Fault Diagnosis Decision Research Articles

Evaluation, Analysis and Diagnosis for HVDC Transmission System Faults via Knowledge Graph under New Energy Systems Construction: A Critical Review

High voltage direct current (HVDC) transmission systems play a critical role to optimize resource allocation and stabilize power grid operation in the current power grid thanks to their asynchronous networking and large transmission capacity. To ensure the operation reliability of the power grid and reduce the outage time, it is imperative to realize fault diagnosis of HVDC transmission systems in a short time. Based on the prior research on fault diagnosis methods of HVDC systems, this work comprehensively summarizes and analyzes the existing fault diagnosis methods from three different angles: fault type, fault influence, and fault diagnosis. Meanwhile, with the construction of the digital power grid system, the type, quantity, and complexity of power equipment have considerably increased, thus, traditional fault diagnosis methods can basically no longer meet the development needs of the new power system. Artificial intelligence (AI) techniques can effectively simplify solutions’ complexity and enhance self-learning ability, which are ideal tools to solve this problem. Therefore, this work develops a knowledge graph technology-based fault diagnosis framework for HVDC transmission systems to remedy the aforementioned drawbacks, in which the detailed principle and mechanism are introduced, as well as its technical framework for intelligent fault diagnosis decision.

Vibration Fault Diagnosis in Wind Turbines Based on Automated Feature Learning

A growing number of wind turbines are equipped with vibration measurement systems to enable the close monitoring and early detection of developing fault conditions. The vibration measurements are analyzed to continuously assess the component health and prevent failures that can result in downtimes. This study focuses on gearbox monitoring but is also applicable to other subsystems. The current state-of-the-art gearbox fault diagnosis algorithms rely on statistical or machine learning methods based on fault signatures that have been defined by human analysts. This has multiple disadvantages. Defining the fault signatures by human analysts is a time-intensive process that requires highly detailed knowledge of gearbox composition. This effort needs to be repeated for every new turbine, so it does not scale well with the increasing number of monitored turbines, especially in fast-growing portfolios. Moreover, fault signatures defined by human analysts can result in biased and imprecise decision boundaries that lead to imprecise and uncertain fault diagnosis decisions. We present a novel accurate fault diagnosis method for vibration-monitored wind turbine components that overcomes these disadvantages. Our approach combines autonomous data-driven learning of fault signatures and health state classification based on convolutional neural networks and isolation forests. We demonstrate its performance with vibration measurements from two wind turbine gearboxes. Unlike the state-of-the-art methods, our approach does not require gearbox-type-specific diagnosis expertise and is not restricted to predefined frequencies or spectral ranges but can monitor the full spectrum at once.

Marine Systems and Equipment Prognostics and Health Management: A Systematic Review from Health Condition Monitoring to Maintenance Strategy

Prognostics and health management (PHM) is an essential means to optimize resource allocation and improve the intelligent operation and maintenance (O&M) efficiency of marine systems and equipment (MSAE). PHM generally consists of four technical processes, namely health condition motoring (HCM), fault diagnosis (FD), health prognosis (HP), and maintenance decision (MD). In recent years, a large amount of research has been implemented in each process. However, there is not any systematic review that covers the technical framework comprehensively. This article presents a review of the framework of PHM in the marine field to fill the gap. First, the essential HCM methods, which are widely observed in the academic literature, are introduced systematically. Then, the commonly used FD approaches and their applications in MSAE are summarized, and the implementation process of intelligent methods is systematically introduced. After that, the technologies of HP have been reviewed, including the construction of health indicator (HI), health stage (HS) division, and popular remaining useful life (RUL) prediction approaches. Afterwards, the evolution of maintenance strategy in the maritime field is reviewed. Finally, the challenges of implementing PHM for intelligent ships are put forward.

