Sensor fault compensation via software sensors: Application in a heat pump's helical evaporator

Escobar, R.F.; Astorga‐Zaragoza, C.M.; Hernández, J.A.; Juárez-Romero, D.; Beltrán, Carlos Daniel García

doi:10.1016/j.cherd.2014.06.017

Cited by 18 publications

(5 citation statements)

References 13 publications

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“…The estimation for deriving the symptom signal is determined via the binary particle swarm optimization algorithm and used for fault detection within turbidity sensor readings of a bioprocess. The calculated symptom signal can also compensate for the detected sensor faults [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Ensemble‐based adaptive soft sensor for fault‐tolerant biomass monitoring

Siegl

Brunner

Geier

et al. 2022

Engineering in Life Sciences

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The accuracy and precision of soft sensors depend strongly on the reliability of underlying model inputs. These inputs (particularly readings of hardware sensors) are frequently subject to faults. This study aims to develop an adaptive soft sensor capable of reliable and robust biomass concentration predictions in the presence of faulty model inputs for a Pichia pastoris bioprocess. Hence, three soft sensor submodels were developed based on three independent model inputs (base addition, CO 2 production, and mid-infrared spectrum). An ensemble-based algorithm combined the submodels to form an ensemble model, that is, an adaptive soft sensor, to achieve fault-tolerant prediction. The algorithm's basic steps are as follows: the initial determination of submodel reliability is followed by selecting appropriate submodels to generate a reliable prediction via variance-based weighting of the submodels. The adaptive soft sensor demonstrated high robustness and accuracy in biomass prediction in the presence of multiple simulated sensor faults (RMSE = 0.43 g L −1 ) and multiple real sensor faults (RMSE = 0.70 g L −1 ).

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Section: Introductionmentioning

confidence: 99%

Ensemble‐based adaptive soft sensor for fault‐tolerant biomass monitoring

Siegl

Brunner

Geier

et al. 2022

Engineering in Life Sciences

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“…These schemes can be categorized in two types: multiple MPCs methods such as [18], [19] that in majority are based on fuzzy models, and adaptive methods such as [20], [21]. In adaptive methods, faults can be compensated by correction the internal model [20], [22], or by correction the constraints in MPC problem [23], [24], or fault correction in measured outputs [25].…”

Section: Introductionmentioning

confidence: 99%

A New Active Fault Tolerant Control System: Predictive Online Fault Estimation

Bavili

Mohammadzadeh²,

Tavoosi

et al. 2021

IEEE Access

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This study presents a new approach for active fault-tolerant controller (FTC) design for constrained nonlinear multi-variable systems. The proposed approach utilize the nonlinear model predictive controller (NMPC) and fault estimation method which is on basis of extended kalman filters (EKFs). The deficiency of actuators and sensors and also the plant states measurement errors are estimated by the suggested approach. A supervisor unit using the fault information and fault modeling per sampling time, corrects the predictor model of the controller and compensates actuator and sensor faults in control system. Furthermore, by the presented feedback compensation, the robustness of the designed method against plant faults and uncertainties is ensured. The important advantages of the proposed method are:(1) The suggested FTC scheme based on NMPC leads to calculate more accurate control action than MPC in nonlinear processes, (2) it is comprehensive in fault accommodation point of view because it is able to compensate all types of faults in control systems simultaneously, (3) it has low computational cost because of using NMPC by analytical solution, (4) it can handle control and states constraints to prevent of actuator saturations and unsafe situations, (5) the simplicity and effectiveness of the designed FTC scheme for real applications is more significant. Simulation results on continuous stirred tank reactor process verifies the superiority and capability of the designed approach.

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“…A typical AHU consists of multiple subsystems, including numerous sensors, controllers, and actuators. Any hardware failure or controller error, such as temperature sensor fault and the error in fault-tolerant control [3–5], associated with these subsystems might result in the abnormality of AHU and thus affects the whole performance of HVAC system, leading to a negative impact on energy consumption, thermal comfort, and building maintenance cost [6]. The statistical result made in [7] shows that the publications about fault detection and diagnosis (FDD) of AHU occupied 42% of the total publications, being the most common application scenario.…”

Section: Introductionmentioning

confidence: 99%

Sensor Fault Detection and Diagnosis Method for AHU Using 1-D CNN and Clustering Analysis

Liu

Zhang

Wang

et al. 2019

Computational Intelligence and Neuroscience

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This paper presents a fault detection and diagnosis (FDD) method, which uses one-dimensional convolutional neural network (1-D CNN) and WaveCluster clustering analysis to detect and diagnose sensor faults in the supply air temperature (Tsup) control loop of the air handling unit. In this approach, 1-D CNN is employed to extract man-guided features from raw data, and the extracted features are analyzed by WaveCluster clustering. The suspicious sensor faults are indicated and categorized by denoting clusters. Moreover, the Tc acquittal procedure is introduced to further improve the accuracy of FDD. In validation, false alarm ratio and missing diagnosis ratio are mainly used to demonstrate the efficiency of the proposed FDD method. Results show that the abrupt sensor faults in Tsup control loop can be efficiently detected and diagnosed, and the proposed method is equipped with good robustness within the noise range of 6 dBm∼13 dBm.

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Sensor fault compensation via software sensors: Application in a heat pump's helical evaporator

Cited by 18 publications

References 13 publications

Ensemble‐based adaptive soft sensor for fault‐tolerant biomass monitoring

Ensemble‐based adaptive soft sensor for fault‐tolerant biomass monitoring

A New Active Fault Tolerant Control System: Predictive Online Fault Estimation

Sensor Fault Detection and Diagnosis Method for AHU Using 1-D CNN and Clustering Analysis

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