Analytical Redundancy Research Articles

Competitive degradation process based monitoring of steer-by-wire system under unknown degradation features

Abstract Monitoring of steer-by-wire (SBW) system under intermittent faults is challenging since it is difficult to obtain the priori knowledge of monotonous degradation feature of intermittent fault due to its stochastic nature. To deal with this issue, this paper develops a prognosis method for the SBW system under unknown degradation features based on the concept of competitive degradation process (CDP). With the aid of fault diagnosis module, the possible faulty components can be determined, where the fault isolation estimators and the expanded analytical redundancy relations are used to improve the isolabilities of sensor faults. For each faulty component, three degradation features are defined and the monotonicity of each feature is unknown beforehand. To judge the monotonicity of the feature, the competition index is developed in the CDP and the feature is applicable (i.e. monotonous) if the predefined criterion can be satisfied using the established dataset of the feature. Once the applicable features are determined, the corresponding degradation models are built for remaining useful life prediction. Experiment results are presented to illustrate the performance of the proposed method.

Research on fault diagnosis of multi-mode electromechanical compound transmission system for hybrid electric vehicle based on global analytical redundancy relations

In order to effectively monitor the operational status and detect faults within a hybrid electric vehicle during mode transitions, this study focuses on a specific power-split hybrid electric vehicle model known as the multimode electromechanical composite transmission system (MM-EMCTS HEV). Taking into account the actual occurrence frequency and simulatability of faults in MM-EMCTS HEV, key faults are extracted. Based on the distinctive characteristics and properties of critical system faults, a fault diagnosis framework is constructed by leveraging global analytical redundancy relations (GARRs). This framework encompasses strategies for residual generation across sensors, actuators, and controlled components, alongside residual evaluation techniques employing mode-dependent adaptive thresholds. The detectability and isolatability of the MM-EMCTS are thoroughly analyzed. Subsequently, simulation and verification of the residual responses under typical faults in the MM-EMCTS are implemented in Matlab/Simulink and Simscape. The results demonstrate the efficacy of the fault diagnosis method, rooted in global analytical redundancy relations and mode-dependent adaptive thresholds, in successfully detecting and isolating faults within multimode electromechanical composite transmission systems. Furthermore, compared to conventional fixed threshold residual evaluation approaches, this method significantly mitigates the occurrence of missed detections, thus verifying the effectiveness and superiority of the proposed method.

AIoT-enabled decentralized sensor fault diagnosis for structural health monitoring

Structural health monitoring (SHM) systems collect and analyze data recorded from civil infrastructure to detect changes in the structural condition and to predict potential damage. However, sensor faults in SHM systems may lead to erroneous structural assessments, thus to inefficient maintenance operations, which could jeopardize the integrity of the structures. Artificial intelligence (AI) algorithms based on analytical redundancy have proven being effective foundations to implement sensor fault diagnosis but are computationally expensive and require large amounts of raw sensor data to be transmitted to centralized servers. Moreover, operating with centralized servers increases the likelihood of single points of failure, which compromises the robustness of SHM systems. To overcome the limitations and to enhance the robustness of SHM systems, this paper proposes a decentralized approach towards fault-tolerant SHM systems based on the concept of Artificial Intelligence of Things (AIoT). Fault diagnosis algorithms based on AI are embedded into sensor nodes deployed for SHM, using Internet of Things technologies to be computed at the edge of civil infrastructure, avoiding large amounts of data to be sent to central servers. In addition, the sensor nodes embed algorithms that collect, process, and analyze the sensor data to assess the condition of the structures being monitored. The result is a smart (i.e., decentralized, fault-tolerant, and AIoT-based) SHM system that is calibrated in a laboratory experiment and then deployed on a bridge for field validation. It is expected that the approach presented in this paper will improve the robustness of SHM systems in assessing the structural condition of civil infrastructure.

