Multi-signal Model Research Articles

Diagnosis, Diagnosticability Analysis, and Test Point Design for Multiple Faults Based on Multisignal Modeling and Blind Source Separation

An effective strategy for analyzing and diagnosing multiple faults is developed, based on the concise causality structure obtained by multisignal modeling and the fault source signals extracted by blind source separation (BSS). The key idea is enlightened by the need to handle the redundant test signals and the multiple fault ambiguity groups when applying multisignal modeling for multiple fault diagnosis. Considering that BSS is inherently suitable to extract the independent source information, it is integrated into the multisignal model to reconstruct the causality structure that will have superior diagnosticability. Preliminary study on test point design is also presented in the proposed strategy. The proposed multiple fault diagnosis strategy has been verified on a hydraulic automatic gauge control simulation system in a cold rolling mill. Results show that it can use less test information to effectively diagnose all simulated single and multiple faults.

DCNN-Based Multi-Signal Induction Motor Fault Diagnosis

Deep learning (DL) architecture, which exploits multiple hidden layers to learn hierarchical representations automatically from massive input data, presents a promising tool for characterizing fault conditions. This paper proposes a DL-based multi-signal fault diagnosis method that leverages the powerful feature learning ability of a convolutional neural network (CNN) in images. The proposed deep model is able to learn from multiple types of sensor signals simultaneously so that it can achieve robust performance and finally realize accurate induction motor fault recognition. First, the acquired sensor signals are converted to time–frequency distribution (TFD) by wavelet transform. Then, a deep CNN is applied to learning discriminative representations from the TFD images. Since then, a fully connected layer in deep architecture gives the prediction of induction motor condition based on learned features. In order to verify the effectiveness of the designed deep model, experiments are carried out on a machine fault simulator where both vibration and current signals are analyzed. Experimental results indicate that the proposed method outperforms traditional fault diagnosis methods, hence, demonstrating effectiveness in induction motor application. Compared with conventional methods that rely on delicate features extracted by experienced experts, the proposed deep model is able to automatically learn and select suitable features that contribute to accurate fault diagnosis. Compared with single-signal input, the multi-signal model has more accurate and stable performance and overcomes the overfitting problem to some degree.

Multimodal image fusion with adaptive joint sparsity model

An adaptive joint sparsity model (JSM) is presented for multimodal image fusion. As a multisignal modeling technique, JSM, which is derived from distributed compressed sensing, has been successfully employed in multimodal image fusion. In traditional JSM-based fusion, a single dictionary learned by K-singular value decomposition (SVD) has higher coherence yet may result in potential visual confusion and misleading. In the proposed model, we first learn a plurality of subdictionaries and use a supervised classification approach based on gradient information. Then, one of the learned subdictionaries is adaptively applied to JSM to obtain the common and innovative sparse coefficients.. Finally, the fused image is reconstructed by the fused sparse coefficients and the adaptive dictionary. Infrared-visible images and medical images were selected to test the proposed approach. The results were compared with those of traditional methods, such as the multiscale transform-based methods, JSM-based method, and adaptive sparse representation (ASR) model-based method. Experimental results on multimodal images demonstrate that the proposed fusion method can obtain better performance than the conventional JSM-based method and ASR-based method in terms of both visual quality and objective assessment.

Fault Diagnosis Reasoning Algorithm Based on Multi-signal Model

The multi-signal model is modeled in the fault space and combines the structural model and the dependent model of the system. The modeling work is easy to implement in different layers and is very suitable for fault modeling and fault diagnosis of the Command and control system. After the model is established, how to meet the requirements of fault coverage and fault isolation rate, fault diagnosis reasoning algorithm is particularly important. The system dependency matrix is obtained by establishing a multi-signal model. Based on this, the fault diagnosis reasoning algorithm is studied. The fault diagnosis of Apollo spacecraft before launch is taken as an example to verify the effectiveness of fault diagnosis inference algorithm based on multi-signal model.

A Research on the Electrical Test Fault Diagnostic and Data Mining of a Manned Spacecraft

The paper introduces the modeling method and modeling tool for the fault diagnosis of manned spacecraft, the multi-signal flow graph model of a manned space equipment was established using this method; the framework of the fault detection and diagnosis system of manned spacecraft is proposed, the function of ground system and function of the spacecraft are clearly defined. The structure of the functional module is given separately; finally, the tool builds the fault detection and diagnosis system, the application of fault diagnosis method for manned spacecraft is used for reference.

