To perform indicator selection and verification for the on-orbit fault reconstruction of a giant satellite swarm, hybrid multi-objective algorithm is proposed then verified by Monte Carlo analysis. First, according to failure analysis several optimization indicators, such as health state total energy consumption reconstruction, balance fuel consumption, are proposed. Then, fitness function cons...

The periodical transient impulses caused by localized faults are sensitive and important characteristic information for rotating machinery fault diagnosis. However, it is very difficult to accurately extract transient impulses at the incipient fault stage because the fault impulse features are rather weak and always corrupted by heavy background noise. In this paper, a new transient impulse ext...

To accurately diagnose the mechanical failure of the fan, two diagnostic methods based on the wavelet packet energy feature and sample entropy feature are proposed. Vibration signals acquisition of 13 kinds of running states are achieved on the 4-73 No. 8D centrifugal fan test bench. The wavelet packet energy feature vector of each vibration signal is rapidly extracted through the wavelet packe...

This study proposes an investigation of a novel thermal image enhancement based on bi-dimensional empirical mode decomposition (BEMD) and applies this method for rotating machinery fault diagnosis system. In this work, thermal images of machine conditions are firstly decomposed into intrinsic mode functions (IMFs) by utilizing BEMD. At each decomposition level, the IMF is expanded and fused wit...

Root cause analysis is an important method for fault diagnosis when used with multivariate statistical process monitoring (MSPM). Conventional contribution analysis in MSPM can only isolate the effects of the fault by pinpointing inconsistent variables, but not the underlying cause. By integrating reconstruction-based multivariate contribution analysis (RBMCA) with fuzzy-signed directed graph (...

In this study, a novel fault diagnosis system for rotating machinery using thermal imaging is proposed. This system consists of bi-dimensional empirical mode decomposition (BEMD) for image enhancement, a generalized discriminant analysis (GDA) for feature reduction, and a relevance vector machine (RVM) for fault classification. Firstly, the thermal image obtained from machine conditions is deco...

Certain signal reconstruction problems can be understood in terms of frames and redundant representations. The redundancy is useful because it leads to robust signal representations, that is, representations in which partial loss of data can be tolerated without misbehavior or adverse effects. This chapter begins by presenting a few engineering problems in which robust data representations play...

This paper presents a new technique for fault diagnosis and estimation of unknown inputs in a class of nonlinear systems. The novelty of the approach is governed by the use of two interconnected sliding mode observers. The first of the two observers is used for fault diagnosis and the second is used for the reconstruction of unknown inputs. The two observers exchange their respective reconstruc...

In this article, a parallel radial basis function network in conjunction with chaos theory (CPRBF network) is presented, and applied to practical fault detection for hydraulic pump, which is a critical component in aircraft. The CPRBF network consists of a number of radial basis function (RBF) subnets connected in parallel. The number of input nodes for each RBF subnet is determined by differen...

This paper describes using Proper Orthogonal Decomposition (POD) method to create low-order dynamical models for the Model Filter component of Beacon-based Exception Analysis for Multi-missions (BEAM). The POD modeling procedure is described, and its usefulness in creating simple low-order dynamical models of a complex system. The POD procedure will be shown on an example problem of a Burgers’ ...