Sensor Fault Detection Method Research Articles

Sensor Fault Detection and Diagnosis of a Process Using Unknown Input Observer

In this paper, a robust sensor fault detection and isolation (FDI) method based on the unknown input observer (UIO) approach is presented. The basic principle of unknown input observers is to decouple disturbances from the state estimation error. A single full-order observer is designed to detect sensor faults in the presence of unknown inputs (disturbances). By doing so, we generate a residual, a weighted output of the state estimation error, decoupled from disturbances. The resulting robust (in the sense of disturbances) residual can be used for fault detection. Although this scheme has successful fault detection, using one observer is not successful in fault isolation. Therefore, a robust sensor fault isolation observer scheme is proposed. In order to evaluate its ability, the presented method is adopted to detect and isolate sensor faults of a highly nonlinear dynamic system. The faulty behavior of output sensors in a jacketed continuous stirred tank reactor (CSTR), around operating point, is investigated. Simulation results show that model uncertainties and disturbances can be distinguished from a response to a sensor fault.

FPGA-based real time current sensor failure diagnosis for shunt active power filters

The performances of conventional shunt active power filters heavily depend on accurate active filter current sensing. A sudden failure in one of the active filter current sensors decreases the filter performances. Moreover, if the fault is not detected and compensated for quickly, its effect leads to disconnection or hard failure of the active filter. Hence, to reduce the failure rate and to prevent unscheduled shutdown, a real-time fault detection, isolation and compensation scheme could be adopted. This article introduces a new fast and robust current sensor fault detection and compensation method for three-phase shunt active power filters. The proposed current sensor fault detection is achieved with a predictive model associated with a logical decision. The satisfactory performances obtained by experimental ‘FPGA in the loop’ prototyping validate the effectiveness of the proposed method.

A Robust Fault Detection and Isolation Method in Load Frequency Control Loops

In this paper, a robust sensor and actuator fault detection and isolation (FDI) method based on unknown input observers (UIO) is adopted and applied to the load frequency control loops of interconnected power systems. Changes in load demand are regarded as the unknown disturbances in the system and the designed UIOs and thus the proposed method are robust to these disturbances. Using selected different sets of measured variables in the UIO design, simulations are performed for the dynamical model of a power control system composed of two areas. As we distinguish the cases for successful and unsuccessful sensor and controller FDI, it is shown that the proposed scheme is able to detect and isolate sensor and controller faults for proper selections of measured variables. By using residuals generated by the UIOs, the designed ldquofault detection and isolation logicrdquo system shows the operator which sensor or controller is faulty. Hence the faulty sensor or controller can be replaced by a healthy one for a more reliable operation.

Experimental study on sensor fault detection and diagnosis and estimation of centrifugal chiller system

The reliability and accuracy of sensor measurements are greatly of significance to control and optimal operation of chiller system,as well as the basis of fault detection and diagnosis of chiller component faults.This paper presents a principle component analysis(PCA)-based sensor fault detection and diagnosis and estimation method for chiller system.A PCA model is composed of the observations of correlated variables in centrifugal chiller system in normal operation conditions aiming to capture the systematic variations of chiller system. These variations are used for the fault detection and diagnosis of new observations in terms of Q-statistic and Q-contribution plot.This PCA-based chiller sensor fault detection and diagnosis method was validated using the experimental test data of a centrifugal chiller.

Variable reconstruction and sensor fault identification using canonical variate analysis

The detection and identification of faults in dynamic continuous processes has received considerable recent attention from researchers in academia and industry. In this paper, a canonical variate analysis (CVA)-based sensor fault detection and identification method via variable reconstruction is described. Several previous studies have shown that CVA-based monitoring techniques can effectively detect faults in dynamic processes. Here we define two monitoring indices in the state and noise spaces for fault detection and, for sensor fault identification, we propose three variable reconstruction algorithms based on the proposed monitoring indices. The variable reconstruction algorithms are based on the concepts of conditional mean replacement and object function minimization. The proposed approach is applied to a simulated continuous stirred tank reactor and the results are compared to those obtained using the traditional dynamic monitoring technique, dynamic principal component analysis (PCA). The results indicate that the proposed methodology is quite effective for monitoring dynamic processes in terms of sensor fault detection and identification.

Subspace Method for Sensor Fault Detection and Isolation-Application to Grinding Circuit Monitoring

Sensor fault detection and isolation method is proposed in this paper. The method is only based on the knowledge of the input and output data. Any parameter estimation nor system order determination are necessary. The proposed technique uses matrix projection in subspace framework. The sensitivity of the method to sensor faults is shown. The method is applied for sensor fault detection and isolation in a grinding circuit.

Joint sensor fault detection for fault tolerant parallel manipulators

Parallel manipulators with redundant joint displacement sensing can be exploited to develop fault tolerant implementations. This is possible since fundamental problems of the associated kinematics can still be solved after the elimination of faulty sensor readings. The ability of detecting faulty sensor readings is a requirement of any fault tolerant implementation scheme. A sensor fault detection method is presented for redundantly sensed parallel manipulators. A broad class of three-branch manipulators is considered where each branch consists of three main-arm joints and supports a common payload through respective passive spherical joints. The detection method is based on the comparison of forward displacement solutions for different cases of joint sensor readings. The existence of common solutions based on the branches–sensors considered, is used to effectively identify the existence of a failed sensor. Once a faulty sensor is identified, continued (fault tolerant) operation is possible using a forward displacement solution based on the readings of the accurate sensors. The detection method is implemented in a computer simulation of a calibrated three-branch parallel manipulator. © 2000 John Wiley & Sons, Inc.

Sensor Fault Detection with the Single Sensor Parity Relation

A sensor fault detection method combining the single sensor parity relation (SSPR) with the likelihood ratio test (LRT) is described. The SSPR is in an algebraic form that correlates system dynamics with multistep readings of a sensor and is therefore fast running. The scheme can easily be duplicated for each sensor of interest and thus has advantages for modular design and parallel processing. In the fault detection architecture, residuals generated from the SSPR module are examined by an LRT module for failure signatures. The likelihood ratios are maximized according to the fault occurrence time to improve detection sensitivity and are then calculated using a recursive form to match the speed of the SSPR module. The proposed concept is demonstrated with hypothetical sensor failures for pressurizer instruments. Comparisons with the Kalman filtering technique and the sequential probability ratio test are discussed.