Analytical Redundancy Relations Research Articles

Direct Bayesian inference for fault severity assessment in Digital-Twin-Based fault diagnosis

For applications in condition-based maintenance of nuclear systems, the assessment of fault severity is crucial. In this work, we developed a framework that allows for direct inference of the probability distributions of possible faults in a system. We employed a model-based approach with model residuals generated from analytical redundancy relations provided by physics-based models of the system components. From real-time sensor readings, the values of the model residuals can be calculated, and the posterior probability distributions of the faults can be computed directly using the methods of Bayesian networks. From the posterior distribution of each fault, one can estimate the fault probability based on a chosen threshold and assess the severity of the fault. By eliminating the discretization and simplifications in middle steps, this approach allows us to leverage the available computational resources to provide more accurate fault probability estimates and severity assessments.

Intermittent Fault Diagnosis and Prognosis for Steer-by-Wire System Using Composite Degradation Model

For automotive manufacturers, intelligent fault diagnosis and prognosis techniques to ensure reliability of their products become increasingly important with the introduction of Industry 4.0. In this paper, an intelligent intermittent fault diagnosis and prognosis method is proposed for steer-by-wire (SBW) system based on composite degradation model. First, a nonlinear bond graph model of the SBW system is established, by which an integrated fault signature matrix combining analytical redundancy relations and dedicated observers is developed to enhance the fault isolability. Second, in order to identify the features (i.e., fault appearing and disappearing instants) of each intermittent fault, an improved whale optimization algorithm is proposed by introducing nonlinear convergence factor, Cauchy-Gaussian mixture mutation and greedy selection strategy. After that, the composite degradation model is established to predict the remaining useful life of the component with intermittent fault, where duration feature and frequency feature are considered with the aid of observation window. Finally, the effectiveness of the proposed approach is validated by simulation and experimental results.

Implementation of a Cascade Fault Tolerant Control and Fault Diagnosis Design for a Modular Power Supply

The main objective of this research work was to develop reliable and intelligent power sources for the future. To achieve this objective, a modular stand-alone solar energy-based direct current (DC) power supply was designed and implemented. The converter topology used is a two-stage interleaved boost converter, which is monitored in closed loop. The diagnosis method is based on analytic redundancy relations (ARRs) deduced from the bond graph (BG) model, which can be used to detect the failures of power switches, sensors, and discrete components such as the output capacitor. The proposed supervision scheme including a passive fault-tolerant cascade proportional integral sliding mode control (PI-SMC) for the two-stage boost converter connected to a solar panel is suitable for real applications. Most model-based diagnosis approaches for power converters typically deal with open circuit and short circuit faults, but the proposed method offers the advantage of detecting the failures of other vital components. Practical experiments on a newly designed and constructed prototype, along with simulations under PSIM software, confirm the efficiency of the control scheme and the successful recovery of a faulty stage by manual isolation. In future work, the automation of this reconfiguration task could be based on the successful simulation results of the diagnosis method.

Fault Diagnosis of the Three-phase Asynchronous Motor Bond Graph Model Based on Bond Graph and Temporal Causal Graph

The bond graph and temporal causal graph (TCG) are proposed to diagnose the fault of three-phase asynchronous motors. The bond graph model of the three-phase asynchronous motor is constructed using the general equations in the three-phase coordinate system. The analytical redundancy relationships (ARRs) and fault signature matrix (FSM) of the three-phase asynchronous motor are derived systematically by introducing TCG to improve the system fault isolability. Simulation experiments are conducted for the rotor-turn short-circuit fault and the rotor-break bar fault of the three-phase asynchronous motor. The experimental results show that introducing TCG for fault diagnosis in this model is feasible.