Diagnosis of a stand-alone photovoltaic installation by the Analytical Redundancy Relationship method (ARR)

Diagnosis of a stand-alone photovoltaic installation by the Analytical Redundancy Relationship method (ARR)

A Hybrid Fault Diagnosis Method for Autonomous Driving Sensing Systems Based on Information Complexity

In the context of autonomous driving, sensing systems play a crucial role, and their accuracy and reliability can significantly impact the overall safety of autonomous vehicles. Despite this, fault diagnosis for sensing systems has not received widespread attention, and existing research has limitations. This paper focuses on the unique characteristics of autonomous driving sensing systems and proposes a fault diagnosis method that combines hardware redundancy and analytical redundancy. Firstly, to ensure the authenticity of the study, we define 12 common real-world faults and inject them into the nuScenes dataset, creating an extended dataset. Then, employing heterogeneous hardware redundancy, we fuse MMW radar, LiDAR, and camera data, projecting them into pixel space. We utilize the “ground truth” obtained from the MMW radar to detect faults on the LiDAR and camera data. Finally, we use multidimensional temporal entropy to assess the information complexity fluctuations of LiDAR and the camera during faults. Simultaneously, we construct a CNN-based time-series data multi-classification model to identify fault types. Through experiments, our proposed method achieves 95.33% accuracy in detecting faults and 82.89% accuracy in fault diagnosis on real vehicles. The average response times for fault detection and diagnosis are 0.87 s and 1.36 s, respectively. The results demonstrate that the proposed method can effectively detect and diagnose faults in sensing systems and respond rapidly, providing enhanced reliability for autonomous driving systems.

Tree based Diagnosis Enhanced with Meta Knowledge Applied to Dynamic Systems

This paper presents an online data and knowledge-based diagnosis method. It leverages decision trees where decisions are informed by diagnosis meta knowledge, specifically focusing on the properties of diagnosis indicators. This knowledge is used at each node to articulate a symbolic classification problem, outputting discriminating functions. The outcome is a multivariate decision tree that produces a compact model for diagnosis. The use of decision trees increases the explainability of the outcome, all the more so as one discovers the explicit formal expressions of diagnosis indicators, structured in the form of analytical redundancy relations. This article centers on the essential components required for the method to be suitable for dynamic systems. It is tested on the well-known two-tank system, showing that the performances match those of model-based diagnosis.

Fault detection with confidence level evaluation for perception module of autonomous vehicles based on long short term memory and Gaussian Mixture Model

The reliability of perception is crucial for developing higher levels of autonomy to enhance the safety and performance of fully autonomous vehicles. As a result, fault detection in the perception module is an essential function for autonomous vehicles. While many approaches rely on hardware redundancy, necessitating additional sensors, achieving analytical redundancy in perception is a pivotal factor in reducing the cost and complexity of the autonomous system. This paper utilizes a motion predictor for surrounding vehicles and a Gaussian Mixture Model (GMM) to evaluate the confidence level of the perception module without requiring additional sensor installations. The motion predictor is designed based on a long short-term memory-based recurrent neural network. The error between the motion prediction and detection results is leveraged to estimate the confidence level of the perception module. In essence, the motion prediction results are treated as redundant sensor measurements. The error distribution is modeled using a GMM, and the cumulative probability density of the GMM is employed to assess the confidence level. The proposed algorithm's effectiveness is evaluated through a driving data-based simulation study with fault injection. This study shows an enhanced sensitivity to injected faults and a reduced time for fault detection. Furthermore, the proposed algorithm provides a quantitative estimate of the performance degradation level of the perception module. This estimation can serve as an indicator of uncertainty that the motion planner should account for.

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.

Predictive Hybrid Redundancy for Aircraft Wheel Braking System - A Design Study

Aircraft brake system plays an important role in safe take-off and landing phases of flight. In order to meet safety requirements, regulatory guidelines like ARP 4761 suggest duo duplex architecture for the aircraft Wheel Braking System (WBS). Safe and efficient braking systems for ground-based vehicles in the name of Brake By Wire (BBW) are also becoming very common resulting in significant research activity in the recent past. In order to reduce number of hardware components and to improve reliability further of BBW systems, Kim and team of Korean university came up with introduction of analytical redundancy for measurement of wheel angular velocity sensors. The scheme involves Predictive Hybrid Redundancy (PHR) for fault detection using a double exponential smoothing method. Since, such systems are not readily suitable for aircraft WBS, the present study is an attempt to extend this approach to aircraft WBS with appropriate modifications to integrate with normal mode, standby mode and emergency modes of aircraft WBS. Simulation studies for synthetic sensor signals show possibility of continued operation, with one sensor being operational, in the presence of transient, intermittent and hard over faults.

Detection of Sensor Faults with or without Disturbance Using Analytical Redundancy Methods: An Application to Orifice Flowmeter.

