Distributed Fault Diagnosis Research Articles

Energy efficient secured cluster based distributed fault diagnosis protocol for IoT

The rapid growth of internet and internet services provision offers wide scope to the industries to couple the various network models to design a flexible and simplified communication infrastructure. A significant attention paid towards Internet of things (IoT), from both academics and industries. Connecting and organizing of communication over wireless IoT network models are vulnerable to various security threats, due to the lack of inappropriate security deployment models. In addition to this, these models have not only security issues; they also have many performance issues. This research work deals with an IoT security over WSN model to overcome the security and performance issues by designing a Energy efficient secured cluster based distributed fault diagnosis protocol (EESCFD) Model which combines the self-fault diagnosis routing model using cluster based approach and block cipher to organize a secured data communication and to identify security fault and communication faults to improve communication efficiency. In addition we achieve an energy efficiency by employing concise block cipher which identifies the ideal size of block, size of key, number of rounds to perform the key operations in the cipher.

A Distributed Fault Diagnosis and Cooperative Fault-Tolerant Control Design Framework for Distributed Interconnected Systems

This paper investigates a design framework for a class of distributed interconnected systems, where a fault diagnosis scheme and a cooperative fault-tolerant control scheme are included. First of all, fault detection observers are designed for the interconnected subsystems, and the detection results will be spread to all subsystems in the form of a broadcast. Then, to locate the faulty subsystem accurately, fault isolation observers are further designed for the alarming subsystems in turn with the aid of an adaptive fault estimation technique. Based on this, the fault estimation information is used to compensate for the residuals, and then isolation decision logic is conducted. Moreover, the cooperative fault-tolerant control unit, where state feedback and cooperative compensation are both utilized, is introduced to ensure the stability of the whole system. Finally, the simulation of intelligent unmanned vehicle platooning is adopted to demonstrate the applicability and effectiveness of the proposed design framework.

Data-knowledge-driven distributed monitoring for large-scale processes based on digraph

Large-scale processes play a pivotal role in modern industry. For process safety concerns, this work proposes a novel digraph-based data-knowledge-driven method (DG-DKD) for large-scale process monitoring. Different from traditional purely data-driven methods, DG-DKD combines process data and knowledge to improve the capability of fault detection and diagnosis. Firstly, the large-scale process is converted into a digraph based on process knowledge and then decomposed into multiple physical meaningful subblocks through a digraph partition method, which eliminates the spurious correlation and improves interpretability. On this basis, the spatial information between subblocks is characterized by a one-hop digraph diffusion method and the temporal information within subblock is captured by canonical variate analysis. With concurrent analysis of temporal and spatial information, the accurate detection of process faults can be guaranteed. Subsequently, a two-stage distributed fault diagnosis method is developed where process data and digraph are leveraged for contribution analysis and causality analysis respectively, which can reduce the smearing effect and identify the fault root cause and propagation path. Finally, the effectiveness of the proposed method is illustrated through the Tennessee Eastman benchmark process and a reactor separator process.

Research on traveling wave fault technology based on ground potential

This paper proposes the dual-terminal positioning method of using ground potential traveling waves to locate the fault location of transmission lines. The traveling wave monitoring terminal is installed at the ground potential end. Installation and maintenance do not need to cooperate with the power outage plan, which can effectively solve the problem of inconvenient maintenance toward the distributed fault diagnosis system. Overhead transmission lines with 110 kV and higher voltage generally have ground lines. When a lightning strike or ground fault happens, not only will a fault traveling wave be generated on the line but also a fault traveling wave will be generated at the ground potential end and propagate along the tower. As the traveling wave current propagating along the ground potential can be effectively detected, the double-ended positioning of the ground potential can be realized. The application in the actual grid shows that a distributed monitoring system on the line tower and the ground line can quickly and accurately determine the fault location.

Fault-tolerant state estimation for stochastic systems over sensor networks with intermittent sensor faults

In this paper, the problem of distributed fault-tolerant state estimation is studied for stochastic systems over sensor networks with intermittent sensor faults. Compared with the traditional state estimation algorithms, the distinct advantage of fault-tolerant state estimation is that the estimator can keep good performance whether sensor faults occur or not. Different from the previous literature concerning with distributed fault diagnosis, the distributed fault diagnosis problem is investigated in this paper for intermittent faults, whose appearing time, disappearing time and magnitude are all nondeterministic. The distributed fault-tolerant state estimation scheme is constructed, in which the appearing time and disappearing time of intermittent faults are detected, intermittent faults are estimated and compensated. By means of the matrix inequality technique, the H∞ performance of state estimation errors is guaranteed by properly choosing the estimator parameters. Finally, two examples are provided to demonstrate the effectiveness of the proposed algorithm.

