Sensor fault detection in a class of nonlinear systems using modal Kalman filter

Robust M-estimation-based maximum correntropy Kalman filter

In this paper, a framework that combines an M-estimation and information-theoretic-learning (ITL)-based Kalman filter under impulsive noises is presented. The ITL-based methods make the most of the features of the data itself and can improve robustness by choosing an appropriate kernel bandwidth. However, small kernel bandwidths may lead to divergence. Nonetheless, robust-regression methods can improve the robustness from the statistical perspective and are independent of kernel bandwidth. This motivates us to fuse M-estimation-based weighting methods and the ITL-based Kalman filter. The proposed framework inhibits the divergence trend of ITL-based Kalman filters at low kernel bandwidth and improves the performance at large kernel bandwidth. Additionally, we use the unscented Kalman filtering method to extend the proposed algorithm to the nonlinear case. Monte Carlo simulations demonstrate the robustness and effectiveness of the proposed algorithm.

Current Status and Prospects of Research on Sensor Fault Diagnosis of Agricultural Internet of Things

Sensors have been used in various agricultural production scenarios due to significant advances in the Agricultural Internet of Things (Ag-IoT), leading to smart agriculture. Intelligent control or monitoring systems rely heavily on trustworthy sensor systems. Nonetheless, sensor failures are likely due to various factors, including key equipment malfunction or human error. A faulty sensor can produce corrupted measurements, resulting in incorrect decisions. Early detection of potential faults is crucial, and fault diagnosis techniques have been proposed. The purpose of sensor fault diagnosis is to detect faulty data in the sensor and recover or isolate the faulty sensors so that the sensor can finally provide correct data to the user. Current fault diagnosis technologies are based mainly on statistical models, artificial intelligence, deep learning, etc. The further development of fault diagnosis technology is also conducive to reducing the loss caused by sensor failures.

A Novel Adaptive Sensor Fault Estimation Algorithm in Robust Fault Diagnosis

The paper deals with a robust sensor fault estimation by proposing a novel algorithm capable of reconstructing faults occurring in the system. The provided approach relies on calculating the fault estimation adaptively in every discrete time instance. The approach is developed for the systems influenced by unknown measurement and process disturbance. Such an issue has been handled with applying the commonly known H∞ approach. The novelty of the proposed algorithm consists of eliminating a difference between consecutive samples of the fault in an estimation error. This results in a easier way of designing the robust estimator by simplification of the linear matrix inequalities. The final part of the paper is devoted to an illustrative example with implementation to a laboratory two-rotor aerodynamical system.

Interval estimation for nabla fractional order linear time-invariant systems

In this paper, we provide a framework to achieve interval estimation for nabla Caputo fractional order linear time-invariant (LTI) systems in the presence of bounded model uncertainties. Interval observers based on fractional order positive systems theory are designed by possessing desired stable and positive error dynamics. Specifically, the basic concepts and conditions for guaranteeing stability and positivity of the considered systems are derived systematically by finding the system responses. Using the developed criteria and the structure of Luenberger-type observers, a classic interval observer is designed directly which further extends the system classes of interval estimation. Besides, due to the possible absence of a gain matrix which ensures positivity requirement, a more general interval observer design scheme is given by exploiting the coordinate transformation technique. Finally, some simulated cases including fault detection and fractional order circuits related scenarios are developed to validate the usefulness and practicality of the framework.

Fuzzy extended state observer for the fault detection and identification

This paper introduces a novel methodology to detect and identify faults for a class of autonomous nonlinear systems. In the proposed design, a fuzzy extended system observer (FESO) based on the Mandami-type fuzzy system is used to estimate the fault that is considered to be the extended system state. In this method, the Mamdani-type fuzzy system is based on a single-input single-output (SISO) where the observer error is considered as the fuzzy input variable. Additionally, the stability analysis under Lyapunov criteria verifies that the solutions of proposed FESO are ultimately bounded. Finally, simulation examples are given to corroborate the feasibility of the proposed FESO.

Quantized recursive filtering for networked systems with stochastic transmission delays

This paper investigates the recursive filtering problem for a class of networked systems subject to the uniform quantization effects and stochastic transmission delays. The system output is quantized according to a uniform quantization mechanism, and then sent to the remote filter via a communication network undergoing stochastic transmission delays (which are modeled by a sequence of independent and identically distributed variables). To deal with the stochastic transmission delays, an indicator function is delicately designed to ensure that the filtering process is implemented based on the quantized measurement with the newest timestamp available for the filter. With the aid of the indicator function, a free-delay system is obtained by using the augmented system method. The aim of this paper is to design a Kalman-type filter for the augmented system such that an upper bound of the filtering error covariance is guaranteed and minimized. With the aid of the stochastic analysis method, the desired upper bound of the filtering error covariance is derived by recursively solving two Riccati-like difference equations. Then, the upper bound is minimized by properly selecting the filter parameters. Finally, a numerical example is provided to illustrate the validity of the developed filtering scheme.

Detection of Unit of Measure Inconsistency in gas turbine sensors by means of Support Vector Machine classifier

The reliability of gas turbine diagnostics clearly relies on reliable measurements. However, raw data reliability can be corrupted by label noise issues, as for instance an erroneous association between data and the respective unit of measure. Such issue, rarely investigated in the literature, is named Unit of Measure Inconsistency (UMI). Machine Learning classifiers are suitable tools to tackle the challenge of UMI detection. Thus, this paper investigates the capability of four Support Vector Machine approaches to detect UMIs. All approaches are tested on a dataset composed of field data taken on a fleet of Siemens gas turbines. The results of this study demonstrate that the Radial Basis Function with One-vs-One decomposition allows higher diagnostic accuracy.