Near-Optimal Decentralized Diagnosis via Structural Analysis

Health monitoring of current complex systems significantly impacts the total cost of the system. Centralized fault diagnosis architectures are sometimes prohibitive for large-scale interconnected systems, such as distribution systems, telecommunication networks, water distribution networks, or fluid power systems. Confidentiality constraints are also an issue. This article presents a decentralized fault diagnosis method that only requires the knowledge of local models and limited knowledge of their neighboring subsystems. The method, implemented in the decentralized diagnoser design ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D^{3}$ </tex-math></inline-formula> ) algorithm, is based on structural analysis and can advantageously be applied to high-dimensional systems, linear or nonlinear. Using the concept of isolation on request, a hierarchy is built according to diagnostic objectives. The resulting diagnoser is based on analytical redundancy relations (ARRs) generated along the hierarchy. Their number is optimized via binary integer linear programming (BILP) while still guaranteeing maximal diagnosability at each level. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D^{3}$ </tex-math></inline-formula> proves of lower time complexity than its centralized equivalent. It is successfully applied to a nonlinear combined cycle gas-turbine power plant.

Sensor fault diagnosis for uncertain dissimilar redundant actuation system of more electric aircraft via bond graph and improved principal component analysis

This paper develops a sensor condition monitoring method integrating the model-based bond graph (BG) technique and data-driven principal component analysis (PCA) for the dissimilar redundant actuation system of more electric aircraft with uncertain parameters. The uncertain dissimilar redundant actuation system is modeled by BG in linear fractional transformation form. After that, the analytical redundancy relations containing the nominal part and the uncertain part can be derived, based on which the adaptive thresholds and the fault signature matrix (FSM) can be obtained for robust fault detection and fault isolation. To improve the fault isolation performance under the multiple faults condition, a new fault isolation method integrating FSM and improved PCA (IPCA) is developed, where the possible fault set generated from the FSM is further refined by the IPCA module with an improved reconstruction algorithm and cyclic PCA monitoring model to achieve a more efficient fault isolation result. The effectiveness of the proposed approach is validated by simulation investigations.

Fault Detection of Landing Gear Retraction/Extension Hydraulic System Based on Bond Graph-Linear Fractional Transformation Technique and Interval Analytic Redundancy Relations

Fault detection in the landing gear retraction/extension hydraulic system is difficult due to uncertainties in component parameters and sensor measurement values. This work lies in the introduction of linear fractional transformation technology and uncertainty analysis theory for the construction of the diagnostic bond graph of the landing gear retraction/extension hydraulic system. Thus, interval analytical redundancy relations can be derived as well as fault signature matrices. By using the fault signature matrix, existing faults can be detected and isolated preliminary. Furthermore, interval analytical redundancy relations can be used to detect system faults in detail. The analysis results of the failure cases of the internal and external leakage of the actuator and landing gear selector valve reversing stuck show that compared to the traditional analytical redundancy relations, this method takes into account the negative factors of uncertainty, so it can effectively reduce missed diagnosis and misdiagnosis; compared to the traditional absolute diagnostic threshold, the interval diagnostic threshold is more accurate and sensitive.

A robust model predictive control-based method for fault detection and fault tolerant control of quadrotor UAV

In this study, first a model predictive control (MPC) solution is utilized to control the quadrotor model in both fault-free and faulty modes by employing a cost function. For this aim, the obtained non-linear continuous model has been discretized after linearization around an equilibrium point. Then, a fault detection and diagnosis (FDD) and fault tolerant control (FTC) strategy is proposed based on MPC in order to neutralize the effect of fault. The method of FDD and FTC designing is based on analytical redundancy relations (ARRs) which detects and tolerates the fault by producing a residual signal. Therefore, this method presents a complete FTC solution to fault issue without using observer. In addition, this solution has robustness against the uncertainties and disturbances which may challenge the model in practical situations. The discussed fault in this paper is a sensor fault which is modeled as an undesired function which affects the sensor of roll angle. However, the proposed FTC can tolerate all types of faults. The results of implementation of this method are validated under various simulation scenarios.