Sensors and transducers play a vital role in the productivity of any industry. A sensor that is frequently used in industries to monitor flow is an orifice flowmeter. In certain instances, faults can occur in the flowmeter, hindering the operation of other dependent systems. Hence, the present study determines the occurrence of faults in the flowmeter with a model-based approach. To do this, the model of the system is developed from the transient data obtained from computational fluid dynamics. This second-order transfer function is further used for the development of linear-parameter-varying observers, which generates the residue for fault detection. With or without disturbance, the suggested method is capable of effectively isolating drift, open-circuit, and short-circuit defects in the orifice flowmeter. The outcomes of the LPV observer are compared with those of a neural network. The open- and short-circuit faults are traced within 1 s, whereas the minimum time duration for the detection of a drift fault is 5.2 s and the maximum time is 20 s for different combinations of threshold and slope.

Fault Detection for PEM Fuel Cells via Analytical Redundancy: A Critical Review and Prospects

Decarbonization of the transport sector could be achieved through fuel cell technology. The candidature of this technology is motivated by its high current density and lack of emissions. However, its widespread deployment is restrained by durability and reliability constraints. During normal operation, the fuel cell system supplies stable power to the load. Contrarily, when it is operated under faulty conditions, the system’s output power deteriorates, leading to low durability. It is therefore of paramount importance to ensure that the system is operated in a non-faulty condition. In this paper, we provide a critical review of the analytical-redundancy-based fault diagnosis methods for proton exchange membrane fuel cells (PEMFCs). An in-depth analysis of the various methods has been presented in terms of accuracy, complexity, implementability, and robustness to aging and dynamic operating conditions.

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.

A novel analytical redundancy method based on decision-level fusion for aero-engine sensors

A novel analytical redundancy method based on decision-level fusion for aero-engine sensors

Analytical Redundancy for Variable Cycle Engine Based on Variable-Weights-Biases Neural Network

Due to the complex nature of a variable cycle engine (VCE), which has numerous control variables and working modes across a broad flight envelope, coupled with the whole engine’s degradation, the analytical redundancy method based on component-level models may not provide an accurate estimation of the sensors. Variable-weights-biases neural network (VWB Net) is proposed to construct VCE’s analytical redundancy. Unlike conventional networks whose weights and biases are fixed, VWB Net’s variable-weights and variable-biases are functions of input which greatly increase its nonlinear mapping capability by integrating input information. Variable-biases can also be used to eliminate the error between actual sensor output and estimated value quickly at the terminal node. Compared with the BP network and Dense net, VWB Net has fewer parameters, faster calculation speed, and higher accuracy. Digital simulation results of VCE parameter estimation demonstrate that VWB Net’s average relative errors are under 0.27% with calculation and parameter efficiency at least 166 times higher than that of Dense net. Hardware in the loop simulation further verifies VWB Net’s estimation accuracy and real-time calculation.

Robust fault detection and diagnosis of primary air data sensors in the presence of atmospheric turbulence

Abstract This paper presents a fault detection and diagnosis (FDD) algorithm for various faults in the primary air data sensors (PADS) of an aircraft in the presence of external disturbances such as atmospheric turbulence. Rapid wind variations due to turbulence induce excessive error in the externally fitted air data probe measurements, which may lead to loss of control and misinterpretations by the flight crew. In adverse environmental conditions, the FDD of air data prefers robust and adaptive air data estimates that use an analytical redundancy approach with fewer computations. The proposed method considers the kinematics of the aircraft instead of the dynamics used in the state-of-the-art algorithms. The advantage of using kinematics is that it can reduce modeling errors significantly, avoiding high false alarm rates in the FDD process. For the estimation of stable and accurate air data under external disturbance, the inertial navigation system and global positioning system (INS/GPS) output are considered instead of actual air data probe or sensor measurements. The proposed algorithm uses estimates of air data using an exponentially weighted adaptive extended Kalman filter (EW-AEKF) to detect and diagnose PADS faults, which can perform well even in the presence of uncertain noise due to atmospheric turbulence experienced during flight. The simulation was carried out to validate the algorithm with flight data obtained from the X-Plane flight simulator under moderate atmospheric turbulence. The simulation experiments were carried out using the MATLAB programming platform. The results show that the proposed method achieves satisfactory FDD performance with lower root mean square error (RMSE) and computation time than traditional EKF-based algorithms.

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.

