Fuzzy Petri net-based fault diagnosis for vacuum system in space environment simulator

The electrical system fault testing and diagnosis system based on I2C-bus and embedded equipment is proposed. This system is accomplished with the integration of PXI-based computer, field fault testing platform, dedicated interface adapter unit and accessories with software program of virtual instrument and database management and fault guidance system. It can obtain various state parameters of the heavy mechanized bridge by interface adapter unit and corresponding sensors. The dedicated interface adapter unit, that is the core of the hardware platform is composed of chassis, embedded microcomputer controller, I2C-bus data acquisition modules, VPC connection package and military aviation sockets. Because of its flexibility and modular design, it can support different data conversion, for example, analog, digital, and serial, while functioning as an intelligent conversion bridge between field fault testing platform or PXI computer and electrical system of heavy mechanized bridge electrical system. The fault diagnosis software is of hierarchical modular structure with integration of system management control, fault detection and circuit hardware driver modules together with fault repair and diagnosis database. Therefore it provides functions of human-machine interaction, equipment fault detection, fault diagnosis and analysis, as well as repair and maintenance guidance The fault testing and diagnosis system in this work is also featured with handy structure, lightweight, low consumption, large storage capacity, excellent expandability and intelligent inference capability. The system can be used in the online fault testing and diagnosis of similar engineering equipments. So it has important engineering and military application value to the rapid fault testing and fault diagnosis of engineering equipments.

In order to realize the condition monitoring of the motor ?anytime, anywhere?, improve the detection accuracy and shorten the detection time, the author proposes a fault signal processing and diagnosis system for the motor heat recovery system based on the IoT. That is, based on the IoT technology, a mobile terminal oriented motor remote monitoring and fault diagnosis system, the sensing layer of the system collects real-time motor operation status data, and the transmission layer realizes data transmission, cloud storage and response to data requests from the application layer, finally, at the mobile end, the motor running status and diagnosis results are displayed through charts and text, so as to realize remote monitoring and fault diagnosis of the motor. The experimental results show that the accuracy of fault diagnosis test of GA-SVM in mobile terminal is more than 90%, and the running time is less than 30 ms, and the running time is very short. It proves that the mobile terminal uses the fault detection method based on GA-SVM model with high accuracy and short detection time, that is, the fault signal processing and diagnosis accuracy of the motor heat recovery system of the IoT is high and the detection time is short.

The expert system for fault diagnosis based on classified hierarchical model is addressed so as to improve the maintenance capability of engineering vehicles under complex battle environment. The analysis of fault feature, fault pattern and failure mechanism of the whole machine and its electrical system is accomplished. Furthermore, the structure of classified hierarchical diagnosis model and method of knowledge representation are implemented so that the knowledge base of the fault diagnosis system can be developed. The forward reasoning mechanism of the fault diagnosis expert system is bring forward based on neural network intelligent reasoning that can achieve fuzzy input and multiple fault diagnosis. This work effectively increases the fault maintenance, diagnosis and support capabilities of the military troops.

This paper deals with information model for power grid fault diagnosis based on Common Information Model (CIM). Fault diagnosis gains information from topology information and device parameters of primary system grid based on IEC61970 standard and device information of secondary system grid based on IEC 61850. Device information contains information of protection movement wave record and protection configuration. However, the existent CIM can't fully represent power system fault diagnosis information. Thus, it is need to extend CIM. All kinds of information are based on different model, which causes different information standard. In this paper a preliminary study on CIM extensions which is based on power system fault diagnosis, has been proposed to perfect fault information model. On the basis of different standard model, this paper has discussed the theory of coordinating IEC 61850 and IEC61970 standard, and proposed using Web Ontology Language (OWL) based on Extensible Mark-up Language (XML) to the solution of this problem. A fault Information Model is then provided to the fault diagnosis system according to Component Interface Specification (CIS) in IEC61970. After receiving fault information, the fault diagnosis system starts for real-time, on-line fault diagnosis. Fault information model has solved the problem of the deficiency and non-standard of fault diagnosis information, attaining fault diagnosis information integrity and standardization, and making the fault diagnosis result fast and accurate.

This paper has analyzed merits and demerits of both neural network technique and of the information fusion methods based on the D-S (dempster-shafer evidence) Theory as well as their complementarity, proposed the hierarchical information fusion fault diagnosis strategy by combining the neural network technique and the fused decision diagnosis based on D-S Theory, and established a corresponding functional model. Thus, we can not only solve a series of problems caused by rapid growth in size and complexity of neural network structure with diagnosis parameters increasing, but also can provide effective method for basic probability assignment in D-S Theory. The application of the strategy to diagnosing faults of motor bearings has proved that this method is of fairly high accuracy and reliability in fault diagnosis.