Aircraft robust data-driven multiple sensor fault diagnosis based on optimality criteria

A general robust data-driven scheme for the Fault Detection, Isolation and Estimation of multiple sensor faults is proposed and validated using multi-flight data records. Robustness to modelling uncertainty and noise is achieved through an optimized data-driven design of the three blocks that constitute the scheme. First, a robust Fault Detection (FD) filter given by the linear combination of previously identified Analytical Redundancy Relationships (AARs) is derived as the solution of a multi-objective optimization where the minimum fault sensitivity is maximized while the standard deviation (STD) of the filtered error, in nominal condition, is minimized. Then, a Fault Pre-Isolation (FPI) block is introduced to select a restricted number of sensors containing with high likelihood the subset of the faulty sensors. In this phase, robustness is achieved through the data-driven design of a redundant number of Multiple-ARRs and a voting logic. Finally, the robust Fault Isolation (FI) is achieved relying on the design of a large collection of additional AARs whose fault signatures are specifically designed to optimize, at the same time, noise immunity while maximizing the decoupling of the (pre-isolated) fault directions. A procedure based on fault amplitude reconstruction is proposed to isolate the set of faulty sensors sequentially. The proposed scheme has been applied to the design of a multiple Fault Diagnosis scheme for a set of 8 sensors of a semi-autonomous aircraft basing on multi-flight data. Validation results are compared with state-of-the-art multiple Fault Diagnosis schemes.

Distributed Diagnosis of Sensor and Actuator Faults in Air Handling Units in Multi-Zone Buildings: A Model-Based Approach

The presence of sensing and actuating equipment faults in Heating, Ventilation and Air-Conditioning (HVAC) systems may result in unsatisfactory indoor thermal conditions and significant wasted energy in the building sector. The design of fault diagnosis algorithms for multi-zone buildings is a highly challenging procedure due to the overall complexity of the system. This paper proposes a distributed model-based fault diagnosis algorithm for detecting, isolating and identifying faults that can affect the actuating devices of Air Handling Units (AHUs) and the sensor devices. Based on modeling the temperature dynamics of each local AHU and its underlying zone with respect to its neighboring zones, a local fault diagnosis agent that collects local control signals and local and neighboring sensor measurements is designed. Each local diagnosis agent is designed to estimate on-line the states of each AHU. For each estimation, a residual signal is derived that corresponds to the difference between the measured and estimated value at each time step. Considering healthy conditions, the residual is bounded by its corresponding adaptive threshold that is calculated considering the effects of modeling uncertainties. Each corresponding pair of residual and adaptive threshold forms an analytical redundancy relation which indicates the possible presence of one or more faults. Exploiting the dependency of each actuator and sensor fault with each Analytical Redundancy Relation (ARR), a decision logic is designed, that can reveal under certain conditions the location and type of the fault during the operation of the system. The proposed algorithm is evaluated using a simulation example of a primary school building with 25 zones.

LFT Bond Graph for Online Robust Fault Detection and Isolation of Hybrid Multi-Source System

Green hydrogen is undoubtedly the most promising energy vector of the future because it is captured by renewable and inexhaustible sources, such as wind and/or solar energy, and can be stored over the long in high-pressure cylinders, which can be used to feed the fuel cells to produce the electricity without emitting any pollutants. The system incorporated renewable sources and process used to produce the green hydrogen is the hybrid multi-source system (HMS). The production of hydrogen needs a reliable HMS, which always requires online monitoring for real-time Fault Detection and Isolation (FDI) because the risk of accidents in HMS and safety issues increases due to the possibility of faults. However, online monitoring of FDI is challenging due to the multi-physics dynamics of HMS and the inclusion of uncertain parameters and several disturbances. This paper proposes an online robust fault detection algorithm to detect system faults based on the properties of the graphical linear fractional transformation bond graph (LFT-BG) modeling approach. Here, the analytical redundancy relations (ARRs) and their uncertain parts extracted from the LFT-BG model are used to develop an online robust FDI algorithm for HMS. Numerical evaluations of ARRs and their uncertain parts, respectively, generate the residual signals known as “faults indicators” and their uncertain bounds known as “adaptive thresholds.” These thresholds evolve with system variables in the presence of parameter uncertainties for ensuring robust FDI for HMS to minimize false alarms. The validation of this approach is carried out using 20sim software that is familiar with BG modeling.

