Pattern recognition

Fault detection of networked dynamical systems (NDSs) has attracted ever-increasing attention since it can maintain high-quality products as well as operational safety. Considering the utilisation ...

https://www.tandfonline.com/doi/pdf/10.1080/00207721.2021.1998722

Fault detection of networked dynamical systems: a survey of trends and techniques

In large industrial systems, alarm management is one of the most important issues to improve the safety and efficiency of systems in practice. Operators of such systems often have to deal with a numerous number of simultaneous alarms. Different kinds of thresholding or filtration are applied to decrease alarm nuisance and improve performance indices, such as Averaged Alarm Delay (ADD), Missed Alarm and False Alarm Rates (MAR and FAR). Among threshold-based approaches, variable thresholding methods are well-known for reducing the alarm nuisance and improving the performance of the alarm system. However, the literature suffers from the lack of an appropriate method to assess performance parameters of Variable Threshold Alarm Systems (VTASs). This study introduces two types of variable thresholding and proposes a novel approach for performance assessment of VTASs using Priority-AND gate and semi-Markov process. Application of semi-Markov process allows the proposed approach to consider industrial measurements with non-Gaussian distributions. In addition, the paper provides a genetic algorithm based optimized design process for optimal parameter setting to improve performance indices. The effectiveness of the proposed approach is illustrated via three numerical examples and through a comparison with previous studies.

Performance evaluation and design for variable threshold alarm systems through semi-Markov process.

Generalized moving variance filters for industrial alarm systems

Attack resilient control for vehicle platoon system with full states constraint under actuator faulty scenario

Event-triggered robust fault diagnosis and control of linear Roesser systems: A unified framework

Filtering is an effective method of alarm management family that can reduce false and missed alarm rates significantly. Simple and effective techniques of fault diagnosis methods are popular in industry. So, deriving a simple analytic filter design approach is important. This study proposes a simple analytic linear filter design based on a probabilistic model of the system. At last, the effectiveness of the proposed method is showed in the deposition fault detection of a V94.2 gas turbine with 162.1 MW and 50 Hz as the nominal power and frequency respectively. It is built by MAPNA group (originally built by SIEMENS) and set up in Shiraz power plant, Shiraz city of Iran.

Alarm management based fault diagnosis of V94.2 gas turbines by applying linear filters

In this paper a new unknown input estimation method is proposed for a class of nonlinear stochastic systems in the presence of time dependent unknown inputs, when the system states and process noises are unknown but bounded. In this study, a new augmented state vector is constructed by augmenting unknown inputs as a new state to the original state vector. Then a recursive algorithm based on unknown but bounded (UBB) uncertainty is developed, that unlike the Bayesian models which consider the state estimate as a single vector, produces a time-varying set of state estimates that contains the system's true state. The particular sets to be discussed, are ellipsoids. The proposed method doesn't need any unknown input detection stage procedure and covariance resetting that are necessary in previous works. At last, efficiency of the proposed method is shown in a numerical simulation for a nonlinear system.

Unknown input estimation by applying extended Kalman filter based on unknown but bounded uncertainties

Estimation of the state variables of dynamical systems in the presence of noise, disturbance, and fault is an important problem in the control and monitoring field. In this article, the mentioned issue is addressed for singular 2-D systems in Fornasini–Marchesini first model (FM-I). In this regard, a functional unknown input observer is proposed to eliminate the effects of unknown inputs and estimate a given linear combination of the system states. Moreover, the proposed observer can be used to estimate the faults and necessary and sufficient conditions for the simultaneous estimation of states and faults are derived. Finally, the performance of the proposed estimation methods is validated through a numerical example and a second-order parabolic heat equation.

Hamid Alikhani

Papers

Event-triggered robust fault diagnosis and control of linear Roesser systems: A unified framework

Alarm management based fault diagnosis of V94.2 gas turbines by applying linear filters

Unknown input estimation by applying extended Kalman filter based on unknown but bounded uncertainties

A Functional Unknown Input Observer for Linear Singular Fornasini–Marchesini First Model Systems: With Application to Fault Diagnosis

Robust Anomaly Detection Based on a Dynamical Observer for Continuous Linear Roesser Systems