is proposed to alleviate the effect of initial parameters on the performance of member classifiers and to improve classification effectiveness. Experiments were conducted on electric motor bearings considering various set of fault categories and fault severity levels. Experimental results demonstrate the proposed diagnosis system can effectively improve bearing fault recognition accuracy and stability in comparison with diagnosis methods based on a single classifier.