Optimal fault diagnosis strategy for complex systems considering common cause failure under epistemic uncertainty

Purpose This paper aims to deal with the problems such as epistemic uncertainty, common cause failure (CCF) and dynamic fault behaviours that arise in complex systems and develop an effective fault diagnosis method to rapidly locate the fault when these systems fail. Design/methodology/approach First, a dynamic fault tree model is established to capture the dynamic failure behaviours and linguistic term sets are used to obtain the failure rate of components in complex systems to deal with the epistemic uncertainty. Second, a β factor model is used to construct a dynamic evidence network model to handle CCF and some parameters obtained by reliability analysis are used to build the fault diagnosis decision table. Finally, an improved Vlsekriterijumska Optimizacija I Kompromisno Resenje algorithm is developed to obtain the optimal diagnosis sequence, which can locate the fault quickly, reduce the maintenance cost and improve the diagnosis efficiency. Findings In this paper, a new optimal fault diagnosis strategy of complex systems considering CCF under epistemic uncertainty is presented based on reliability analysis. Dynamic evidence network is easy to carry out the quantitative analysis of dynamic fault tree. The proposed diagnosis algorithm can determine the optimal fault diagnosis sequence of complex systems and prove that CCF should not be ignored in fault diagnosis. Originality/value The proposed method combines the reliability theory with multiple attribute decision-making methods to improve the diagnosis efficiency.

The Architecture Design and Implementation of Aircraft Structural Fault Assistant Decision System Based on Data Analysis

That the aircraft structural damage diagnosis and prediction is very important for flight safety. In this paper, the aircraft structural damage can be predicted based on the maintenance data analysis and the structure fault diagnosis and assistant decision are realized. Thus, the maintenance management of aircraft structure damage can be developed. According to the actual service data, both the structural damage prediction and decision assistant support system are designed, which apply the micro-service architecture and Vue.Js front-end development technology. Thus, the effective container server and excellent usability are achieved.

Distributed Fast Fault Diagnosis for Multimachine Power Systems via Deterministic Learning

In this paper, a distributed fast fault diagnosis approach is proposed for multimachine power systems based on deterministic learning (DL) theory. First, a learning estimator is constructed to accumulate the knowledge of transient fault dynamics in power systems. Through DL, a partial persistent excitation condition of the local neurons is satisfied and convergence of the neural weights is achieved. Thus, a knowledge bank can be established and gradually updated. To deal with the high dimensional data, only the neurons centered in a neighborhood of the system trajectory are activated. In this manner, the computation load is mitigated. Second, by using the learnt knowledge, a distributed fault diagnosis scheme is designed to monitor the power system. The memory of the transient fault dynamics can be quickly recalled and a fast fault diagnosis decision is made. Finally, based on the concepts of mismatch interval and duty ratio, the diagnosis capabilities of the proposed scheme are investigated. The effectiveness of the proposed diagnosis scheme is demonstrated by computer simulation and the hardware-in-loop experimental test based on the RT-LAB realtime simulator.

Planetary gearbox fault diagnosis based on data-driven valued characteristic multigranulation model with incomplete diagnostic information

There are many uncertain factors that may result in incomplete diagnostic information of planetary gearboxes, such as sensor malfunctions, communication lags, and data discretization, etc. Therefore, incomplete diagnostic information of planetary gearboxes may simultaneously contain two categories of unknown attribute values. However, existing fault diagnosis methods of planetary gearboxes are hard to realize fault diagnosis using incomplete diagnostic information that simultaneously contains two categories of unknown attribute values. To overcome this issue, a fault diagnosis method of planetary gearboxes based on data-driven valued characteristic multigranulation model with incomplete diagnostic information is proposed. First, a calculation method of characteristic similarity degrees among cases is introduced, and a data-driven valued characteristic relation is defined. The data-driven valued characteristic relation is used to analyze and process incomplete diagnostic information that simultaneously contains two categories of unknown attribute values. Then, a data-driven valued characteristic multigranulation model is defined according to multigranulation model. An attribute reduction algorithm based on pessimistic data-driven valued characteristic multigranulation model is employed to extract fault diagnosis decision rules. Finally, naive Bayesian classifier is constructed to identify planetary gearbox conditions. The effectiveness of this method is validated and the advantages are investigated using a fault diagnosis experiment of planetary gearbox. Experimental results demonstrate that this method can accurately determine indiscernibility relation among cases, reduce computational complexity, and enhance fault diagnosis accuracy.