GAS turbine sensor fault diagnostic system in a real-time executable digital-twin

In this study, a sensor fault diagnostic system to detect/isolate and accommodate faults in sensors from an industrial gas turbine has been developed. The sensor fault diagnostic module is integrated with a gas turbine real-time executable digital-twin (RT xDT) reported in a previous study. The sensor fault diagnostic module of the digital-twin considers analytical sensor redundancy using a reference engine model to provide redundant estimates of measured engine variables. A Software-in-the-Loop (S-i-L) architecture and Hardware-in-the-Loop (H-i-L) facility are constructed to assess the sensor diagnostic module (fault detection/ fault isolation) during failure in sensors from the engine. The results demonstrated that if the discrepancy between virtual measurement (provided by digital-twin) and sensor measurement exceeds the prescribed tolerance levels, the sensor fault diagnostic logic determines the state of switching between the virtual and engine sensor measurements in a dual lane control configuration of the gas turbine control system. The sensor fault detection system implemented in the gas turbine RT xDT can be deployed onto a distributed control system of industrial gas turbines to diagnose sensor deficiencies and ensure continuous and safe operation of the gas turbine. Consequently, the developed system will increase engine availability and reliability by diagnosing engine operational deficiencies before severe failure.

Real-Time Sensor Fault Identification and Remediation for Single-Phase Grid-Connected Converters Using Hybrid Observers With Unknown Input Adaptation

Unexpected faults that occur in the sensors of the single-phase grid-connected converter (SGC) may deteriorate the control performance ofthe grid current and dc-link voltage and pose risks to the connected distributed generation system. This article proposes an unknown input sliding mode observer (UI-SMO) based sensor fault identification and remediation strategy. Therein, an UI-SMO is established that integrates the observations of the grid current and dc-link voltage with the reconstruction of the unknown input (i.e., load condition of the SGC). Such an observer-based analytical redundancy generates the residuals that indicate the abnormalities of the sensors for fault identification. An adaptive fault-tolerant control (AFTC) strategy for the SGC is developed, where an automated control system reconfiguration with unknown input adaptation is seamlessly implemented by substituting the data reported by the faulty sensors with their observed values. Extensive simulation and experimental studies validate the effectiveness of the proposed UI-SMO-based sensor fault identification and AFTC strategy of the SGC.

Adaptive Fault Diagnosis for Simultaneous Sensor Faults in Structural Health Monitoring Systems

Structural health monitoring (SHM) is a non-destructive testing method that supports the condition assessment and lifetime estimation of civil infrastructure. Sensor faults may result in the loss of valuable data and erroneous structural condition assessments and lifetime estimations, in the worst case with structural damage remaining undetected. As a result, the concepts of fault diagnosis (FD) have been increasingly adopted by the SHM community. However, most FD concepts for SHM consider only single-fault occurrence, which may oversimplify actual fault occurrences in real-world SHM systems. This paper presents an adaptive FD approach for SHM systems that addresses simultaneous faults occurring in multiple sensors. The adaptive FD approach encompasses fault detection, isolation, and accommodation, and it builds upon analytical redundancy, which uses correlated data from multiple sensors of an SHM system. Specifically, faults are detected using the predictive capabilities of artificial neural network (ANN) models that leverage correlations within sensor data. Upon defining time instances of fault occurrences in the sensor data, faults are isolated by analyzing the moving average of individual sensor data around the time instances. For fault accommodation, the ANN models are adapted by removing faulty sensors and by using sensor data prior to the occurrence of faults to produce virtual outputs that substitute the faulty sensor data. The proposed adaptive FD approach is validated via two tests using sensor data recorded by an SHM system installed on a railway bridge. The results demonstrate that the proposed approach is capable of ensuring the accuracy, reliability, and performance of real-world SHM systems, in which faults in multiple sensors occur simultaneously.

Fault Detection in Aircraft Flight Control Actuators Using Support Vector Machines

Future generations of flight control systems, such as those for unmanned autonomous vehicles (UAVs), are likely to be more adaptive and intelligent to cope with the extra safety and reliability requirements due to pilotless operations. An efficient fault detection and isolation (FDI) system is paramount and should be capable of monitoring the health status of an aircraft. Historically, hardware redundancy techniques have been used to detect faults. However, duplicating the actuators in an UAV is not ideal due to the high cost and large mass of additional components. Fortunately, aircraft actuator faults can also be detected using analytical redundancy techniques. In this study, a data-driven algorithm using Support Vector Machine (SVM) is designed. The aircraft actuator fault investigated is the loss-of-effectiveness (LOE) fault. The aim of the fault detection algorithm is to classify the feature vector data into a nominal or faulty class based on the health of the actuator. The results show that the SVM algorithm detects the LOE fault almost instantly, with an average accuracy of 99%.