Error-Rate Estimation Based on Multi-Signal Flow Graph Model and Accelerated Radiation Tests.

A method of evaluating the single-event effect soft-error vulnerability of space instruments before launched has been an active research topic in recent years. In this paper, a multi-signal flow graph model is introduced to analyze the fault diagnosis and meantime to failure (MTTF) for space instruments. A model for the system functional error rate (SFER) is proposed. In addition, an experimental method and accelerated radiation testing system for a signal processing platform based on the field programmable gate array (FPGA) is presented. Based on experimental results of different ions (O, Si, Cl, Ti) under the HI-13 Tandem Accelerator, the SFER of the signal processing platform is approximately 10−3(error/particle/cm2), while the MTTF is approximately 110.7 h.

Nanoengineered materials to control cell fate.

The enormous variety and specificity of cell functions observed in vivo are the result of multifaceted interplays occurring between adjacent cells or between cells and the extracellular environment. Arrays of signals whose presentation is tightly controlled in time and space regulate different aspects of cell behavior including differentiation, quiescence, proliferation and biosynthesis [1]. The integration of such a complex set of information within synthetic platforms has acquired large interest from the scientific community in order to fabricate biomaterials and devices that precisely control cell functions and fate in vitro. The field of functional biointerfaces is widely interdisciplinary, spanning from biochemistry, molecular and cell biology, microand nanotechnologies, material synthesis and functionalization. The successful translation of the scientific findings into the engineering of devices for practical biotechnological applications relies on integrating those disciplines. In this context, diverse but complementary contributions need to be integrated into new biomaterials, that is, multisignal patterning methodologies, multiscale modeling, advanced characterization and processing technologies. These should be optimized and match the desired biomedical or biotechnological applications. Thus, the concept of functional biointerfaces is rapidly evolving and constantly gaining new valences [2,3]. When a cell lands on a material surface, an integrin-mediated material recognition occurs and clusters of integrins assemble in the form of punctuate-wide nascent adhesions (<0.2 μm). If the surface characteristics are favorable, in terms of ligand stability and density, these adhesions can mature in larger focal complexes up to focal adhesions (FAs, 1.0–10.0 μm) [4]. The actomyosin cytoskeleton is anchored to the adhesion that provides a mechanical continuity with the extracellular environment by transmitting forces from the inside of the cytoplasm and vice versa. Therefore, patterned surface features, that is, topography, biochemical ligands or mechanical properties, with dimensions that interfere with the establishment and dynamics of the adhesion process, can control the mechanical identity of the cytoskeleton and such a pathway is usually referred to as the material–cytoskeleton crosstalk [5]. In this context, the capability of manipulating material properties at the nanoscale is key central. Surfaces can be nanoengineered in order to affect adhesion plaque size, orientation and shape, thus affecting the mechanical identity of the cell through its cytoskeletal assembly. Additionally, contractile forces generated by the cytoskeleton may alter nuclear stress state and shape, which has a direct influence on chromatin dynamics [6,7]. Therefore, chromatin might directly perceive material features through the FAs–cytoskeleton– Nanoengineered materials to control cell fate

Method of testability evaluation using hierarchical testability model

The layered and modularized design is always used in complex electronic equipment,where the maintenance level and test requirement are both characterized by hierarchy configuration. Therefore,a novel evaluation approach was presented based on hierarchical testability model. The multi-signal flow graph model has ability to present multi-fault factors,according to the hierarchical classification based on maintenance level and physical structure of equipment,the multi-signal flow graph model could be established for each level respectively. For any part which can't be tested or maintained,the multi-signal flow graph model would be established on lower level; thereby the hierarchical testability model was built up. The hierarchical dependence matrix was obtained based on above model,and then the testing level of equipment on each maintenance level was obtained by using the mathematical model of testability evaluation. The analysis results of example prove that this method could effectively evaluate the testability levels of equipments in different situations,at the same time this method could provide available guidance and assistance for the design of testability and maintenance.

