Instrumentation network design and upgrade for process monitoring and fault detection

Bagajewicz, Miguel J.; Fuxman, Adrian; Uribe, Ariel

doi:10.1002/aic.10279

Cited by 43 publications

(32 citation statements)

References 23 publications

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“…(2) If fault f 2 occurs, i.e., the primary helium blower malfunctions, then there must be abnormality in the primary helium flowrate, which induces abnormality in the acquired signal by s 2 . Since the steam temperature is very sensitive to the helium flowrate, abnormality in the acquired signal by s 2 can further lead to that of sensor s 4 . Because the temperature of the secondary steam/water flow can influence the primary helium temperature, abnormality in the acquired signal by s 4 can induce that of s 3 .…”

Section: Directed Graph For Fault Propagationmentioning

confidence: 99%

“…Since the steam temperature is very sensitive to the helium flowrate, abnormality in the acquired signal by s 2 can further lead to that of sensor s 4 . Because the temperature of the secondary steam/water flow can influence the primary helium temperature, abnormality in the acquired signal by s 4 can induce that of s 3 . (3) If fault f 3 occurs, i.e., the heat transfer between the two sides the OTSG will be degraded, then the thermal resistance of the OTSG becomes abnormal, which immediately leads to abnormalities of sensors s 3 and s 4 .…”

Section: Directed Graph For Fault Propagationmentioning

confidence: 99%

“…Sensor selection was treated as different DG-based optimization problems in most early studies. Bagajewicz et al summarized the sensor selection in a process as mix integer linear programming (MILP) problems focusing on optimizing cost or (and) reliability [1][2][3][4]. Bhushan, Narasimhan, and Rengaswamy added the criteria of robustness to the MILP problems [5].…”

Section: Introductionmentioning

confidence: 99%

“…The faults to be detected or discriminated are given in Table 2, which include the abnormal reactivity injection, malfunction of the primary helium blower, and heat transfer degradation between the two sides of the OTSG. 3 average temperature of the primary flow • C 300~700 1 4~20 mA s 4 average temperature of the secondary flow • C 330~430 0.5 4~20 mA Table 2. Fault nodes to be detected or discriminated.…”

Section: Introductionmentioning

confidence: 99%

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Boolean Network-Based Sensor Selection with Application to the Fault Diagnosis of a Nuclear Plant

Dong

2017

Energies

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Fault diagnosis is crucial for the operation of energy systems such as nuclear plants, and heavily relies on various types of sensors for temperature, pressure, concentration, etc. Due to the redundancy of sensors in each energy system, the sensor selection scheme can deeply influence the diagnostic efficiency. In this paper, a Boolean network (BN) with its linear representation is proposed for describing the fault propagation among sensors. Both the sufficient condition of fault detectability and that of fault discriminability are given. Then, a sensor selection method for fault detection and discrimination is proposed. Finally, the theoretic result is applied to realize the diagnosis oriented sensor selection for a nuclear steam supply system based on a modular high temperature gas-cooled reactor (MHTGR). The computation and simulation results verify the correctness of the theoretical results.

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Section: Directed Graph For Fault Propagationmentioning

confidence: 99%

Section: Directed Graph For Fault Propagationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Boolean Network-Based Sensor Selection with Application to the Fault Diagnosis of a Nuclear Plant

Dong

2017

Energies

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“…[10] and [11]. Nevertheless, in our case, the water supply company is not interested in the best sensor configuration but in the best diagnosis performance that can be achieved by installing a specific number of sensors.…”

Section: Problem Formulationmentioning

confidence: 99%

Sensor placement for fault diagnosis performance maximization in Distribution Networks

Sarrate

Nejjari

Rosich

2012

2012 20th Mediterranean Conference on Control &Amp; Automation (MED)

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Abstract-The success of any diagnosis strategy critically depends on the sensors measuring process variables. This paper presents a strategy based on diagnosability maximization for optimally locating sensors in distribution networks. The goal is to characterize and determine the set of sensors that guarantees a maximum degree of diagnosability taking into account a given sensor configuration cardinality constraint. The strategy is based on the structural model of the system under consideration. Structural analysis is a powerful tool for determining diagnosis possibilities and evaluating whether the number and the location of sensors are adequate in order to meet some diagnosis specifications. The proposed approach is successfully applied to leakage detection in a Drinking Water Distribution Network.

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A new and improved MILP formulation to optimize observability, redundancy and precision for sensor network problems

Kelly

Zyngier

2008

AIChE Journal

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in Wiley InterScience (www.interscience.wiley.com).New mathematical formulations of observability, redundancy, and an improved formulation for precision are provided which can be explicitly and analytically solved using mixed integer linear programming (MILP). By using the Schur complement found at the heart of both Gaussian elimination and Cholesky factorization for direct block matrix reduction and the variable classification and covariance calculations found in the reconciliation, regression, and regularization approach of Kelly, it is possible to efficiently optimize the overall instrumentation cost considering both estimability and variability as constraints during the branch-and-bound search of the MILP. Two illustrative examples are highlighted which minimize the cost of sensor placement subject to software and hardware redundancy of the measured variables, observability of the unmeasured variables, and their precision (i.e., inverse of their variance). This formulation is well suited to the problems of designing as well as retrofitting sensor locations in arbitrary networks.

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Instrumentation network design and upgrade for process monitoring and fault detection

Cited by 43 publications

References 23 publications

Boolean Network-Based Sensor Selection with Application to the Fault Diagnosis of a Nuclear Plant

Boolean Network-Based Sensor Selection with Application to the Fault Diagnosis of a Nuclear Plant

Sensor placement for fault diagnosis performance maximization in Distribution Networks

A new and improved MILP formulation to optimize observability, redundancy and precision for sensor network problems

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