Sensor fault-tolerant attitude determination system based on the nonlinear interacting-multiple-model approach

Recursive Actuator Fault Detection and Diagnosis for Emergency Landing of UASs

Fault Detection and Diagnosis (FDD) in District Heating (DH) systems is vital for improving operational efficiency. As DH networks evolve towards Fourth-Generation District Heating (4GDH), their reliance on lower operational temperatures intensifies the need for robust FDD. However, implementing effective FDD faces challenges due to the lack of labelled fault data and the complexity of DH substations. This paper introduces a novel FDD methodology using Transfer Learning (TL) to bridge the gap between faulty bearings, controlled DH substation experiments and real-world operational data. We propose a fault signature method that aligns the data to reduce the domain gap, revealing similarities between faulty bearings’ vibration patterns and Δ T readings in DH substations. The TL-enhanced models demonstrated robust performance, achieving F1 scores up to 98% on lab data and 91% on real-world operational data, respectively. These results mark a notable advancement in FDD of DH substations, as our method offers accurate fault detection and valuable insights across diverse operational contexts, ranging from valve issues, faulty sensors, wrong control strategies and normal behaviour. Notably, temperature dynamics resemble behaviour akin to faulty bearing vibrations, highlighting their potential as a critical indicator of a faulty substation, enabling more effective FDD in DH systems. • Transfer learning framework using faulty-bearing, lab-emulated district heating, and real-world district heating data for substation fault detection. • A novel daily-signature generation pipeline bridging mechanical vibration and thermal temperature patterns. • The temperature signatures exhibit high variance and vibration-like patterns when a substation is faulty, as opposed to a stable, normal operational substation. • Models achieve high fault detection performance with an F1 score of 98% on lab emulation and 91% on real-world DH substation data.

This paper presents a new scheme for fault detection and diagnosis in spacecraft Attitude Determination (AD) sensors. An integrated attitude determination system, which includes measurements of rate and angular position using rate gyros and vector sensors, is developed. Measurement data from all sensors are fused by a linearized Kalman filter, which is designed based on the system kinematics, to provide attitude estimation and the values of the gyro bias. Using this information the erroneous sensor measurements are corrected, and unbounded sensor measurement errors are avoided. The resulting bias-free data are used in the Fault Detection and Diagnosis (FDD) scheme. The FDD algorithm uses model-based state estimation, combining the information from the rotational dynamics and kinematics of a spacecraft with the sensor measurements to predict the future sensor outputs. Fault isolation is performed through Extended Kalman Filters (EKFs). The innovation sequences of EKFs are monitored by several statistical tests to detect the presence of a failure and to localize the failures in all AD sensors. The isolation procedure is developed in two phases. In the first phase, two EKFs are designed, which use subsets of measurements to provide state estimates and form residuals, which are used to verify the source of the fault. In the second phase of isolation, testing of multiple hypotheses is performed. The generalized likelihood ratio test is utilized to identify the faulty components. In the scheme developed in this paper a relatively small number of hypotheses is used, which results in faster isolation and highly distinguishable fault signatures. An important feature of the developed FDD scheme is that it can provide attitude estimations in presence of fault in one type of sensors.

Fault Detection and Diagnosis (FDD) in complex industrial systems requires methods that can handle uncertain operating conditions, soft thresholds, evolving sensor behavior, and increasing volumes of heterogeneous data. Traditional model-based or rule-driven approaches offer interpretability but lack adaptability, while purely data-driven and Large Language Model (LLM)-based methods often struggle with consistency, traceability, and causal grounding. Dynamic Master Logic (DML) provides a causal and temporal reasoning structure with fuzzy rules that capture gradual drift, soft limits, and asynchronous sensor signals while preserving traceability and deterministic evidence propagation. Building on this foundation, this paper presents a DML-LLM hybrid architecture that integrates targeted LLM inference to interpret unstructured information such as logs, notes, or retrieved documents under controlled prompts that maintain domain constraints. The combined system integrates Bayesian updating, deterministic routing, and semantic interpretation into a unified FDD pipeline. In a semiconductor manufacturing case study, the proposed framework reduced time to detection (TTD) from 7.4 h to 1.2 h and improved the F1 score from 0.59 to 0.83 when compared with conventional Statistical Process Control (SPC) and Fault Detection and Classification (FDC) workflows. Provenance completeness increased from 18% to 96%, while engineer triage time was reduced from 72 min to 18 min per event. These results demonstrate that the hybrid framework provides a scalable and explainable approach to anomaly detection and fault diagnosis in sensor-rich industrial environments.

