Multi-agent based coordinated protection systems for distribution feeder fault diagnosis and reconfiguration

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.

Adaptive Convergent Visibility Graph Network: An interpretable method for intelligent rolling bearing diagnosis

Nowadays, chemical processes are becoming more complex, and so the safety requirement is getting higher and higher. Intelligent and effective fault detection and diagnosis are becoming more and more important. However, complex industrial systems generate high-dimensional data, which has bad influence on fault detection and diagnosis. Thus, it is necessary to handle industrial data with high dimensionality. Discriminant Locality Preserving Projections (DLPP) has attracted much interest as a dimensionality reduction method. However, there is a small size sample problem when the data dimension is higher than the number of data classes. Under this condition, DLPP cannot achieve acceptable performance in reducing data dimension. In order to solve this problem, this paper proposes a novel DLPP based on improved Synthetic Minority Oversampling Technique (SMOTE-DLPP). The improved SMOTE is used to sample the original data set to generate new data sets so that the number of data classes is basically the same as the number of data dimension. Simulation results on the Tennessee Eastman process (TEP) show that the proposed SMOTE-DLPP can achieve acceptable performance in fault diagnosis.

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 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.

To solve the problem of fault prediction and diagnosis of household air conditioning, an improved Bayesian network (BN) fault diagnosis model is proposed. Firstly, the orthogonal defect classification (ODC) is used to make statistics and analysis of air conditioning fault data, and the structure of BN fault diagnosis model is built based on the analysis results. Then, genetic algorithm (GA) is used to optimize the conditional probability of network nodes and determine the network parameters. Finally, the cooling and heating failure data of household air conditioning were taken as an example to diagnose. Compared with the traditional BN model, the accuracy of fault diagnosis increases from 81.13% to 92.83%, which verifies the effectiveness of the model.

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.

In order to realize a stable and effective gearbox fault diagnosis system, an uncertain information fusion method based on BP neural network and modified DS evidence theory is proposed. The common gearbox fault diagnosis system consists of two dominating parts: signal feature extraction and fusion decision. Actually, the main task of most papers is to extract the characteristics of gearbox fault with various algorithms, whereas the fusion decision part is still absent. To manage the integration of gearbox fault diagnosis in the fusion decision part, this paper puts forward a novel fusion method by the introduction of two-layer fusion. In this paper, BP neural network is used to establish the local-layer fusion, while an improved DS evidence theory is taken to build decision-layer fusion. It can be seen from the definition and derivation of the two-layer fusion, the original method settles the uncertainty of multi-sensor information fusion in gearbox fault diagnosis system. According to the experiment results and analyses, the innovative algorithm improves the accuracy of fault identification and diagnosis, in addition, reduces the uncertainty of fault diagnosis system.