AI-empowered, blockchain and SDN integrated security architecture for IoT network of cyber physical systems

Internet of things (IoT) is one of the most emerging technologies nowadays and it is one of the key enablers of industrial cyber physical system (CPSs). It has started to participate in almost every aspect of our social life, ranging from financial transactions to the healthcare system, communication to national security, battlefield to smart homes, and so on. However, the wide deployment of IoT suffers certain issues as well, such as interoperability, compatibility, heterogeneity, large amount of data, processing of heterogeneous data etc. Among others, energy efficiency and security are the utmost prominent issues. Scarce computing resources of IoT devices put hindrances on information sharing across edge or IoT network. Indeed, unintentional or malicious interference with IoT data may lead to severe concerns. In this study, the researcher exploits the potential benefits of a blockchain system and integrates it with software-defined networking (SDN) while justifying energy and security issues. More in detail, the researcher proposed a new routing protocol with the cluster structure for IoT networks using blockchain-based architecture for SDN controller. The proposed architecture obviates proof-of-work (PoW) with private and public blockchains for Peer-to-Peer (P2P) communication between SDN controllers and IoT devices. In addition to this, distributed trust-based authentication mechanism makes blockchain even more adoptive for IoT devices with limited resources. The experimental results show that the proposed cluster structure based routing protocol outperforms the state-of-the-art Ad-hoc On-demand Distance Vector (AODV), Destination-Sequenced Distance Vector (DSDV), Secure Mobile Sensor Network (SMSN), Energy efficient secured cluster based distributed fault diagnosis (EESCFD), and Ad-hoc On-demand Multipath Distance Vector (AOMDV), in terms of energy consumption, network throughput, and packet latency. Proposed protocol help overcome the issues especially, energy management and security of the next generation industrial cyber physical systems.

Distributed Fault Diagnosis Framework for Nuclear Power Plants

A fault diagnosis can quickly and accurately diagnose the cause of a fault. Focusing on the characteristics of nuclear power plants (NPPs), this study proposes a distributed fault diagnosis method based on a back propagation (BP) neural network and decision tree reasoning. First, the fault diagnosis was carried out using the BP neural network and decision tree reasoning, and then a global fusion diagnosis was performed by fusing the resulting information. Second, the key technologies of the BP neural network and decision tree sample construction were studied. Finally, the simulation results show that the proposed distributed fault diagnosis system is highly reliable and has strong diagnostic ability, enabling efficient and accurate diagnoses to be realized. The distributed fault diagnosis system for NPPs provides a solid foundation for future research.

Collaborative deep learning framework for fault diagnosis in distributed complex systems

In distributed complex systems, condition monitoring and fault diagnosis have received considerable attention, especially for recent developments of data-driven methods with deep learning structures, which greatly enhance the performance as of superior representation capacity over big data. To apply these methods, massive data needs to be collected from distributed systems, requiring high costs for data transmission and causing more and more concerns on privacy issues. For the naturally distributed data in such scenario, this work presents a novel collaborative deep learning framework with the idea that the features, as representations of data, can be transmitted through latent parameters of deep learning structure while the raw data won’t be shared in the distributed network. Based on the collaborative learning setup, the proposed framework adopts a secure communicating strategy with no need of transmitting raw data, and obtains a consensus for distributed deep learning models that can be geographically located. To validate the proposed scheme, four case studies are carried out and the results show that it is able to improve the diagnosis accuracy compared with local learning models. Also, it is robust and adaptive for diagnosis problems with data that is imbalanced or from different distributions.

Distributed Fault Diagnosis System based on Wireless Sensor Networks

This article presents the development of a distributed fault diagnosis and monitoring system whose remote nodes are responsible for data collection and distributed analysis to identify problems that could lead to critical faults in industrial processes or systems. The developed intelligent remote node was implemented with MCU LPCXpresso54114 connected to a ZigBee protocol wireless sensor network through XBee communication module. The gateway node is a Raspberrry PI with HTTP communication and JSON format to the PI System industrial monitoring system database. Motor Current Signature Analysis (MCSA) was implemented and validated to identify interturn faults of induction motors. The developed platform is a tool to perform comparison and validation of analysis techniques, indicators, and fault classification, because there are different combinations that can be applied to improve diagnosis reliability, fault observability, differentiation between fault conditions, classification accuracy, tolerance to transients, sensitivity, among others.

