Process fault prognosis using a fuzzy‐adaptive unscented Kalman predictor

Tian, Xuemin; Cao, Yu; Chen, Sheng

doi:10.1002/acs.1243

Cited by 14 publications

(5 citation statements)

References 36 publications

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“…The context-free grammars are supported by ISO 14224 [5] and OREDA [6], in which an exhaustive technical terminology and format related to maintenance, safety are explain by relevant experts. To provide syntactic and logical forms, we employ FMEA, FTA, ETA & HAZOP to identify cascade events [4,[8][9][10]. To reach a pure knowledge and interpretation from a causality context (logic form), prepared by semantic interpretation, which can be developed and shared among users, we need a base to define and support a common data model for long-term data integration, access and exchange.…”

Section: Methodsmentioning

confidence: 99%

Integrated Intelligent Equipment Health Management System to Support Life Cycle Decisions Considering Environmentally Conscious Production

Ebrahimipour¹

2014

Int J of Swarm Intel Evol Comput

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Integrated Intelligent Equipment Health Management System (IIEHMS) is a systematic engineering design Framework that includes multi-functional disciplines (such as mechanical, electrical, optical and software engineering, quality assurance, and manufacturing) with their partners and suppliers works together to achieve a green topology plant with respect to life cycle cost. The proposed structural framework is defined as fitness for operation at any time during plant life cycle. Plant life cycle includes the entire spectrum of activities; beginning with the identification of need and extending through plant design and development, production and/or construction, operational use and sustaining maintenance and support, and plant retirement and material disposal. The methodology recognizes five main aspects of the framework as follows: a) Plant Topology and Semantic Knowledge Acquisition Modeling, to synchronize different failure mechanisms database, environment and safety incident and accident reports, and properly retrieve recorded data and synchronize them during decision making process b) Equipment Failure Reasoning & Modeling, to better understand, represent and address equipment failure mode and their impacts on process, environment and human health c) Adaptive Intelligent Fault Diagnosis & Prognosis, which is fault diagnosis process modeling and recommendations including fault detection, fault isolation, scenario assessment and mitigation plan according to Environmental/safety/Health/Business requirements, d) Green Assessment of diagnosis scenarios, to particularly evaluate CO 2 emissions, the consequence of each failure scenario from the view point of pollution, risk, and global warming human health and ecological risks due to toxicity of pollutants emitted into the environment and social impact e) Reliability, Maintainability and Availability prediction and lifetime estimation and, to calculate some measure of performance that can be used in the instantaneous actions decision making, and to prevent future accidents through preventive actions f) Optimization of purchasing cost considering lifetime, warranty and environmental consciousness, to choose suppliers that minimize LCC and maximize the reliability of equipment and spare parts. Moreover, in the model, we incorporated failure mode, effects, and criticality analysis (FMECA) through which an enterprise is able to evaluate the external failure costs of purchased equipment/ components in order to optimize their choice of suppliers/vendors who provide better components for operation. Those aspects are included in an Equipment Health Management System that aims at assuring the best actions and decisions during plant life cycle and lifetime. They are linked to the result-type information and knowledge flows coming from internal/external parties, experts and stake holders and finally from the dynamic and continuous fault recognition process.

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Section: Methodsmentioning

confidence: 99%

Integrated Intelligent Equipment Health Management System to Support Life Cycle Decisions Considering Environmentally Conscious Production

Ebrahimipour¹

2014

Int J of Swarm Intel Evol Comput

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“…During initial few cycles when the window size for adaptation is not adequate, the value of ζ may be set to 0 to inhibit the adaptation of R. † The presented ACDF algorithm is structured for the additive noises but may be easily extended to systems with non-additive noises. † A critical examination of the steps of the algorithm or a simple benchmarking would readily confirm that the proposed first order ACDF is less computationally intensive when compared with ADDF [18] or AUKF [10][11][12][13][14][15] based algorithms. For ADDF in particular the additional computation arises because the algorithm generally tends to use more computational intensive second order approximation.…”

Section: Acdf Algorithm With Residual Based R Adaptationmentioning

confidence: 99%

“…In particular adaptive non-linear filters based on derivative free SPKF have started to appear recently in the literature. The adaptive SPKFs include adaptive unscented Kalman filter (AUKF) ( [10][11][12][13][14][15]) and adaptive divided difference filter (ADDF) [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Adaptive central difference filter for non‐linear state estimation

Das

Dey

Sadhu

et al. 2015

IET Science, Measurement & Technology

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“…Prediction is also very important since it can help people make decisions in advance and prevent unknown dangers. Following the same idea of filters, various predictors have been studied, e.g., Kalman predictor (KP) [19], extended Kalman predictor (EKP) [20], unscented Kalman predictor (UKP) [21], cubature Kalman predictor (CKP) [22] and particle predictor (PP) [23]. It is well known that smoothing is, in general, more accurate than the corresponding filtering.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Cramér-Rao Lower Bounds for Prediction and Smoothing of Nonlinear TASD Systems

Duan

Tang

et al. 2022

Sensors

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The performance evaluation of state estimators for nonlinear regular systems, in which the current measurement only depends on the current state directly, has been widely studied using the Bayesian Cramér-Rao lower bound (BCRLB). However, in practice, the measurements of many nonlinear systems are two-adjacent-states dependent (TASD) directly, i.e., the current measurement depends on the current state as well as the most recent previous state directly. In this paper, we first develop the recursive BCRLBs for the prediction and smoothing of nonlinear systems with TASD measurements. A comparison between the recursive BCRLBs for TASD systems and nonlinear regular systems is provided. Then, the recursive BCRLBs for the prediction and smoothing of two special types of TASD systems, in which the original measurement noises are autocorrelated or cross-correlated with the process noises at one time step apart, are presented, respectively. Illustrative examples in radar target tracking show the effectiveness of the proposed recursive BCRLBs for the prediction and smoothing of TASD systems.

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Process fault prognosis using a fuzzy‐adaptive unscented Kalman predictor

Cited by 14 publications

References 36 publications

Integrated Intelligent Equipment Health Management System to Support Life Cycle Decisions Considering Environmentally Conscious Production

Integrated Intelligent Equipment Health Management System to Support Life Cycle Decisions Considering Environmentally Conscious Production

Adaptive central difference filter for non‐linear state estimation

Bayesian Cramér-Rao Lower Bounds for Prediction and Smoothing of Nonlinear TASD Systems

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