Adaptive Ensemble Fault Diagnosis based on Online Learning of Personalized Decision Parameters

Data-driven fault diagnosis is increasingly prevalent in recent years for complex systems, but still suffers from two practical difficulties: one is the lack of enough historical examples of fault equipment and the other is the absence of personalized characteristics of the equipment to be diagnosed. This paper proposes an adaptive ensemble fault diagnosis method to resolve these two difficulties. The proposed method first extracts fault and normal patterns by constructing hidden Markov models (HMMs), respectively, from historical fault and normal examples. Then fault diagnosis decisions made by the supervised and unsupervised methods are intrinsically integrated through the hidden states identified by HMMs to overcome the difficulty of lacking enough historical fault examples. Finally, a learning process is designed and embedded in the proposed method to describe the impacts of personalized characteristics of the equipment on its fault diagnosis. Theoretical and experimental results both verify the effectiveness of the proposed method.

Data‐driven fault diagnosis and robust control: Application to PEM fuel cell systems

SummaryA data‐driven methodology that includes the unfalsified control concept in the framework of fault diagnosis and isolation (FDI) and fault‐tolerant control (FTC) is presented. The selection of the appropriate controller from a bank of controllers in a switching supervisory control setting is performed by using an adequate FDI outcome. By combining simultaneous online performance assessment of multiple controllers with the fault diagnosis decision from structured hypothesis tests, a diagnosis statement regarding what controller is most suitable to deal with the current (nominal or faulty) mode of the plant is obtained. Switching strategies that use the diagnosis statement are also proposed. This approach is applied to a nonlinear experimentally validated model of the breathing system of a polymer electrolyte membrane fuel cell. The results show the effectiveness of this FDI–fault‐tolerant control data‐driven methodology. Copyright © 2017 John Wiley & Sons, Ltd.

Predictive Flight Control with Active Diagnosis and Reconfiguration for Actuator Jamming

Actuator jamming in flight control applications may be attributed to a temporary stall load due to large aerodynamic forces, or even a permanently stuck faulty control surface. These two root causes of actuator jamming have different consequences on available control authority and fault duration. As an important consequence, any reconfiguration strategy that may be applied to handle these types of jamming faults needs to distinguish between them in order to take appropriate measures. However, the similarity of fault phenomena between the two root causes makes this problem challenging (i.e., not possible using only passive fault diagnosis). We propose to integrate reconfigurable model predictive control (MPC) with active fault diagnosis (FD) to address this problem. After detecting actuator jamming, a sequence of reconfiguration strategies (relying on actuator constraint updates and a soft constraint formulation) is adopted to aid in the FD decision process and take appropriate measures after obtaining the FD results. The proposed reconfiguration strategies allow the FD module to distinguish the two root causes of the jamming and also detect the end of stall load. The MPC then implements a suitable controller reconfiguration depending on the FD results. A nonlinear Airbus civil aircraft simulator is used to illustrate the effectiveness of our proposed approach.

A Compact Error Management Algorithm to Minimize False-Alarm Rate of Motor/Generator Faults in (Hybrid) Electric Vehicles

This paper addresses an emerging reliability and safety requirement for more electric transportation systems by proposing an on-board diagnostics plug-in tool. For this purpose, a compact diagnostic algorithm is presented using an advanced speed feedback error management technique for traction motor and generator faults which is one of the most critical parts of the vehicle powertrain. The speed measurement accuracy can be degraded for various reasons such as: sensor issues, vehicle vibration, hardware tolerances, and environmental impacts like temperature, moisture, and electromagnetic interferences. Although the speed feedback errors within an acceptable level can be tolerated by most of the drive systems, the accuracy of speed measurement is critical for the reliability of fault diagnosis. The major reason behind this fact is the frequency of fault signature in the spectrum is primarily determined using the motor speed. It is shown how the reliability of a motor fault diagnosis decision can significantly be improved under erroneous speed feedback conditions by using the developed effective error localization and coarse-to-fine detection technique. The proposed scheme is implemented through effective strategies to minimize computational complexity and memory occupancy in every step of signal processing according to manufacturers' strict cost limitation.

