Distributed Fault Diagnosis Research Articles

Sentinella: Smart Monitoring of Photovoltaic Systems at Panel Level

The monitoring of photovoltaic (PV) systems is important for the optimization of their efficiency. In this paper, a low-cost smart multisensor architecture equipped with voltage, current, irradiance, temperature, and inertial sensors, for the monitoring (at the panel level) of a PV system, is presented with the aim of detecting the causes of efficiency losses. The system is based on a Wireless Sensor Networks with sensing nodes installed on each PV panel. The acquired data are then transferred to a service center where dedicated paradigms continuously perform the assessment of electrical efficiency as well the estimation of correlated causes, at the single panel level. In this paper, the detection of critical faults (temporary and permanent shadowing, dirtying, and anomalous aging) is addressed. The methodology adopted to estimate efficiency losses and related causes is based on the comparison between the measured efficiency of each PV panel and the nominal one estimated in the real operating conditions. Moreover, the anomalous aging estimation is based on the five parameter model approach that exploits a dedicated minimization paradigm to analyze the mismatch between the nominal current–voltage model of the PV panel and the measured one. The main advantage of the proposed approach is the continuous monitoring of PV plants and the assessment of possible causes of power inefficiency at the PV panel level, allowing for the implementation of a really efficient distributed fault diagnosis system. The experimental results are presented along with the analysis of the uncertainty affecting the measurement system.

Distributed fault diagnosis for process and sensor faults in a class of interconnected input–output nonlinear discrete-time systems

This paper presents a distributed fault diagnosis scheme able to deal with process and sensor faults in an integrated way for a class of interconnected input–output nonlinear uncertain discrete-time systems. A robust distributed fault detection scheme is designed, where each interconnected subsystem is monitored by its respective fault detection agent, and according to the decisions of these agents, further information regarding the type of the fault can be deduced. As it is shown, a process fault occurring in one subsystem can only be detected by its corresponding detection agent whereas a sensor fault in a subsystem can be detected by either its corresponding detection agent or the detection agent of another subsystem that is affected by the subsystem where the sensor fault occurred. This discriminating factor is exploited for the derivation of a high-level isolation scheme. Moreover, process and sensor fault detectability conditions characterising quantitatively the class of detectable faults are derived. Finally, a simulation example is used to illustrate the effectiveness of the proposed distributed fault detection scheme.

Secure Vehicle Localization and Cruise Control for Connected Vehicles

A distributed fault diagnosis, isolation and estimation scheme for the positioning system of a group of connected vehicles is presented in this paper. The decentralized mode of operation enables each vehicle to perform its own fault diagnostics harnessing the information exchange in the connected vehicle framework. The approach used in this paper takes advantage of the vehicle-to-vehicle (V2V) connectivity in vehicle state estimation and utilizes a Dedicated Observer Design scheme for the fault detection and isolation process. A fault estimation approach is also adopted in this paper which is fed back to the measurement/control system for safe and reliable operation of the vehicle in presence of fault. The fault diagnostics approach focuses on the vehicle localization and proximity measurement sensors which are utilized by many autonomous functionalities of modern vehicles such as Adaptive Cruise Control (ACC). The simulation results presented in this paper indicates the ability of the diagnostics scheme in the detection and isolation of multiple faults in the system. The performance of the fault estimation methodology is also presented in this paper.

Diagnosis of Wireless Sensor Networks in Presence of Permanent and Intermittent Faults

In this paper we consider the problem of distributed fault diagnosis in Wireless Sensor Networks (WSNs). The proposed Fault Diagnosis Algorithm (FDA) aims to handle both permanent and intermittent faults. The sensor nodes with permanent communication faults can be diagnosed by using the conventional time-out mechanism. In contrast, it is difficult to detect intermittent faults due to their inherent unpredictable behavior. The FDA is based on the comparison of sensor measurements and residual energy values of neighboring sensor nodes, exploiting their spatial correlations. To handle intermittent faults, the comparisons are made for $$r$$ r rounds. Two special cases of intermittent faults are considered: one, when an intermittently faulty node sends similar sensor measurement and similar residual energy value to some of its neighbors in all $$r$$ r rounds; another, when it sends these values, either or both of which deviates significantly from that of some neighbors in all $$r$$ r rounds. Through extensive simulation and analysis, the proposed scheme is proved to be correct, complete, and efficient to handle intermittent faults and hence, well suited for WSNs.