Robust Detection of Minute Faults in Uncertain Systems Using Energy Activity

Fault detection is one of the key steps in Fault Detection and Isolation (FDI) and, therefore, critical for subsequent prognosis or implementation of Fault Tolerant Control (FTC). It is, therefore, advisable to utilize detection algorithms which are quick and can detect the smallest faults. Model-based detection methods satisfy both these criteria and should be preferred. However, a big limitation for model-based methods is that they require the accurate value of the component parameters, which is difficult to obtain in real situations. This limits the accuracy of model-based methods. This paper proposes a new method for fault detection using Energy Activity (EA) which can detect minute levels of fault in systems with high component uncertainty. Different forms of EA are developed for use as an FDI metric. The proposed forms are simulated using a two-tank system under various types of faults. The results are compared with each other and with the traditional model-based FDI method using Analytical Redundancy Relations (ARRs). The simulations are performed considering model uncertainties to check the inherent performance of the methods. From initial simulations, it is established that the integral form of EA is most suited for fault detection. The integral for if EA is then tested using a real two-tank system considering both the model and measurement uncertainties.

Remaining Useful Life Prediction of Aircraft Flap Control System with Mode Transition

Hybrid system is a dynamic system with mode transition, and the degradation behavior of its components is varied with different operating modes. However, the recent prognosis methodologies treat the degradation of different operating modes as the same and ignore their differences or even do not consider degradation in idle mode. These methods have a significant error on remaining useful life (RUL) prediction of aircraft flap control system, which is a typical hybrid system and also vital for flight safety. In response to this problem, this paper proposes a method based on bond graph, which integrates the mode transition information into global analytical redundancy relations. Here, the mode-dependent fault signature matrix is constructed and different degradation models are identified. Finally, simulation experiments are done, and the RUL prediction value of the proposed method is closer to the preset value than that of the method without considering degradation of the idle mode.

Computational Intelligence-Based Prognosis for Hybrid Mechatronic System Using Improved Wiener Process

In this article, a fast krill herd algorithm is developed for prognosis of hybrid mechatronic system using the improved Wiener degradation process. First, the diagnostic hybrid bond graph is used to model the hybrid mechatronic system and derive global analytical redundancy relations. Based on the global analytical redundancy relations, the fault signature matrix and mode change signature matrix for fault and mode change isolation can be obtained. Second, in order to determine the true faults from the suspected fault candidates after fault isolation, a fault estimation method based on adaptive square root cubature Kalman filter is proposed when the noise distributions are unknown. Then, the improved Wiener process incorporating nonlinear term is developed to build the degradation model of incipient fault based on the fault estimation results. For prognosis, the fast krill herd algorithm is proposed to estimate unknown degradation model coefficients. After that, the probability density function of remaining useful life is derived using the identified degradation model. Finally, the proposed methods are validated by simulations.

Robust Model-Based Fault Detection Using Monte-Carlo Methods and Highest Density Regions

One of the major challenges in model based fault detection is the robust design of thresholds for the analytical redundancy relations. Those relations are residuals which differ from a zero in case of a fault and are equal to zero in the fault free case. In real world applications, however, these residuals usually differ from zero even in the fault free case due to, e.g. measurement errors and model uncertainties. This paper proposes a method based on Monte-Carlo simulations of possible residuals taking into account uncertainties as a-priori probability distributions. The statistical analysis of the resulting residual's probability distribution using posterior highest density regions enables a likelihood-based decision about the occurrence of a fault. The presented method is demonstrated and evaluated using a nonlinear physical model of an air cooling system of an unmanned aerial vehicle.

Analytical redundancy relationship generation on a progressive horizon for fault diagnosis of a labelled Petri net

Abstract In this article, a diagnosis approach for partially observed labelled Petri nets is developed based on building a set of analytical redundancy relationships on a progressive horizon. A nominal model is used for fault detection based on a set of relationships linking the known data of the nominal behaviour. A fault model is used for fault isolation by establishing a set of relationships for each fault transition connecting the known data of the fault behaviour. The above-mentioned analytical redundancy relationships are established offline by eliminating unknown variables from the considered model. The proposed online procedure for fault diagnosis is polynomial with respect to the number of unobservable events.