In this paper, a high-gain nonlinear observer based fault detection and diagnosis (FDD) approach is proposed for a general class of nonlinear uncertain systems with measured output probability density functions (PDFs). The objective of the presented FDD algorithm is to use the measured output probability density functions (PDFs) and the input of the system to construct a exponential observer-based residual generator such that the fault can be detected and diagnosed. The main result is given in a constructive manner by developing a novel nonlinear observer, without resort to any linearization. By a coordinates transformation, the design of the proposed observer does not necessitate the resolution of kind of linear matrix inequalities (LMIs) and its expression is explicitly given. The exponential convergence of the errors in the presence of parameters uncertainties is proved to guarantee the fastness of the proposed fault diagnosis scheme. Furthermore, the bound of the estimation errors in the presence the faults is minimized by appropriately choosing the parameters of the presented observer. Finally a simulation example is given to illustrate the efficiency of the proposed fault detection and diagnosis method.

Hybrid modeling based double-granularity fault detection and diagnosis for quadrotor helicopter

Electric Vehicles (EVs) are emerging as the preferred mode of transportation owing to various advantages and to reach the Sustainable Development Goals (SDGs). However, the commercial usage of EVs is in its initial stage, due to multiple factors, among which faults caused by the operational environment and component failures are one. Furthermore, early diagnosis of such faults is necessary to protect from severe damage and catastrophic failures. This paper presents various fault modes in EV components, their impact on EV performance, and Fault Detection and Diagnosis (FDD) practices. Later sections of the paper focus on the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques in FDD. Also, it provides a comparative analysis of model-based, signal-based, data-driven, and hybrid FDD approaches. Additionally, the paper explores component-specific FDD techniques, modelling of drive faults, current research trends, and suggestions on the selection of feature extraction techniques. Furthermore, the paper highlights the need to develop a hybrid FDD method to enhance the accuracy, reliability, and speed of FDD in EVs. The future scope of EV FDD techniques, considering various aspects, is also presented.

Feasibility and improvement of fault detection and diagnosis based on factory-installed sensors for chillers

A data-driven fault detection and diagnosis scheme for air handling units in building HVAC systems considering undefined states

When nuclear power plants (NPPs) are in failure state, it may release radioactive substance into the environment. Thus, the safety of NPPs is put forward a high standard. Fault detection and diagnosis (FDD) are significant for NPPs to help operator timely know the state of system and provide the online guidance. Fault diagnosis can improve the safety of nuclear power plants, but current fault diagnosis methods pay too much attention to accuracy of diagnostic results. As a complex industrial system, how to explain the causes of faults in NPPs becomes more important. Although there are many studies on the knowledge graph, there is no detailed description on the failure process (consider timing). This paper proposed a three-layer structure for FDD in NPPs. Each layer represents the stage of the accident, it can give the operator a clear cognitive process to faults. The three-layer structure includes “smooth layer”, “threshold layer” and “fault layer”. The three layers indicate the reason of faults, the response of the parameters at each stage, and clearly showing the accident process. The smooth layer uses the stability analysis to analyze whether the current NPP is operating abnormally; the threshold layer uses the thresholds of the NPP to monitor which parameters have exceeded the upper limit or the lower limit; the fault layer reflects what is happening in the current operation and accidents are explained using signed directed graph. This paper takes the loss of coolant accident as an example, three-layer structure is analyzed, which shows the feasibility of the method. The case shows that the proposed method is superior to the conventional SDG method, can diagnose the faults, and give the reason of diagnosis results.