Automatic Partitioning of DES Models for Distributed Fault Diagnosis Purposes

The automatic partitioning of automata models for large concurrent Discrete Event Systems (DES) is considered. Distributed model architectures are widely used for automatabased fault diagnosis to ensure model completeness and to avoid the state space explosion problem. Partitioning determines the size and structure of partial models to represent concurrent operating parts of the DES. In this paper, new automatic and data-based partitioning methods are proposed using observations of the fault-free system behavior. The application on a real-world benchmark system shows the capabilities of the developed methods.

Belief consensus–based distributed particle filters for fault diagnosis of non-linear distributed systems

This article presents a study on distributed fault diagnosis of spatially distributed systems composed of physically interconnected subsystems. To handle the complexity of such complicated systems, the overall state space model is decomposed to its constituent subsystems. Moreover, hybrid modelling is considered for the fault diagnosis problem. In this case, each subsystem is modelled as a hybrid one. Two different modes (healthy and faulty) are considered as discrete modes of the hybrid sub systems. A particle filtering–based algorithm is then proposed for the purpose of fault diagnosis of generally non-linear and non-Gaussian systems. The proposed method is applicable to a large class of distributed systems that undergo autonomous transitions from healthy to faulty modes. To implement the algorithm in a decentralized architecture, we employ embedded systems, called agents. Agents cooperate in the process of fault diagnosis by fusing local information to make the result of the decentralized approach asymptotically equivalent to the corresponding centralized one. To this end, belief (conditional probability) consensus algorithm is exploited. Simulation results are applied to a strongly interconnected four-tank system (as a benchmark) to demonstrate the efficiency of the proposed method and how it improves the previous ones.

Research and design of distributed fault diagnosis system in nuclear power plant

Nuclear power plant (NPP) is a complex system with abundant operation data and various fault types. Moreover, in most cases, change of system parameters and prompt of the alarm system can not necessarily tell us directly what types the fault belongs to or where it lies in. When multiple faults occur, there is no one-to-one relationship between the fault symptom and the fault itself. Furthermore, the degree and response rate of the fault are different, so some faults are gradual and some are sudden. It is thus clear that for different faults, we need to conduct combined diagnosis with a variety of diagnostic methods to ensure accuracy and instantaneity in NPP fault diagnosis. According to the characteristic of the distributed function of equipment and the digital instrument and control (I&C) system, we studied and designed the distributed condition monitoring and fault diagnosis system in NPP. Based on the “disassemble-synthesizing” diagnostic idea, this paper proposed an intelligent diagnosis method which applied the fuzzy neural network (FNN) in doing local diagnosis and multi-source information fusion technology in global diagnosis. The simulation result showed that this method can quickly and accurately complete the tasks of diagnosing different levels of the single fault and different types of multiple faults.

Distributed fault diagnosis for continuous-time nonlinear systems: The input–output case

In this paper, some new results on distributed fault diagnosis of continuous-time nonlinear systems with partial state measurements are proposed. By exploiting an overlapping decomposition framework, the dynamics of a nonlinear uncertain large-scale dynamical system is described as the interconnections of several subsystems. Each subsystem is monitored by a Local Fault Diagnoser: a set of local estimators, based on the nominal local dynamic model and on an adaptive approximation of the interconnection and of the fault function, allows to derive a local fault decision. A consensus-based protocol is used in order to improve the detectability and the isolability of faults affecting variables shared among different subsystems because of the overlapping decomposition. A sufficient condition ensuring the convergence of the estimation errors is derived. Finally, possibly non-conservative time-varying threshold functions guaranteeing no false-positive alarms and theoretical results dealing with detectability and isolability sufficient conditions are presented.

