A novel approach of testability modeling and analysis for PHM systems based on failure evolution mechanism

Tan, Xiaodong; Qiu, Jing; Liu, Guanjun; Li, Kehong; Yang, Shuming; Wang, Chao

doi:10.1016/j.cja.2013.04.044

Cited by 22 publications

(9 citation statements)

References 22 publications

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“…First, by means of failure modes, evolution mechanisms, effects and criticality analysis (FMEMECA) [16], an FEMM can be constructed. Based on the model information, a method of testability analysis for PHM is proposed and PHM-related performance indices can be obtained.…”

Section: Tso Model 31 Tso Frame For Phm Systemsmentioning

confidence: 99%

“…This paper proposes a novel approach of hierarchical testability modeling based on failure evolution mechanism. At the component level, the information related to failure evolution and fault-symptom dependency can be obtained by using FMEMECA [16]. At the system level, the dynamic attributes of elements are assigned by using bond graph methodology, and the failure propagation paths are constructed by means of the functional flow method [21][22][23][24].…”

Section: Femmmentioning

confidence: 99%

“…Thus, the dependency of fault symptom parameters and tests can be achieved according to the requirements for test placement. Based on the above information, failure evolution mechanism model (FEMM) can be generated [16]. It can express the fault evolution-test dependency in the overall system by means of fault-symptom parameter and symptom-test parameter, and describe the fault propagation time and fault severity, as well as establish the attributes of TE and sensitivity of the test for fault in the fault evolution process.…”

Section: Introductionmentioning

confidence: 99%

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Test selection and optimization for PHM based on failure evolution mechanism model

Qiu

Tan

Liu

et al. 2013

J. of Syst. Eng. Electron.

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The test selection and optimization (TSO) can improve the abilities of fault diagnosis, prognosis and health-state evaluation for prognostics and health management (PHM) systems. Traditionally, TSO mainly focuses on fault detection and isolation, but they cannot provide an effective guide for the design for testability (DFT) to improve the PHM performance level. To solve the problem, a model of TSO for PHM systems is proposed. Firstly, through integrating the characteristics of fault severity and propagation time, and analyzing the test timing and sensitivity, a testability model based on failure evolution mechanism model (FEMM) for PHM systems is built up. This model describes the fault evolutiontest dependency using the fault-symptom parameter matrix and symptom parameter-test matrix. Secondly, a novel method of inherent testability analysis for PHM systems is developed based on the above information. Having completed the analysis, a TSO model, whose objective is to maximize fault trackability and minimize the test cost, is proposed through inherent testability analysis results, and an adaptive simulated annealing genetic algorithm (ASAGA) is introduced to solve the TSO problem. Finally, a case of a centrifugal pump system is used to verify the feasibility and effectiveness of the proposed models and methods. The results show that the proposed technology is important for PHM systems to select and optimize the test set in order to improve their performance level.

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Section: Tso Model 31 Tso Frame For Phm Systemsmentioning

confidence: 99%

Section: Femmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Test selection and optimization for PHM based on failure evolution mechanism model

Qiu

Tan

Liu

et al. 2013

J. of Syst. Eng. Electron.

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“…[6] , respectively. According to the requirements of SOSM for gearbox health monitoring, the paper proposes an optimal model which maximizes the fault trackability and minimizes the test cost based on fault detectability and trackability of sensors and with a constraint that FDR and FIR are greater than FDR* and FIR* [7] .…”

Section: The Sosm For Health Monitoringmentioning

confidence: 99%

“…The main purpose of this paper is to select an optimal sensor set SS* from SS on the basis of meeting the scheduled testability requirements that FDR and FIR are greater than 98% and 94%, respectively. The above SOSM is solved using AGASA introduced by tan et al, [6] and the solution process is implemented in MATLAB. The control parameters are initialized as follows: the weight factors of the two objective functions w 1 =w 2 =w 3 =1/3; the population size PopSize=40; the crossover probability P c =0.95 and the mutation probability P m =0.01; the initial temperature T 0 =100; the cooling rate k=0.98.…”

Section: Case Studymentioning

confidence: 99%

Sensor Optimization Selection Based on Fault Detectability and Trackability

Luo¹,

Tan²

2015

Proceedings of the 2015 Joint International Mechanical, Electronic and Information Technology Conference

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Abstract. Correctly selecting and reasonably arranging sensors are critical to high fidelity health assessment and low testing costs. A novel approach of sensor optimization placement for health monitoring based on fault detectability and trackability is proposed in this paper. Firstly, the requirements of sensor selection for health monitoring, the definitions and calculations of fault detectability and trackability are presented. Thus, a Sensor Optimization Selection Model (SOSM), whose objectives are to maximize the fault detectability and trackability and minimize cost of sensors, is built. Afterwards, an Adaptive Simulated Annealing Genetic Algorithm (ASAGA) is implemented to solve the SOSM. Finally, the real gearboxes and experimental data are used to verify the effectiveness of the SOSM proposed in this paper and its solution. The results from this study have shown that the approach can provide a better strategy for health monitoring in order to reduce the test cost, improve the reliability and the capability.

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A mission execution decision making methodology based on mission-health interrelationship analysis

Geng

Zhou

et al. 2016

Computers & Industrial Engineering

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A novel approach of testability modeling and analysis for PHM systems based on failure evolution mechanism

Cited by 22 publications

References 22 publications

Test selection and optimization for PHM based on failure evolution mechanism model

Test selection and optimization for PHM based on failure evolution mechanism model

Sensor Optimization Selection Based on Fault Detectability and Trackability

A mission execution decision making methodology based on mission-health interrelationship analysis

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