Fault Diagnosis Method of Vehicle Power System Using Bayesian Network

The fault diagnosis of vehicle power system that the structure and characteristics of components are complex, each module and internal modules exist coupling, cross-linked mutual relations and the uncertainties, the system status and working conditions are difficult to describe by precisely mathematical model, and test cost expensive, less fault samples. Thus its fault diagnosis is the decision problem of uncertain information in a small sample. it is proposed that combining multi-signal flow graph model with Bayesian network fault diagnosis method. The fault diagnosis model of power system and the corresponding Bayesian network structure are built, which achieve the fault diagnosis of power system, Diagnosis example shows that the method of the vehicle power has a higher failure troubleshooting capabilities of the system single and multiple faults.

Research of AMU Unit Fault Testability Based on Multi-Signal Modeling and Fuzzy Decision

The Multi-signal Flow Graph (MFG) is a simple and effective system modeling methodology, widely used in testability analysis and fault diagnosis field. In order to shorten the maintenance time and reduce the influence of human factors, the MFG method was introduced, and it was used to set up the testability model of aircraft Audio Management Unit (AMU) and found the fault-testability dependency matrix and the fault-fault dependency matrix. Based on dependency matrix and real unit fault message, the trapezoidal fuzzy number algorithm was introduced, thus the new fault diagnosis method was generated. Finally, an example proves that the fault diagnosis algorithm can not only locate the fault more accurately, but also improve the maintenance efficiency.

Overview of Current Methods in Sedimentation Velocity and Sedimentation Equilibrium Analytical Ultracentrifugation

AbstractModern computational strategies have allowed for the direct modeling of the sedimentation process of heterogeneous mixtures, resulting in sedimentation velocity (SV) size‐distribution analyses with significantly improved detection limits and strongly enhanced resolution. These advances have transformed the practice of SV, rendering it the primary method of choice for most existing applications of analytical ultracentrifugation (AUC), such as the study of protein self‐ and hetero‐association, the study of membrane proteins, and applications in biotechnology. New global multisignal modeling and mass conservation approaches in SV and sedimentation equilibrium (SE), in conjunction with the effective‐particle framework for interpreting the sedimentation boundary structure of interacting systems, as well as tools for explicit modeling of the reaction/diffusion/sedimentation equations to experimental data, have led to more robust and more powerful strategies for the study of reversible protein interactions and multiprotein complexes. Furthermore, modern mathematical modeling capabilities have allowed for a detailed description of many experimental aspects of the acquired data, thus enabling novel experimental opportunities, with important implications for both sample preparation and data acquisition. The goal of the current unit is to describe the current tools for the study of soluble proteins, detergent‐solubilized membrane proteins and their interactions by SV and SE. Curr. Protoc. Protein Sci. 71:20.12.1‐20.12.49. © 2013 by John Wiley &amp; Sons, Inc.

Research on Fault Diagnosis Based on TEAMS

The paper focuses on introducing the basic condition of the TEAMS and the basic design process of multi-signal modeling and diagnostic strategy. Moreover, it introduces the applications of TEAMS in fault diagnosis from three aspects: model establishment, performance analysis and diagnostic strategy. The superiority and effectiveness of above-mentioned method is verified by an instance and the results are useful to the fault diagnosis of complex electronic equipment.

A Modified Simulation-Based Multi-Signal Modeling for Electronic System

Multi-signal model is an effective modeling method applicable to large scale complex system. In this paper, an improved simulation-based modeling method for parametric fault is put forward. Two times of Monte-Carlo simulation are done in normal tolerance. Based on the estimated sample variance obtained from the first Monte-Carlo simulation, the analysis runs of the second Monte-Carlo is determined empirically to reduce the simulation cost. Then the statistical distribution of data from the second Monte-Carlo simulation is judged qualitatively by normality test. After that, an adaptive method is adopted to estimate the threshold range for signal feature in normal state, which can improve modeling precision. At last, the effectiveness of the proposed method is verified and the performances are compared to manifest the advantage of the proposed method. The work in this paper is valuable for future further research of complex system test and diagnosis based on multi-signal.

Research on Design for Testability of Marine Diesel Engine

The rapid development of manufacturing industry making the pursuit of equipment performance increasingly demanding. Proceeded from design, considering testing and diagnosis problems from whole system and life cycle is an effective way to avoid major accidents and reduce the cost of maintenance, therefore, testability is widespread concerned. In this paper, taken marine diesel engine as research object, analyzed its necessity and feasibility of design for testability (DFT), through fully study of marine diesel engine’s testability characteristics, introduced multi-signal model to analysis marine diesel engine’s lubrication system testability, then advanced improvements for its testability, it can detect and isolate all faults of lubrication system, which provided a reference guide to marine diesel engine’s manufacturing.