Dissipativity-based distributed fault diagnosis for plantwide chemical processes

Most modern chemical processes consist of a number of process units interconnected with mass and energy flows, often with energy integration and materials recycle loops. As such, faults (process faults, actuator faults, or sensor faults) often propagate to multiple process units (subsystems), causing significant difficulties in fault diagnosis for plantwide systems. In this paper, a general distributed fault diagnosis approach is proposed for plantwide chemical processes, which takes into account the interactions among process units. The distributed fault diagnostic observers are designed to be sensitive to the local faults (local sensitivity) and insensitive to faults in other process units (remote faults insensitivity) and disturbances. The above requirements are formulated as plantwide dissipativity conditions and the gains for the distributed estimators and residual generators are obtained offline by solving a set of linear matrix inequalities. A case study of heat exchanger network is presented to demonstrate the effectiveness of the proposed approach.

Distributed fault diagnosis of rotor-bearing system using dimensional analysis and experimental methods

Distributed defects in rolling element bearing arise due to the progression of small localized pits. If not monitored on time, this leads to premature or may turn into catastrophic failure of a rotor system and thus increases the cost of maintenance of machinery in industries. This research work introduced a novel method for the diagnosis of rolling element bearings to detect distributed defects using Dimensional Analysis (DA). The rolling element bearing system is modeled using DA with Buckingham’s theorem to study the vibration characteristics theoretically. Significant geometrical and response parameters are considered for modeling the rotor-bearing system. The in-house experimental test rig is designed and fabricated to study the vibration characteristics of distributed defects experimentally. Geometrical imperfections are created intentionally on the bearings surfaces using electro-discharge machining and vibration signatures generated are studied under different operating conditions. Experimental results are compared with analytical models developed using the DA. A comparison is made between distributed and localized defects and vibrations signatures from both are correlated with healthy bearings. It is noticed that there is a good correlation between theoretical and experimental results. The present study contributes as a simple and effective diagnosis of rolling element bearings for distributed defects and useful for condition monitoring of industrial machinery.

Data-based Distributed Fault Diagnosis for Adaptive Structures using Convolutional Neural Networks

Adaptive structures are able to react to environmental impacts and have become a promising approach in civil engineering to improve the load-bearing behavior of buildings. Since reliability and safety of building structures are major concerns, the detection and isolation of faults are essential. In this work, the data-based distributed fault diagnosis of sensor and actuator faults in an adaptive high-rise truss structure is investigated and compared to a centralized approach. The decomposition of the different subsystems is given by the hardware layout of the different sensor systems and actuators. The mechanical structure is modeled and extended by dynamic sensor and actuator models containing different faults. Based on the simulation model, different fault scenarios are generated and used for training a convolutional neural network with dropout regularization. It is shown that the distributed approach needs less training data and yields better classification results than the centralized approach due to a significant reduction of the complexity and dimensionality.

Modular Distributed Fault Diagnosis for Adaptive Structures using Local Models

Adaptivity has become a promising concept in civil engineering to improve the load-bearing behavior of buildings and to reduce material consumption. However, the integrated actuators and sensors increase the complexity and adversely affect the reliability making a fault diagnosis for actuator and sensor faults necessary. In this paper, the distributed fault diagnosis of an adaptive high-rise truss structure, which is characterized by a modular design, is investigated. Based on local models and the local measurement information of hydraulic actuators and strain gages, a distributed fault diagnosis scheme is proposed for the diagnosis of actuator and sensor faults. Since the local models do not have information about the interconnection to other modules, the model-based residual is uncertain and faults in the other module can affect the local residual. For this reason, an effective online estimation of the probability density function and the Kullback-Leibler divergence of the residual is presented for change detection considering the stochastic uncertainties. Moreover, the selected sensor layout of the adaptive structure allows the isolation of the investigated actuator faults such that fault propagation paths of the distributed system are analyzed and sensor faults are isolated by communicating the detected changes in the modules. The effectiveness of the approach is illustrated in a simulation study.

Distributed Fault Diagnosis for a Class of Time-Varying Systems over Sensor Networks with Stochastic Protocol

This paper is concerned with the distributed fault diagnosis problem for a class of time-varying systems over sensor networks with nonlinearity and uncertainty. For the purpose of solving the problem of data conflict, the stochastic protocol is used to determine which node has the right to send data to the estimator at a certain transmission time. The aim of this paper is to design a set of distributed estimators to detect, isolate and estimate fault signals. The upper bound of estimation error covariance is obtained by solving two recursive matrix equations and the upper bound can be minimized by designing appropriate estimator gain at each step. Finally, a numerical example is provided to show the effectiveness of the proposed design scheme.