Application of Uncertain Information Fusion in Diagnosis Decision of Electronic Equipment

Focuses on how to dispose the multi-source uncertain information and promote the testability evaluation and fault diagnosis capability of the electronic equipment, this paper uses fuzzy theory in the uncertain information description and modeling. Based on the fuzzy set description of fuzzy target, new method is proposed to obtain fuzzy evidences from fuzzy fault features, and then, Dempster-Shafer combination rule are used to fuse multi-source fuzzy evidence to get diagnosis results. The proposed method of fuzzy evidence extraction can reduces uncertainties in fusion makings and improves fault identifications, and the fusion diagnosis method based on multi fuzzy evidence matching enhances the precision and reliability of the system fault diagnosis decision furthermore.

A novel probabilistic neural network‐based algorithm for classifying internal fault in transformer windings

AbstractThe major function of protective devices in a power system is to detect the occurrence of faults and to isolate the faulty sections from the rest of the system. Much progress has been made in the development algorithms for detecting faults in power transformers, which depend on transients‐based techniques. This paper presents an algorithm based on a combination of discrete wavelet transforms and probabilistic neural networks (PNNs) for classifying internal faults in a two‐winding three‐phase transformer. Fault conditions of the transformer are simulated using alternative transients program/electromagnetic transients program (ATP/EMTP) in order to obtain current signals. The mother wavelet Daubechies4 is employed to decompose the high‐frequency components from these signals. All three phases of the differential current signals are used in the fault detection decision algorithm. The variations of first‐scale high‐frequency component that detects fault are used as an input for the training pattern. The training process for the neural network and fault diagnosis decision is implemented using toolboxes on MATLAB/Simulink. Various cases and fault types based on the Thailand electricity transmission and distribution systems are studied to verify the validity of the algorithm. Backpropagation neural network is also compared with the PNN in this paper. It is found that the proposed method gives satisfactory accuracy with less training time, and will be particularly useful in the development of a modern differential relay for a transformer protection scheme. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Researching on Distribution Network Fault Location System

In this study, we propose the method using rough set theory to realize the distribution network fault positioning in WEBGIS environment. According to the distribution network tree structure, using of the area user s’ fault complain information as condition attributes, fault elements as decision attribute, form the decision table automatically. By using rough set method reduce the decision table, the minimal reduction of decision table for fault diagnosis is derived, the minimal diagnostic rules are obtained, guarantee the objectivity of the rule. When the fault complain call information is imperfect, it can still achieve rapid, accurate fault location on purpose, has good fault tolerance performance. In this study the use of C# language programming to achieve the reduction process for distribution network fault diagnosis decision table, combined with the WEBGIS platform, make full use of internal network database resources, to realize fault diagnosis simple and rapid operation and fault location visualization. The results show that the method is feasible and effective.

Granularity Entropy-Based SDG Fault Reasoning Method

The fault diagnosis method based on qualitative SDG deep knowledge model has better completeness, but lower resolution. Because it ignores the fact that the effect is different between different nodes in a SDG graph. To solve this problem, a method which added the quantitative information of the effect between nodes into the qualitative SDG reasoning by granularity entropy was proposed, according to which the priority could be given to the multi-potential fault origin and the resolution could be improved. In this method, the value of the effect can be obtained by calculation of attribute state values in fault diagnosis decision table. The shortcoming of the previous fuzzy SDG fault diagnosis method can be avoided.