Integrating point glacier mass balance observations into hydrologic model identification

Abstract. The hydrology of high mountainous catchments is often predicted with conceptual precipitation-discharge models that simulate the snow accumulation and ablation behavior of a very complex environment using as only input temperature and precipitation. It is hereby often assumed that some glacier-wide annual balance estimates, in addition to observed discharge, are sufficient to reliably calibrate such a model. Based on observed data from Rhonegletscher (Switzerland), we show in this paper that information on the seasonal mass balance is a pre-requisite for model calibration. And we present a simple, but promising methodology to include point mass balance observations into a systematic calibration process. The application of this methodology to the Rhonegletscher catchment illustrates that even small samples of point observations do contain extractable information for model calibration. The reproduction of these observed seasonal mass balance data requires, however, a model structure modification, in particular seasonal lapse rates and a separate snow accumulation and rainfall correction factor. This paper shows that a simple conceptual model can be a valuable tool to project the behavior of a glacier catchment but only if there is enough seasonal information to constrain the parameters that directly affect the water mass balance. The presented multi-signal model identification framework and the simple method to calibrate a semi-lumped model on point observations has potential for application in other modeling contexts.

System Testability Analysis for Complex Electronic Devices Based on Multisignal Model

It is necessary to consider the system testability problems for electronic devices during their early design phase because modern electronic devices become smaller and more compositive while their function and structure are more complex. Multisignal model, combining advantage of structure model and dependency model, is used to describe the fault dependency relationship for the complex electronic devices, and the main testability indexes (including optimal test program, fault detection rate, fault isolation rate, etc.) to evaluate testability and corresponding algorithms are given. The system testability analysis process is illustrated for USB-GPIB interface circuit with TEAMS toolbox. The experiment results show that the modelling method is simple, the computation speed is rapid and this method has important significance to improve diagnostic capability for complex electronic devices.

Diagnosis knowledge representation and interference - Understanding fundamental ideas behind graphical models

In this article, we presented three graphical modeling techniques for diagnostic knowledge representation and inference: behavioral Petri nets (BPNs), multisignal flow graphs, and Bayesian networks (BNs). By using the same example from (Portinale, 1997) we showed that both multisignal flow graph model and BN model yield the same diagnosis. In addition, we showed that the P-invariant concept in BPN is similar to the D-separation concept in BNs

On‐line monitoring and diagnosis for satellite telemetry data

PurposeTo improve the speed and precise of online monitoring and diagnosis for satellite using satellite telemetry data.Design/methodology/approach – In monitoring system, a fuzzy range which gives the probability of alarm for telemetry channels using fuzzy reasoning is outlined. A failure confidence factor is presented to modify the traditional real‐time diagnosis algorithm based on multisignal model to describe the relative failure possibility for suspected components. According to the modified real‐time diagnosis algorithm based on multisignal model, it rapidly generates the states for all the components of the system such as good, bad, suspected and unknown. Then the failure probability for suspected components is obtained by Mamdani fuzzy reasoning algorithm.Findings – The experimental results reveal that the diagnosis system can not only improve diagnosis of speed but also can improve the diagnostic precision by giving failure probability for suspected fault components which may be potential failure components.Research limitations/implications – It requires the clear fault dependency relationship between components and tests.Practical implications – A very useful method for researchers and engineers who are engaged in satellite online monitoring and diagnosis.Originality/value – This paper presents a new method combining multisignal model and fuzzy theory to give the failure probability for suspected components which improves the speed and precision for fault diagnosis.

Reducing information asymmetries in market entry decisions

A Bayesian belief network is adopted to structure and manage an entry decision problem, where a potential entrant has to evaluate the profitability of a given market, depending on an established firm's unknown and unobservable multidimensional type. A multiperiod multisignal model is defined to take account of strategic interaction processes between the incumbent and the potential competitor. Since significant theoretical and computational issues prevent us from finding optimal strategies, a myopic policy is discussed that leads to a reasonable outcome. An application example of the proposed solution procedure is also presented.