Fault estimation algorithms: design and verification

The research in this thesis is undertaken by observing that modern systems are becoming more and more complex and safety-critical due to the increasing requirements on system smartness and autonomy, and as a result health monitoring system needs to be developed to meet the requirements on system safety and reliability. The state-of-the-art approaches to monitoring system status are model based Fault Diagnosis (FD) systems, which can fuse the advantages of system physical modelling and sensors' characteristics. A number of model based FD approaches have been proposed. The conventional residual based approaches by monitoring system output estimation errors, however, may have certain limitations such as complex diagnosis logic for fault isolation, less sensitiveness to system faults and high computation load. More importantly, little attention has been paid to the problem of fault diagnosis system verification which answers the question that under what condition (i.e., level of uncertainties) a fault diagnosis system is valid. To this end, this thesis investigates the design and verification of fault diagnosis algorithms. It first highlights the differences between two popular FD approaches (i.e., residual based and fault estimation based) through a case study. On this basis, a set of uncertainty estimation algorithms are proposed to generate fault estimates according to different specifications after interpreting the FD problem as an uncertainty estimation problem. Then FD algorithm verification and threshold selection are investigated considering that there are always some mismatches between the real plant and the mathematical model used for FD observer design. Reachability analysis is drawn to evaluate the effect of uncertainties and faults such that it can be quantitatively verified under what condition a FD algorithm is valid. First the proposed fault estimation algorithms in this thesis, on the one hand, extend the existing approaches by pooling the available prior information such that performance can be enhanced, and on the other hand relax the existence condition and reduce the computation load by exploiting the reduced order observer structure. Second, the proposed framework for fault diagnosis system verification bridges the gap between academia and industry since on the one hand a given FD algorithm can be verified under what condition it is effective, and on the other hand different FD algorithms can be compared and selected for different application scenarios. It should be highlighted that although the algorithm design and verification are for fault diagnosis systems, they can also be applied for other systems such as disturbance rejection control system among many others

Smart Hospital Innovation : Technology, Service, and Policy

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Further results on "Reduced order disturbance observer for discrete-time linear systems"

Reduced order Disturbance OBservers (DOB) have been proposed in Kim et al (2010) and Kim and Rew (2013) for continuous-time and discrete-time linear systems, respectively. The existence condition of the promising algorithm has been established but is not straightforward to check. This work further improves the reduced order DOB design by formulating it as a functional observer design problem. By carefully designing the state functional matrix, a generic DOB is resulted with an easily-checked necessary and sufficient existence condition and an easily-adjusted convergence rate. It is also shown that both the reduced order DOB in Kim and Rew (2013) and the full order DOB in Chang (2006) are special cases of this new DOB

Fault diagnosis for vehicle lateral dynamics with robust threshold

This paper investigates the robust fault diagnosis problem for vehicle lateral dynamics, which play a key role in vehicle stability and driving safety. The proposed fault diagnosis system consists of two sub-systems: fault diagnosis observer and robust threshold. By treating faults as disturbances, Disturbance/Uncertainty Estimation technique is used as fault diagnosis observer to generate residuals. Considering that residuals of model-based fault diagnosis are subject to the effect of uncertainties and consequently large false alarm rate may be resulted in, a novel robust threshold is then proposed based on reachability analysis technique for uncertain systems. The proposed fault diagnosis system is finally applied to the accelerometer and gyrometer sensor fault diagnosis problem of vehicle lateral dynamics, where initial states and velocity are considered to be uncertain. Simulation study verifies the effectiveness of the proposed fault diagnosis system

Model based fault diagnosis system verification using reachability analysis

In model-based Fault Detection and Isolation (FDI) systems, Fault Indicating Signals (FIS) such as residuals and fault estimates are corrupted by various noises, uncertainties and variations. It becomes challenging to verify whether a FDI system still works or not in real life applications. It is also challenging to select a threshold so that false alarm rate and missed detection rate are kept low depending on real operation conditions. This paper proposes solutions to the aforementioned problems by quantitatively analyzing the effect of uncertainties on FIS. The problems are formulated into reachability analysis problem for uncertain systems. The reachable sets of FIS are calculated under normal and selected faulty cases respectively. From these reachable sets, the effectiveness of a FDI system can be qualitatively verified under described uncertainties. A dedicated threshold can be further chosen to be robust to all possible described uncertainties. As a by-product, the minimum detectable fault can also be quantitatively determined by checking the intersection of the computed reachable sets. The proposed approach is demonstrated by evaluating a FDI algorithm of a motor in the presence of parameter uncertainties, unknown load and sensor noises, where a fault estimation based approach is adopted to diagnose amplifier, velocity and current sensor faults

Wheat stripe rust grading by deep learning with attention mechanism and images from mobile devices