An approach for failure recognition in IP‐based industrial control networks and systems

SUMMARYIndustrial control networks (ICNs) and systems support robust communications of devices in process control or manufacturing environments. ICN proprietary protocols are being migrated to Ethernet/IP networks in order to merge various different types of networks into a single common network. ICNs are deployed in mission‐critical operations, which require a maximum level of network stability. Network stability is often described using several categories of network performance quality‐of‐service metrics, such as throughput, delay, and loss measurements. The question arises as to whether these network performance metrics are sufficient to run valuable diagnostics of ICN components and their communications. Any abnormal decision with respect to typical IP traffic behavior does not necessarily coincide with ICN fault cases. A precise and specific diagnostic technique for ICNs is required to remove the uncertainty in detecting problems. However, existing Ethernet/IP diagnosis tools have not been able to fully handle fault symptoms and mainly focus on network diagnostics rather than process or device diagnostics. This paper demonstrates that the absence of advanced fault diagnosis techniques leads to the development of new methodologies that are suitazble for ICN. We describe unique traffic characteristics and categorize the faults of ICN. We also propose a fault diagnosis, prediction, and adaptive decision methodologies, and verify them with real‐world ICN data from the steel‐making company POSCO. Our experience in developing the fault diagnosis system provides a firm guideline to understand the fault management mechanisms in large ICNs. Copyright © 2012 John Wiley & Sons, Ltd.

Application of MMAS and Rough Sets in Compound-Fault Diagnosis of Bearing

The diagnosis of compound-fault is always a difficult point, and there is not an effective method in equipment diagnosis field. Rough set theory is a relatively new soft computing tool to deal with vagueness and uncertainty. Condition attribute reduce algorithm is the key point of rough set research. However, it has been proved that finding the best reduction is the NP-hard problem. For the purpose of getting the reduction of systems effectively, an improved algorithm is put forward. The worst Fisher criterion was adopted as heuristic information to improve the searching efficiency and Max-Min Ant System was selected. Simplify the fault diagnosis decision table, then clear and concise decision rules can be obtained by rough sets theory. This method raises the accuracy and efficiency of fault diagnosis of bearing greatly.

Component reliability in fault-diagnosis decision making based on dynamic Bayesian networks

The decision making in fault diagnosis methods generally relies on the analysis of fault signature vectors. The current paper presents a new approach of decision making for the signature vectors for various identical or similar faults. The main contribution of the paper consists in the fusion between the reliability and the evaluation of the residuals in order to increase the fault isolation efficiency. The decision making, formalized as a Bayesian network, is established with a priori knowledge on fault signatures, false alarm and missing detection probability, online component state estimation computed by a Bayesian fusion of the component reliability, and measurements. The effectiveness and performances of the method are illustrated on a heating water process corrupted by various faults.

A Fault Diagnosis Method for Transformer Integrating Rough Set with Fuzzy Rules

Although the International Electrotechnical Commission (IEC) codes method has been widely used to interpret dissolved gas-in-oil analysis (DGA) results in the world, it sometimes fails to diagnose, especially when more than one fault exists in a transformer. This paper presents a rough set-based fuzzy logic technique, which can diagnose multiple faults in a transformer. By applying rough set theory to the IEC codes, the reduct of the decision table can be obtained. By using a fuzzy method, the fuzzy membership functions of each fault diagnosis decision rules are displayed, and finally the fault type of the transformer is diagnosed. The application of this method to some transformers has yielded promising results.

Pruning and Extraction of Qualitative Fault Diagnosis Rules from a Neural Network

Artificial neural networks have been shown to be an effective tool for process fault diagnosis. However, a main criticism is that details of how fault diagnosis decisions are made are embedded in the complex input-output mapping performed by a network, and are in general difficult to obtain. In this paper, statistical techniques and relationships between fuzzy systems and standard radial basis function networks, are exploited to prune a trained network and to extract qualitative rules that explain the network operation for fault diagnosis. The procedures are demonstrated in an application to the diagnosis of a range of both soft and hard faults in a simulated, complex, chemical process.