Signal Interpretation for Monitoring and Diagnosis

1999

Journal of Process Control

This paper considers the precision degradation type of sensor faults within control loops. In a closed loop, sensor faults propagate through controller to manipulated variables and disturb the other process variables, which obscures the source of sensor faults but receives less attention in existing methods of data-driven sensor fault diagnosis. With the assumption that only closed-loop data in normal condition are available, difficulty arises due to the facts that little a priori knowledge is known about closed-loop sensor fault propagation and the open-loop process model may not be identifiable. The proposed method in this paper constructs residual that is regarded as including two parts: the first part is the current sensor faults whose fault direction is known to be the identity matrix; and for the purpose of diagnosing the first part, the second part is considered as the disturbance which is affected by noises and past sensor faults due to unknown fault propagation. The disturbance variance is minimized in residual generator design to improve fault sensitivity. And the corresponding disturbance covariance is estimated and then utilized in residual evaluation. The proposed method in this paper is motivated by a pioneer work on closed-loop sensor fault diagnosis which performs principal component analysis in the feedback-invariant subspace of the closed-loop process outputs. But it is revealed by the proposed method that the feedback-invariant signal is affected by past sensor faults, leading to performance degradation of the pioneer work. The improvement of the proposed approach is due to analysis of residual dynamics and explicit handling of the disturbance in residual evaluation, which is not considered in the pioneer work. A simulated 4 × 4 dynamic process and a simulated two-product distillation column are studied to verify the effectiveness of the proposed approach compared to the existing principal component analysis method in feedback-invariant subspace.

2006

Traditional hydraulic servomechanisms for aircraft control surfaces are being gradually replaced by newer technologies, such as Electro-Mechanical Actuators (EMAs). Since field data about reliability of EMAs are not available due to their recent adoption, their failure modes are not fully understood yet; therefore, an effective prognostic tool could help detect incipient failures of the flight control system, in order to properly schedule maintenance interventions and replacement of the actuators. A twofold benefit would be achieved: Safety would be improved by avoiding the aircraft to fly with damaged components, and replacement of still functional components would be prevented, reducing maintenance costs. However, EMA prognostic presents a challenge due to the complexity and to the multidisciplinary nature of the monitored systems. We propose a model-based fault detection and isolation (FDI) method, employing a Genetic Algorithm (GA) to identify failure precursors before the performance of the system starts being compromised. Four different failure modes are considered: dry friction, backlash, partial coil short circuit, and controller gain drift. The method presented in this work is able to deal with the challenge leveraging the system design knowledge in a more effective way than data-driven strategies, and requires less experimental data. To test the proposed tool, a simulated test rig was developed. Two numerical models of the EMA were implemented with different level of detail: A high fidelity model provided the data of the faulty actuator to be analyzed, while a simpler one, computationally lighter but accurate enough to simulate the considered fault modes, was executed iteratively by the GA. The results showed good robustness and precision, allowing the early identification of a system malfunctioning with few false positives or missed failures.

2001

Abstract TRANSCEND, our system for fault detection and isolation of complex dynamic systems, uses a model based approach to predict and analyze transient effects resulting from abrupt faults in the system. Abrupt faults are attributed to discrete and persistent parameter value changes. Fault isolation is performed by matching features extracted from the transients against those predicted by the model.

IEEE Transactions on Control Systems Technology, 2000

In this paper a model-based procedure exploiting analytical redundancy for the detection and isolation of faults in input-output control sensors of a dynamic system is presented. The diagnosis system is based on state estimators, namely dynamic observers or Kalman filters designed in deterministic and stochastic environment, respectively, and uses residual analysis and statistical tests for fault detection and isolation. The state estimators are obtained from input-output data process and standard identification techniques based on ARX or errors-in-variables models, depending on signal to noise ratio. In the latter case the Kalman filter parameters, i.e., the model parameters and input-output noise variances, are obtained by processing the noisy data according to the Frisch scheme rules. The proposed fault detection and isolation tool has been tested on a single-shaft industrial gas turbine model. Results from simulation show that minimum detectable faults are perfectly compatible with the industrial target of this application.

HAL (Le Centre pour la Communication Scientifique Directe), 2015

This work falls within the fault detection and sensor faults dedicated to monitoring climate data and or air pollution. The strategy discussed in this paper represents a contribution to the study of methods for detecting and locating defects by analytical redundancy. Indeed, it is a network of sensors installed in an urban area, measuring and continuously observing climate change and pollution. The failure of one or more of these sensors at given moments will inevitably result in an erroneous analysis of the situation. The proposed technique is based on the nonlinear modeling using blind Multi-models, choosing the "Multi-model decoupled state" structure and at the same time we will give an idea about the multi-model in coupled state says Sugeno-Takagi; and hierarchical structure. The goal through using these models is the generation of residues for diagnosis.

Complex industrial and aerospatial systems require efficient monitoring and fault detection schemes to ease prognosis and health monitoring tasks.

Proceedings of the 1997 IFAC International Symposium on Advanced Control of Chemical Processes (ADCHEM '97), Shah, S.L. and Arkun, Y. (eds.), Elsevier Science., 1997

One way to detect faults or monitor process performance is to use process measurements and model predictions to generate residuals that can then be evaluated. The use of fixed threshold logic is the most straightforward method for residual evaluation. In this paper a variation on the fixed threshold scheme, involving the use of multivariate statistics, is described and applied to operating data from an industrial acid conditioning process. It is shown that the method can be used to monitor the performance of a sensor known to be unreliable.

2020 IEEE 29th International Symposium on Industrial Electronics (ISIE)

The problem of sensor fault identification in mechatronic systems described by linear and nonlinear dynamic models is considered. To address the problem, sliding mode observers are used. The suggested approach for constructing sliding mode observers is based on the reduced order model of the initial system. This allows to reduce complexity of sliding mode observers and relax the limitations imposed on the initial system.

Fault Diagnosis of Dynamic Systems, 2019