Distributed Fault Diagnosis for Continuous time nonlinear systems: The input-output case

Distributed Fault Diagnosis for Continuous time nonlinear systems: The input-output case

Optimal configuration of fault-tolerance parameters for distributed replicated server access

Server replication is a common fault-tolerance strategy to improve transaction dependability for services in communications networks. In distributed architectures, fault-diagnosis and recovery are implemented via the interaction of the server replicas with the clients and other entities such as enhanced name servers. Such architectures provide an increased number of redundancy configuration choices. The influence of a (wide area) network connection can be quite significant and induce trade-offs between dependability and user-perceived performance. This paper develops a quantitative stochastic model using stochastic activity networks (SAN) for the evaluation of performance and dependability metrics of a generic transaction-based service implemented on a distributed replication architecture. The composite SAN model can be easily adapted to a wide range of client-server applications deployed in replicated server architectures. In order to obtain insight into the system behaviour, a set of relevant environment parameters and controllable fault-tolerance parameters are chosen and the dependability/performance trade-off is evaluated.

Online Distributed Fault Diagnosis in Wireless Sensor Networks

This paper presents an online fault diagnosis algorithm for wireless sensor networks. This work explicitly takes into account the possibility of faults in different sections of sensor networks and communication channel. The diagnostic local view is obtained by exploiting the spatially correlated sensor measurements. These local views are then disseminated using a spanning tree of cluster heads. Our algorithm is shown to be energy efficient as it works in conjunction with the normal network activities and requires minimum additional diagnostic messages to be exchanged. Simulation results show that the performance of our algorithm is less sensitive to the average node degree for a wide range of fault rates.

Distributed fault diagnosis in a class of interconnected nonlinear uncertain systems

In this article, a distributed fault detection and isolation (FDI) method is developed for a class of interconnected nonlinear uncertain systems. In the distributed FDI architecture, a FDI component is designed for each subsystem in the interconnected system. For each subsystem, its corresponding local FDI component is designed by utilising local measurements and certain communicated information from neighbouring FDI components associated with subsystems that are directly interconnected to the particular subsystem under consideration. Under certain assumptions, adaptive thresholds for distributed FDI in each subsystem are derived, ensuring robustness with respect to interactions among subsystems and system modelling uncertainty. Moreover, the fault detectability and isolability conditions are rigorously investigated, characterising the class of faults in each subsystem that are detectable and isolable by the proposed distributed FDI method. Additionally, the stability and learning capability of the local adaptive fault isolation estimators designed for each subsystem is established. A simulation example of interconnected inverted pendulums mounted on carts is used to illustrate the effectiveness of the method.

Design and Research of Condition Monitoring and Fault Diagnosis System of Geotechnical Centrifuge

With some increase in capacity, load and runtime, running safety of geotechnical centrifuge becomes more and more grim. General layout and structure of geotechnical centrifuge are introduced. Main fault and fault components of geotechnical centrifuge are analyzed. A new distributed equipment condition monitoring and fault diagnosis network system based on RS485 field bus and industrial Ethernet is designed. Key technologies of this system are researched. Real-time signals of vibration, temperature and strain can be synchronously collected and processed. The application of this system can provide a reliable basis for running safety and maintenance timely of centrifuge.

An Integrated Framework for Model-Based Distributed Diagnosis and Prognosis


 
 
 Diagnosis and prognosis are necessary tasks for system re-configuration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation and identification of faults, while prognosis consists of prediction of the remaining useful life of systems. This paper presents a novel integrated framework for model-based distributed diagnosis and prognosis, where system decomposition is used to enable the diagnosis and prognosis tasks to be performed in a distributed way. We show how different submodels can be automatically constructed to solve the local diagnosis and prognosis problems. We illustrate our approach using a simulated four- wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs distributed fault diagnosis and prognosis in an efficient and robust manner.
 