Fault diagnosis and prognosis of steer-by-wire system based on finite state machine and extreme learning machine

In this paper, an integrated condition monitoring method combining model-based fault diagnosis and data-driven prognosis is proposed for steer-by-wire (SBW) system. First, the SBW system is modeled by bond graph (BG) technique and a two-degree-of-freedom (2-DOF) state-space model of the vehicle is built. Based on the 2-DOF model, the estimated self-aligning torque is used for the control of feedback motor. The fault detection is carried out by evaluating the analytical redundancy relations derived from the BG model. Since the fault isolation performance is essential to subsequent fault estimation process, a new fault isolation method based on finite state machine is developed to improve the isolation ability by combining the dependent and independent analytical redundancy relations, where the number of potential faults could be decreased. In order to refine the possible fault set to determine the true fault, a cuckoo search (CS)–particle filter is developed for fault estimation. Based on the estimated true fault, prognosis can be implemented which is important to achieve failure prevention and prolong system lifespan. To this end, an optimized extreme learning machine (OELM) is proposed where the input weights and hidden layer biases are optimized by CS. Based on data representing fault values obtained from the fault identification, the OELM model is trained for remaining useful life prediction of failing component. Finally, the proposed methodologies are validated by simulations.

Optimized fault detection using bond graph in linear fractional transformation form

This article deals with the optimal robust fault detection problem using the bond graph in its linear fractional transformation form. Generally, this form of the bond graph allows the generation of two perfectly separate analytical redundancy relations, that are used as residual and threshold. However, the uncertainty calculation method gives overestimated thresholds. This may, for instance, lead to undetectable faults. Therefore, enhancing the robustness of fault detection and isolation algorithms is of utmost importance in designing a bond graph–based fault detection system. The main idea of this article is to develop optimized thresholds to ensure an optimal detection, otherwise this article proposes a method to detect tiny magnitude faults concerning parameter’s uncertainties. This work considers the issue of optimal fault detection as an optimization problem of the gap between the residuals and its threshold. New uncertainty values will be calculated in a way that these estimated parameters ensure the desired optimized gap between residuals and thresholds. These estimated uncertainty values will be used to generate optimized adaptive thresholds. Through these thresholds, we increase the sensitivity of the residuals to tiny magnitude faults, and we ensure an optimal and early detection.

Modulation-Function-Based Finite-Horizon Sensor Fault Detection for Salient-Pole PMSM using Parity-Space Residuals

An online model-based fault detection and isolation method for salient-pole permanent magnet synchronous motors over a finite horizon is proposed. The proposed approach combines parity-space-based residual generation and modulation-function-based filtering. Given the polynomial model equations, the unknown variables (i.e. the states, unmeasured inputs) are eliminated resulting in analytic redundancy relations used for residual generation. Furthermore, in order to avoid needing the derivatives of measured signals required by such analytic redundancy relations, a modulation-function-based evaluation is proposed. This results in a finite-horizon filtered version of the original residual. The fault detection and isolation method is demonstrated using simulation of various fault scenarios for a speed controlled salient motor showing the effectiveness of the presented approach.

An Analytical Robust Sensor Fault Detection and Fault Tolerant Control in Quadrotor

Nowadays, because of advancements in technology and increasing use of machine for daily tasks, the optimal use of these devices has become an important issue. Quadrotor is one of tools that are widely used today in various fields of military, agriculture, meteorology, researches, transportation and so on. In this paper, our model is a quadrotor. One of the issues that threaten the optimal performance of today’s machines is fault. The fault is any undesired factor that disables the system’s performance. The fault can be applied to different parts of an industrial system, including system actuators, sensors or parameters. In this paper, we intend to investigate the sensor fault in a quadrotor model. So that, first the sensor fault is detected and then the fault tolerant control (FTC) algorithm is designed to modify the control signals and eliminate the effect of this fault. The method presented in this paper is an analytical method named analytical redundancy relation (ARR) that can easily detect the fault occurrence and design the fault tolerant control algorithm by using residual signals. Another advantage of this method is its robustness against disturbance and uncertainty in system parameters.