This paper deals with fault diagnosis and fault prognosis in discrete event systems described by sequences of events. We are interested in large industrial systems where the modelling with tools usually used for discrete event system (automata, Petri net…) is a complex task. The description of DES is made with a statistical model composed of events recorded from the considered DES and regrouped in the form of temporal windows. In the first phase, the theoretical framework is developed to perform diagnosis and prognosis using statistical model and temporal window concept. The second phase uses the result of the first phase to train radial basic function neural networks that preform online diagnosis and online prognosis. A case study and several examples serving to illuminate the developed approach are given.

Achieving asymptotic and concurrent fault diagnosis in hydraulic system remains a challenging endeavor due to the inherent attributes of the hidden occurrence, simultaneous manifestation, coupling, and limited sample size. To address the above issues, this paper proposes a hierarchical multi-output fault detection and diagnosis framework, namely, HMDF, based on a hierarchical learning strategy to leverage an improved convolutional neural network (CNN) and support vector machine (SVM). Both a multi-channel CNN and a multi-branch CNN are employed to extract and downscale features collected by the sensors at diverse sampling frequencies first, and then, such features are subsequently subjected to classification using SVM. The hierarchical learning strategy enables the identification of different fault states, both at the component and the intra-component level. Additionally, a modified whale optimization algorithm is also utilized to optimize the classification process of SVM. Extensive experiments are conducted to test the proposed HMDF with the hydraulic system datasets. Results show that HMDF achieves a diagnostic accuracy of up to 98.9% for the dataset, surpassing traditional methods reliant on manual extraction of time–frequency features, and it also exhibits superior classification performances with a small sample size. The HMDF is expected to offer a generalized framework for the multi-output fault detection and diagnosis in hydraulic systems and other complex components.

In order to meet the requirements of multi-parameter and real-time fault diagnosis of China VI vehicle emission standards diesel engine air system, this paper focuses on the research of the diesel engine air system fault detection and diagnosis method based on deep learning to improve the operational safety and fault diagnosis efficiency. In this paper, the air system fault detection and diagnosis are completed based on the AE model and the CNN model, combined with the actual operation of the diesel engine. Among them, the AE model successfully detected all real-time operation faults, and the false detection rate on the health data was 0.1162%; the CNN model obtained a 90.77% fault diagnosis accuracy rate on the test set of the real-time operation data set. The results show that the model has high accuracy in the diagnosis of diesel engine faults, which is of great significance to the application of deep learning based on big data processing.

The concept of Industry 4.0 uses cyber-physical systems and the Internet of Things to create "smart factories" that enable automated and connected production. However, the complex and diverse nature of cyber-physical systems makes them vulnerable to physical threats, where any fault in their physical components can lead to serious consequences, from financial losses to human safety. To reduce the risk of such failures, it is essential to have techniques in place to monitor the condition of these physical components closely. In recent years, data-driven predictive maintenance methods have become common due to the growth in data and advancements in machine learning. This thesis focuses on fault diagnosis, a key part of predictive maintenance, which aims to detect and identify faults in physical components. Incorporating advanced techniques, including machine learning algorithms and sensor data analysis, enhanced fault diagnosis systems' accuracy and efficiency. However, several real-world barriers challenge the effectiveness of these techniques. This research is structured around three primary objectives. The first objective aims to establish an efficient fault diagnosis system that is highly sensitive to fault occurrences while being resilient to uncertainties such as variability in fault patterns and data imbalance. To address variability in fault patterns, we developed a multi-step approach integrating clustering techniques and an adaptive neuro-fuzzy inference system, which significantly improved diagnosis robustness even under noisy conditions. For imbalanced datasets, a two-step method was introduced, combining permutation entropy for fault detection and envelope analysis for fault isolation. This approach was enhanced by an automatic frequency band selection method leveraging wavelet transform and statistical criteria, resulting in high fault diagnosis accuracy and reliability, even with limited labeled faulty data. The second objective focuses on developing fault diagnosis systems that can maintain high performance despite domain shifts and limited training data in both source and target domains. To address the challenge of domain shifts, a transfer learning technique was introduced, and a systematic literature review was conducted. This study bridges a technical gap by providing a comprehensive taxonomy, state-of-the-art methods, and insights into the challenges of applying transfer learning to predictive maintenance. Additionally, a conceptual framework was proposed to explore scenarios for transferring diagnostic knowledge from different source domains to target domains with insufficient training datasets. In a subsequent study, a new diagnostic framework was developed using digital twins and partial transfer learning to address the lack of real training datasets in both source and target domains as well as label space inconsistencies between them. The framework includes a general approach for developing digital twins of real machines to generate synthetic diagnostic data as source domain data. Furthermore, by incorporating a deep learning network and a double-layer attention mechanism, a partial transfer learning diagnostic model was developed to transfer diagnostic knowledge from the digital twin to the real target domain despite label space inconsistencies. The proposed framework was validated through an industrial case study involving rolling bearings in rotating equipment. The third objective focuses on developing an efficient fault diagnosis system capable of maintaining reliability under unseen and evolving working conditions. To achieve this, a novel self-adaptive fault diagnosis system was proposed, incorporating the MAPE-K loop, a domain generalization network model, and digital twin technologies. This self-adaptive fault diagnosis system leverages the MAPE-K loop to adapt in real time to evolving working conditions. Digital twins were employed to continuously generate data aligned with the real system’s operational context, enabling accurate fault diagnosis in dynamic environments. The domain generalization network model enhances generalization to unseen target working conditions by addressing overfitting and feature loss through multi-domain data augmentation, adversarial learning, and domain discrepancy modules. Validation using multiple rotating machinery datasets demonstrated that the developed fault diagnosis system outperformed state-of-the-art methods across various scenarios. In summary, this dissertation proposes a comprehensive and practical approach to addressing variability in fault patterns, imbalance datasets, data scarcity, domain shifts, and unseen and evolving working conditions challenges in existing fault diagnosis systems.