Wheat stripe rust is one of the main wheat diseases worldwide, which has significantly adverse effects on wheat yield and quality, posing serious threats on food security. Disease severity grading plays a paramount role in stripe rust disease management including breeding disease-resistant wheat varieties. Manual inspection is time-consuming, labor-intensive and prone to human errors, therefore, there is a clearly urgent need to develop more effective and efficient disease grading strategy by using automated approaches. However, the differences between wheat leaves of different levels of stripe rust infection are usually tiny and subtle, and, as a result, ordinary deep learning networks fail to achieve satisfying performance. By formulating this challenge as a fine-grained image classification problem, this study proposes a novel deep learning network C-DenseNet which embeds Convolutional Block Attention Module (CBAM) in the densely connected convolutional network (DenseNet). The performance of C-DenseNet and its variants is demonstrated via a newly collected wheat stripe rust grading dataset (WSRgrading dataset) at Northwest A&F University, Shaanxi Province, China, which contains a total of 5,242 wheat leaf images with 6 levels of stripe rust infection. The dataset was collected by using various mobile devices in the natural field condition. Comparative experiments show that C-DenseNet with a test accuracy of 97.99% outperforms the classical DenseNet (92.53%) and ResNet (73.43%). GradCAM++ network visualization also shows that C-DenseNet is able to pay more attention to the key areas in making the decision. It is concluded that C-DenseNet with an attention mechanism is suitable for wheat stripe rust disease grading in field conditions

Simultaneous state and input estimation with partial information on the inputs

This paper investigates the problem of simultaneous state and input estimation for discrete-time linear stochastic systems when the information on the inputs is partially available. To incorporate the partial information on the inputs, matrix manipulation is used to obtain an equivalent system with reduced-order in puts. Then Bayesian inference is drawn to obtain a recursive filter for both state and input variables. The proposed filter is an extension of the recently developed state filter with partially observed inputs to the case where the input filter is also of in terest, and an extension of the Simultaneous State and Input Estimation (SSIE) to the case where the information on the inputs is partially available. A numerical example is given to illustrate the proposed method. It is shown that, due to the additional information on the inputs being incorporated in the filter design, the performances of both state and input estimation are substantially improved in comparison with the conventional SSIE without partial input information

High order disturbance observer design for linear and nonlinear systems

© 2015 IEEE. In this paper, a disturbance observer is proposed for nonlinear systems with high order disturbance, where not only disturbance but also its high order derivatives are estimated. The relationship of the proposed observer with the existing results is discussed. Then, the result is further extended to the case of minimal-order output-based disturbance observer design for linear systems subject to high order disturbances. Two practical examples about actuator fault diagnosis for a nonlinear missile system and disturbance estimation for a double-effect pilot plant evaporator system with unobservable states are provided to illustrate the effectiveness of the proposed approaches

Model-Based Fault Diagnosis System Verification Using Reachability Analysis

In model-based fault detection and isolation (FDI) systems, fault indicating signals (FISs) such as residuals and fault estimates are corrupted by various noises, uncertainties and variations. It becomes challenging to verify whether an FDI system still works or not in real life applications. It is also challenging to select a threshold so that false alarm rate and missed detection rate are kept low depending on real operation conditions. This paper proposes solutions to the aforementioned problems by quantitatively analyzing the effect of uncertainties on FIS. The problems are formulated into reachability analysis problem for uncertain systems. The reachable sets of FIS are calculated under normal and selected faulty cases, respectively. From these reachable sets, the effectiveness of an FDI system can be qualitatively verified under described uncertainties. A dedicated threshold can be further chosen to be robust to all possible described uncertainties. As a by-product, the minimum detectable fault can also be quantitatively determined by checking the intersection of the computed reachable sets. The proposed approach is demonstrated by evaluating an FDI algorithm of a motor in the presence of parameter uncertainties, unknown load, and sensor noises, where a fault estimation-based approach is adopted to diagnose amplifier, velocity, and current sensor faults

On relationship between time-domain and frequency-domain disturbance observers and its applications

This paper provides a generic analysis of the relationship between time/frequency-domain DOB design methodology. It is discovered that the traditional frequency-domain DOBs using a low pass filter with unity gain can only handle disturbances satisfying matching condition, while the traditional time-domain DOBs always generate an observer with a high order. A Functional Disturbance OBserver (FDOB) is proposed to improve the existing results together with its design guideline, frequency analysis and existence condition. Compared with the existing frequency-domain DOBs, the proposed FDOB can handle more classes of disturbances, while compared with the existing time-domain DOBs the proposed FDOB can generate an observer with a lower order. Numerical examples are presented to illustrate the main findings of this paper including a rotary mechanical system of nonminimum phase