 

Distributed Fault Diagnosis for Input-Output Continuous-Time Nonlinear Systems

In this paper, new results on distributed fault diagnosis of continuous–time nonlinear systems with partial state measurements are proposed. Following an overlapping decomposition framework, the dynamics of a nonlinear uncertain large-scale dynamical systems is described as the interconnection of several subsystems. Each subsystem is monitored by its own Local Fault Diagnoser, based on a set of local estimators. A consensus-based protocol is used to improve the detectability and the isolability of faults affecting variables shared among different subsystems because of the overlapping decomposition. A sufficient condition assuring the convergence of the estimation errors is derived. Time-varying threshold functions guaranteeing no false-positive alarms and theoretical results containing detectability and isolability conditions are presented.

Construction of Bayesian Network Structure with Importance Indicator of Arc in Distributed Fault Diagnosis

Construction of Bayesian Network Structure with Importance Indicator of Arc in Distributed Fault Diagnosis

Sensor Data Collection and the Distributed Fault Diagnosis Algorithm Applicable to a Huge-scale Sensor Network

This paper proposes a distributed and self-organized method which has the mechanisms of robustness sensor data collection and fault diagnosis for large-scale sensor networks. To cope with complex sensor networks which include (i) an ad-hoc multi-hop communication, (ii) data aggregation, and (iii) high error data rate caused by the a huge number of nodes, the proposed method collects the information of nodes and diagnoses its nodes by introducing the new nodes called token nodes and the new type of broadcast called a limited virtual broadcast. The token node can create packets which can only be transmitted in the limited multi-hop networks as a limited virtual broadcast, which can prevent a broadcast storm. To clarify the effectiveness of our method, we conduct the simulations on two-hops network and compare the results of our method with those of the conventional method called Adaptive DSD. The intensive simulation results have revealed that our proposed method works well in the large-scale sensor network and shows better performance than Adaptive DSD method from the viewpoint of the communication traffic and rate of information sharing.

Study on Remote Distributed Fault Diagnosis System in Modern Agricultural Machinery

A remote distribution based agricultural mechanical fault diagnosis system was proposed against the characteristics of high technology and difficult fault diagnosis in the modern agriculture and the time-sensitive requirements in agricultural production. The paper introduced the overall system architecture and working principle, analyzed the development of site monitoring and diagnostic system, remote diagnosis center, network communication systems, and software systems development, and investigated the system’s main implementation techniques, which include network database technology, data compression and transmission technology and system security technology.

Multi-Agent Based Design for Distributed Fault Diagnosis Systems

A Multi-Agent based distributed fault diagnosis reference model is important for high speed and automation. Fault diagnosis and maintenance are vital aspects in industrial process, in this sense, fault diagnosis systems should support decision-making tools, new diagnosis approaches and techniques, updating of the devices. In this paper a Multi-Agent based fault diagnosis reference model is presented which combines the existing models and Multi-Agent. This model is based on a generic framework using Multi-Agent System; in this sense, the fault diagnosis problem is viewed like a feedback control process and the actions are related to the decision-making in the scheduling of the preventive maintenance task and the running of preventive and corrective specific maintenance tasks. As a result, a particular prototype design based on the JADE platform is obtained. This new model is compared to some important existing models and applied to a real investigation. This Multi-Agent based model provides a comprehensive treatment of the fault management problem, it can be used in any level of industrial environments and it can be integrated with the supervision applications without modifying the current automation architecture.

Web Service and Multi-Agent Based Fault Diagnosis System for MSW Incinerators

Based on Web Service and Multi-agents technology, this paper proposes to build a remote distributed fault diagnosis system for equipments of an incineration plant which requires real-time diagnosis in a widely distributed environment. The system is modeled and implemented by multi-agent technology and is composed of a large number of agents, which are deployed at different web sites. To extend system capabilities, a user can provide additional diagnosis procedures, diagnosis services or data services. Based on diagnosis goals, relevant agents are assembled together to form a remote collaborative diagnosis system. The system can be used in any incineration plants, which greatly simplifies the deployment and maintenance efforts.