Distributed Fault Diagnosis of Nonlinear Stochastic Systems With Monitoring Networks

The design of a distributed fault detection and isolation filter is addressed for stochastic nonlinear systems subjected to multiple failure modes. The monolithic process is monitored by a network of communicating detection nodes, in lieu of a singleton, supervising diagnostic unit. Each detection node is a fault-sensitive estimator unit with processing capabilities and limited observation access to the process. The detection nodes exchange post-processed information over a communication network. An average consensus protocol is employed to fuse the detection nodes' outputs to obtain an agreement over the local likelihood functions. The proposed approach conducts online hypothesis testing without a bank of estimators, reducing the computational complexity of the algorithm significantly. Numerical simulations validate the efficiency of the proposed algorithm.

A Bayesian Argumentation Framework for Distributed Fault Diagnosis in Telecommunication Networks.

Traditionally, fault diagnosis in telecommunication network management is carried out by humans who use software support systems. The phenomenal growth in telecommunication networks has nonetheless triggered the interest in more autonomous approaches, capable of coping with emergent challenges such as the need to diagnose faults’ root causes under uncertainty in geographically-distributed environments, with restrictions on data privacy. In this paper, we present a framework for distributed fault diagnosis under uncertainty based on an argumentative framework for multi-agent systems. In our approach, agents collaborate to reach conclusions by arguing in unpredictable scenarios. The observations collected from the network are used to infer possible fault root causes using Bayesian networks as causal models for the diagnosis process. Hypotheses about those fault root causes are discussed by agents in an argumentative dialogue to achieve a reliable conclusion. During that dialogue, agents handle the uncertainty of the diagnosis process, taking care of keeping data privacy among them. The proposed approach is compared against existing alternatives using benchmark multi-domain datasets. Moreover, we include data collected from a previous fault diagnosis system running in a telecommunication network for one and a half years. Results show that the proposed approach is suitable for the motivational scenario.

SOA-based distributed fault prognostic and diagnosis framework: An application for preheater cement cyclones

Complex engineering manufacturing systems require efficient online fault diagnosis methodologies to improve safety and reduce maintenance costs. Traditionally, diagnosis and prognosis approaches are centralised, but these solutions are difficult to implement on distributed systems; whereas a distributed approach of multiple diagnosis and prognosis agents can offer a solution. Also, controlling process plant from a remote location has several benefits including the ability to track and to assist in solving a problem that might arise. This paper presents a distributed and over prognosis and diagnosis approach for physical systems basing on multi agent system and service-oriented architecture. Specifics prognostic and diagnostic procedures and key modules of the architecture for web service-based distributed fault prognostic and diagnosis framework are detailed and developed for the preheater cement cyclones in the workshop of SCIMAT clinker. The experimental case study, reported in the present paper, shows encouraging results and fosters industrial technology transfer.

Distributed fault diagnosis of networked dynamical systems with time-varying topology

In this paper, the distributed fault diagnosis (DFD) of networked dynamical systems with time-varying connected topologies, e.g., wireless sensor networks in harsh environments, is considered. Specifically, two essential problems are focused on, which are faced in extending the the decomposition-based adaptive DFD approach to such topology-varying systems. The problems introduced by the time-varying topologies are, respectively, decomposition schemes deterioration and pre-training difficulties. The causes of the two problems are detailed and addressed in our work. First, for the decomposition schemes deterioration problem, a multi-agent dynamics-based online distributed decomposition algorithm are developed, so that a decent decomposed network structure for such topology-varying network can be maintained. Second, to alleviate the pre-training difficulties in topology-varying systems, a fault detection method is proposed, which avoids the need for pre-training. The distributed decomposition algorithm is proved to converge in finite steps, and the proposed fault detection method is verified both theoretically and experimentally.

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.

Distributed fault tolerant tracking control for large‐scale multi‐motor web‐winding systems

This study is concerned with the distributed fault tolerant tracking control for large-scale multi-motor web-winding systems. Firstly, the web-winding system is considered a synthetic system with several dynamic subsystems subject to multiple disturbances and actuator faults. Then, a disturbance compensation-based distributed fault diagnosis scheme is developed to estimate the system actuator faults. By setting the reference outputs, a method of obtaining equilibrium control inputs and equilibrium states is formulated to achieve the distributed variation dynamic model at equilibrium points by Taylor expansion. Based on the estimated actuator faults, an effective distributed fault tolerant control strategy is proposed by the techniques of interval matrix and disturbance local compensation. Sufficient conditions of asymptotic stability of the estimation error systems and the closed-loop systems are derived based on the Lyapunov theory. A fault diagnosis observer and a fault tolerant controller gain matrices are obtained by solving the linear matrix inequalities. Finally, simulations and analysis are performed on the three-motor web-winding system to verify the effectiveness of the proposed distributed fault tolerant control strategy.