This report summarizes a comprehensive literature review for the selected sensor suites used in building controls and fault detection and diagnostics (FDD), which is a Q2 deliverable of the Sensor Impact Evaluation and Verification project. The project’s overarching goal is to develop a framework for investigating the impact of sensor deployment and configuration for building energy optimization, fault detection and diagnosis (FDD), occupants’ thermal comfort, and potential grid efficiency. While the first phase of the literature review performed in Q1 reviewed the current methods of various sensor selection and placements and their impact, this report focuses on high impactful potential use cases for the selected sensor suites in building control and FDD. The use cases identified in this literature review will be further investigated and be included in the final sensor impact evaluation framework. For the review, the multi-laboratory team reviewed 241 research papers, technical reports, and books to identify existing technologies, the potential impact on energy savings and thermal comfort, and future research opportunities for the selected use cases in building controls and FDD. Along with the literature review, the team conducted a series of interviews with experts in sensors and controls area to identify the state-of-art sensor technologies, and the most impactful use cases in controls and FDD areas. The interview results will also be incorporated with the findings from the literature review in the next quarter, and the final use cases to be evaluated will be selected.

This paper presents a fault detection and diagnosis (FDD) method to enhance the reliability and safety for longitudinal control of an autonomous all-terrain vehicle (ATV). An integrated approach using decentralized and centralized FDD is proposed to optimize the tradeoff between sensitivity and robustness. While the decentralized approach is suitable for detecting faults in actuators and sensors directly connected to a single processor, it is sensitive to noises and disturbances and thus may result in false alarms. On the other hand, the centralized approach is based on information communicated between multiple processors, and it detects and diagnoses faults through analyzing concurrent computations of multiple hardware modules. However, its performance is still limited to isolating faults specifically in terms of components in the single hardware. To incorporate the advantages of both FDD approaches, a two-layered structure integrating both decentralized and centralized FDD is proposed and allows us to perform more robust fault detection as well as more detailed fault isolation. Finally, the proposed method is validated experimentally via field tests of an ATV.

Various faults occurred in the heating, ventilation and airconditioning (HVAC) systems usually lead to more energy consumption and worse thermal comfort inevitably. This paper presents a feasible and valid solution of HVAC fault detection and diagnosis (FDD) problem based on statistical machine learning technology. It learns the consistent nature of different types of faults of HVAC operation based on support vector machine (SVM), and then identify types of fault in all subsystems using the statistical relationships between groups of measurements. In order to speed up the learning process, principle component analysis (PCA) has been applied to compress the training data. Our approach models the dynamical sub-systems and sequence data in HVAC system. The learnt models can then be used for automatic fault detection and diagnosis. The approach has been tested on commercial HVAC systems. It had successfully detected and identified a number of typical AHU faults.

The technological advancement in Cyber-Physical Systems (CPS) has evolved into sophisticated hardware, leading to systems that are complex and interconnected. This trend has made modern CPS susceptible to faults. Traditional methods for fault detection and diagnosis are unable to adequately scale up to manage the faults that occur in CPS because of the tight interconnectivity between the physical and cyber parts of CPS. Also, hard real-time constraints present challenges that are not sufficiently addressed by traditional fault-tolerant design approaches; therefore, new methods are needed to deal with these faults. Here, we present a new scheme to detect and diagnose CPS faults, which relies on the combination of knowledge-based and model-driven Fault Detection and Diagnosis (FDD). The method is developed to be applied when building CPS using Discrete Event Methodologies.

This paper investigates the problem of fault detection and diagnosis (FDD) problem for non-gausian singulat stochastic distribution control (SDC) systems via the output probability density functions(PDFs). The PDFs can be approximated by using square-root B-spline expansion,via this expansions to represent the dynamics weighting systems between the system input and the weights related to the output PDFs. In this works ,an optimal fault detection and diagnosis algorith is present by introducing the parameter-updating .when the fault occurs, an adaptive network parameter-updating law is designed to approximated the fault. Finally, the simulation result are given to show that the approach can detect faults and estimate the size of fault.