Fault diagnosis and accommodation of a three-tank system based on analytical redundancy

This paper proposes a model-based approach to develop a novel fault diagnosis scheme for a sensor network of a cascade, parallel and feedback combination of subsystems. The objective is to detect and isolate a fault in any of the subsystems and measurement sensors which are subject to disturbances and/or measurement noise. Our approach hinges on the use of a bank of Kalman filters (KF) to detect and isolate faults. Each KF is driven by either a pair (a) of consecutive sensor measurements or (b) of a reference input and a measurement. It is shown that the KF residual is a reliable indicator of a fault in subsystems and sensors located in the path between the pair of the KF's input. The simple and efficient procedure proposed here analyzes each of the associated paths and leads to both the detection and isolation of any fault that occurred in the paths analyzed. The scheme is successfully evaluated on several simulated examples and on a physical fluid system exemplified by a benchmarked laboratory-scale two-tank system to detect and isolate faults including sensor, actuator and leakage ones.

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Data-Driven Adaptive Observer for Fault Diagnosis

Mathematical Problems in Engineering ◽

10.1155/2012/832836 ◽

2012 ◽

Vol 2012 ◽

pp. 1-21 ◽

Cited By ~ 90

Author(s):

Shen Yin ◽

Xuebo Yang ◽

Hamid Reza Karimi

Keyword(s):

Fault Diagnosis ◽

Process Model ◽

Large Scale ◽

Process Models ◽

Physical Models ◽

Adaptive Observer ◽

Data Driven ◽

Process Data ◽

Tank System ◽

Residual Generator

This paper presents an approach for data-driven design of fault diagnosis system. The proposed fault diagnosis scheme consists of an adaptive residual generator and a bank of isolation observers, whose parameters are directly identified from the process data without identification of complete process model. To deal with normal variations in the process, the parameters of residual generator are online updated by standard adaptive technique to achieve reliable fault detection performance. After a fault is successfully detected, the isolation scheme will be activated, in which each isolation observer serves as an indicator corresponding to occurrence of a particular type of fault in the process. The thresholds can be determined analytically or through estimating the probability density function of related variables. To illustrate the performance of proposed fault diagnosis approach, a laboratory-scale three-tank system is finally utilized. It shows that the proposed data-driven scheme is efficient to deal with applications, whose analytical process models are unavailable. Especially, for the large-scale plants, whose physical models are generally difficult to be established, the proposed approach may offer an effective alternative solution for process monitoring.

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Nonlinear Analytical Redundancy Method for Sensor Fault Diagnosis in an Automotive Application

Annual Conference of the PHM Society ◽

10.36001/phmconf.2018.v10i1.538 ◽

2018 ◽

Vol 10 (1) ◽

Author(s):

Huanyi Shui ◽

Shiming Duan ◽

Chaitanya Sankavaram ◽

Jun Ni

Keyword(s):

Fault Diagnosis ◽

Nonlinear Systems ◽

Automotive Application ◽

Sensor Fault ◽

Vehicle Operation ◽

Special Cases ◽

Analytical Redundancy ◽

Observation Matrix ◽

Air Intake ◽

Nonlinear Observation

Sensors have been widely implemented in vehicle systems for control, driving, and vehicle condition monitoring purposes. In a typical automotive vehicle, there are 60-100 sensors on board and is projected to reach 200 sensors per car. Those sensors provide rich information to ensure safe vehicle operation. However, like any dynamic systems, sensors are vulnerable to degrade or fail over time, which leads to the need of real-time sensor fault diagnosis. Analytical redundancy has been the key model-based approach for sensor fault diagnosis. However, existing analytical redundancy approaches are limited to linear systems, or some special cases of nonlinear systems. In this paper, the analytical redundancy approach is extended to nonlinear systems in general to ensure the accuracy of sensor measurements. Parity relations based on nonlinear observation matrix are formulated to characterize system dynamics and sensor measurements. Robust optimization is designed to identify the coefficient of parity relations that can tolerate certain level of measurement noise and model uncertainties. At last, sensor fault diagnosis in an air intake system is employed to demonstrate the effectiveness of the proposed method.

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