Filter Design for System Modeling, State Estimation and Fault Diagnosis analyzes the latest methods in the design of filters for system modeling, state estimation and fault detection with the intention of providing a new perspective of both theoretical and practical aspects.This book also includes fault diagnosis techniques for unknown but bounded systems, their real applications on modeling and fault diagnosis for lithium battery systems, DC-DC converters and spring damping systems. It proposes new methods based on zonotopic Kalman filtering, a variety of state estimation methods of zonotope and its derived algorithms, a state estimation method based on convex space, set inversion interval observer filtering-based guaranteed fault estimation and a novel interval observer filtering-based fault diagnosis. The methods presented in this text are more practical than the common probabilistic-based algorithms, since these can be applied in unknown but bounded noisy environments. This book will be an essential read for students, scholars and engineering professionals who are interested in filter design, system modeling, state estimation, fault diagnosis and related fields.

The battery system, as the core energy storage device of new energy vehicles, faces increasing safety issues and threats. An accurate and robust fault diagnosis technique is crucial to guarantee the safe, reliable, and robust operation of lithium-ion batteries. However, in battery systems, various faults are difficult to diagnose and isolate due to their similar features and internal coupling relationships. In this paper, the current research of advanced battery system fault diagnosis technology is reviewed. Firstly, the existing types of battery faults are introduced in detail, where cell faults include progressive and sudden faults, and system faults include a sensor, management system, and connection component faults. Then, the fault mechanisms are described, including overcharge, overdischarge, overheat, overcool, large rate charge and discharge, and inconsistency. The existing fault diagnosis methods are divided into four main types. The current research and development of model-based, data-driven, knowledge-based, and statistical analysis-based methods for fault diagnosis are summarized. Finally, the future development trend of battery fault diagnosis technology is prospected. This paper provides a comprehensive insight into the fault and defect diagnosis of lithium-ion batteries for electric vehicles, aiming to promote the further development of new energy vehicles.

Hierarchical Fault Diagnosis System Utilizing the Signed Directed Graph and the Extended Kalman Filter

This work approaches relative aspects to the alarm processing problem and fault diagnosis in system level, having as purpose filter the alarms generated during a outage and identify the equipment under fault. A methodology was developed using Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in order to resolve the problem. This procedure had as initiative explore the GA capacity to deal with combinatory problems, as well as the ANN processing speed and generalization capacity. Such strategy favors a fast and robust solution.

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.

The aim of this paper is to discuss the methodology of Fault Detection and Diagnosis (FDD) in dynamic systems. Fault diagnosis is characterized as a control system that controls the ability to adapt system component faults automatically. Fault detection is an implementation of using the error signals, where when error signal is zero or approximately zero. A coupled water tank system was used as a study case model for implementing and testing the proposed methodology. The developed system should generate a set of signals to notify the process operator about the faults that are occurring, enabling changes in control strategy or control parameters. Due to the damage risks involved with sensors, actuators and structural faults of the real plant, the data set of the faults are computationally generated and the results will be collected from numerical simulations of the process model. A Recurrent Neural Networks (RNN) is employed in this paper for modeling the used system. This paper shows how to determine the structure and how to estimate the results using the gradient-based algorithm which it is allowed to obtain a Neural Network with relatively small modeling uncertainly. It describes how to increase the stability of dynamic systems using FDD based on RNN. Faults in the study case are faults in the sensors, faults in the actuators and the structural faults. The faults which are presented in the controlled system are reduced to 98% from the default value.