비점오염원은 세정에 의해 하수관 내로 유입되어 수계까지 흘러 수계의 오염부하를 가중시킬 뿐만 아니라, 관의 상태에 따라 누수 및 월류를 야기하여 오염을 일으킨다. 이에 따라 하수관거의 유지관리 및 환경오염 방지를 위한 관거내 하수의 유량, 수질, 불명수 및 월류 등을 효율적으로 관리하기 위한 모니터링의 필요성이 증가하고 있다. 그러나 하수관거의 경우, 지하에 설계되며 그 구조 및 연결이 복잡한 특성으로 인해 실제 하수관거에 대한 모니터링은 쉽지 않다. 본 연구에서는 시스템해석이론에 기초한 하수관망 오염제거 해석과 관망 모니터링 및 이상진단방법을 제시하였다. 먼저 하수관망 공정모사 프로그램인 Stormwater & Wastewater Management Model for expert (XP-SWMM)을 이용하여 관망 내의 오염물질의 거동패턴을 해석하였다. 둘째, 다변량 통계 모니터링을 이용하여 하수관망 내의 수질 모니터링 및 하수관망 유출을 탐지하는 관망 이상 진단을 수행하였다. 정적/동적 상태 시스템에 기초한 하수관망 오염 매카니즘 해석결과, 강우시 총질소와 총인 부하량이 비강우시보다 급격하게 증가함을 확인하였으며, 이는 수계의 오염부하를 가중시킬 것으로 판단되다. 이에 따라 하수관망 내 유출은 강우로 생긴 유량 및 유입오염물질의 농도 증가로 인한 관망이상으로 사료된다. 제안된 하수관망 모니터링 및 이상진단 기법은 도시 유역에서의 비점오염원 관리와 지속적인 모니터링에 있어 효과적으로 사용될 수 있을 것으로 예상된다. Nonpoint source pollution causes leaks and overtopping, depending on the state of the sewer network as well as aggravates the pollution load of the aqueous water system as it is introduced into the sewer by wash-off. According, the need for efficient sewer monitoring system which can manage the sewage flowrate, water quality, inflow/infiltration and overflow has increased for sewer maintenance and the prevention of environmental pollution. However, the sewer monitoring is not easy since the sewer network is built in underground with the complex nature of its structure and connections. Sewer decontamination mechanism as well as pipe network monitoring and fault diagnosis of water network system on system analysis proposed in this study. First, the pollution removal pattern and behavior of contaminants in the sewer pipe network is analyzed by using sewer process simulation program, stormwater & wastewater management model for expert (XP-SWMM). Second, the sewer network fault diagnosis was performed using the multivariate statistical monitoring to monitor water quality in the sewer and detect the sewer leakage and burst. Sewer decontamination mechanism analysis with static and dynamic state system results showed that loads of total nitrogen (TN) and total phosphorous (TP) during rainfall are greatly increased than non-rainfall, which will aggravate the pollution load of the water system. Accordingly, the sewer outflow in pipe network is analyzed due to the increased flow and inflow of pollutant concentration caused by rainfall. The proposed sewer network monitoring and fault diagnosis technique can be used effectively for the nonpoint source pollution management of the urban watershed as well as continuous monitoring system.

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.

Long term exposure of photovoltaic (PV) systems under relatively harsh and changing environmental conditions can result in fault conditions developing during the operational lifetime. The present solution is for system operators to manually perform condition monitoring of the PV system. However, it is time-consuming, inaccurate and dangerous. Thus, automatic fault detection and diagnosis is a critical task to ensure the reliability and safety in PV systems. The current state-of-the-art techniques either cannot provide enough detailed fault information with high accuracy or have too much complexity. This work presents an automatic fault detection and diagnosis method for string based PV systems. It combines an artificial neural network (ANN) with the conventional analytical method to conduct the fault detection and diagnosis tasks. A two-layered ANN is applied to predict the expected power which is then compared with the measured power from the real PV system. Based on the difference between the ANN estimated power and the measured power, the open circuit voltage and short circuit current of the PV string determined using analytical equations are used to identify any of the six defined fault types. The proposed method has a fast detection, compact structure and good accuracy. Simulation results show the effective fault detection and diagnosis capability of the proposed method.

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.

In this paper, a fuzzy expert off-line system has been developed for fault diagnosis in the distribution network based on the structural and functional operation of the relay and circuit breakers. Functional operations (correct operation, false operation and failure to operate) of the relays and circuit breakers are described by fuzzy logic. Input data for the proposed fuzzy expert fault diagnosis system (FDS) are status and time stamps of the alarms, associated with relays and circuit breakers. The diagnostic system from a huge number of alarms sets, logically organizes and quantifies the diagnosis. FDS can diagnose correct operation, false operation and failure to operate of the relays and circuit breakers. Also, it can identify and quantify fault location based on the Hamacher’s operator of a fuzzy union. The additional contribution of this paper is in modeling unknown information using linear fuzzy membership function. Statuses of certain components may be unknown due to telemetry failures or are simply unavailable to the operator and proposed FDS can make diagnosis in such a situation. Developed fuzzy expert FDS is tested on the two examples of faults in real life distribution network

A novel protection scheme for low voltage DC microgrid using inductance estimation