Fault Propagation Paths Research Articles

Power System Fault Reasoning and Diagnosis Based on the Improved Temporal Constraint Network

Temporal information is fundamental in model-based fault diagnosis, and the alarm-processing problem is to interpret the alarm sequences to infer the type and time of fault event occurrences. There can be cycles or feedback loops in real power systems, but the fault reasoning methods for such cases are seldom considered in the literature. This paper provides an analytic model based on the improved temporal constraint network. The reasoning method is dependent on the time point and time distance information, with which the fault motivators (or actuators) and fault responders (or victims) can be identified. The system fault event reasoning and diagnosis are formulated as an optimization problem with the fault hypotheses being tested. The calculation process consists of three steps: 1) establishment of the objective function, 2) determination of the fault propagation paths, and 3) determination of the expected states under a given hypothesis. Finally, the proposed method is demonstrated via a power system example, which is verified to be successful in performance assessment and fault diagnosis.

Growth by Optimization of Work (GROW): A new modeling tool that predicts fault growth through work minimization

Growth by Optimization of Work (GROW) is a new modeling tool that automates fracture initiation, propagation, interaction, and linkage. GROW predicts fracture growth by finding the propagation path and fracture geometry that optimizes the global external work of the system. This implementation of work optimization is able to simulate more complex paths of fracture growth than energy release rate methods. In addition, whereas a Coulomb stress analysis determines two conjugate planes of potential failure, GROW identifies a single failure surface for each increment of growth. GROW also eliminates ambiguity in determining whether shear or tensile failure will occur at a fracture tip by assessing both modes of failure by the same propagation criterion. Here we describe the underlying algorithm of the program and present GROW models of two propagating faults separated by a releasing step. The discretization error of these models demonstrates that GROW can predict fault propagation paths within the numerical uncertainty produced by discretization. Model element size moderately influences the propagation paths, however, the final fault geometry remains similar between models with significantly different element sizes. The propagation power of the fault system, calculated from the change in work due to fault propagation, indicates when model faults interact through both soft- and hard-linkage.

A fault diagnosis method based on signed directed graph and matrix for nuclear power plants

In order to solve SDG online fault diagnosis and inference, matrix diagnosis and inference methods are proposed for fault detection and diagnosis (FDD). Firstly, “rules matrix” based on SDG model is used for FDD. Secondly, “status matrix” is proposed to achieve SDG online inference. According to different diagnosis results, “status matrix” is applied for the depth-first search and the breadth-first search respectively to find the propagation paths of each fault. Finally, the SDG model of the secondary-loop system in pressurized water reactor (PWR) is built to verify the effectiveness of the proposed method. The simulation experiment results indicate that the “status matrix” used for online inference can be used to find the fault propagation paths and to explain the causes for fault. Therefore, it can be concluded that the proposed method is one of the fault diagnosis for nuclear power plants (NPPs), which can be used to facilitate the development of fault diagnostic system.

Application of Data-based Process Topology and Feature Extraction for Fault Diagnosis of an Industrial Platinum Group Metals Concentrator Plant

The development and application of a fault diagnosis methodology combining process topology and feature extraction to an industrial platinum group metals (PGM) concentrator plant is presented in this paper. Using the available historical process data, process topology was extracted to generate a connectivity map of the process. This connectivity map was used to separate the process into multiple blocks to be analyzed separately. Feature extraction was applied in these blocks to determine if a known fault could be detected, and which block showed the largest effect of the fault. The connectivity map of this block was then used to determine possible fault propagation paths to identify possible root causes of the fault. The developed methodology provided accurate fault detection and identification results, indicating that incorporation of topology information in fault diagnosis is beneficial for fault diagnosis. A description of the envisioned application of these techniques and recommendations regarding this application is given.

Multiple detection test generation with diversified fault partitioning paths

The dependability of current and future nanoscale technologies highly depends on the ability of the testing process to detect emerging defects that cannot be modeled traditionally. Generating test sets that detect each fault more than one times has been shown to increase the effectiveness of a test set to detect non-modeled faults, either static or dynamic. Traditional n-detect test sets guarantee to detect a modeled fault with minimum n different tests. Recent techniques examine how to quantify and maximize the difference between the various tests for a fault. The proposed methodology introduces a new systematic test generation algorithm for multiple-detect (including n-detect) test sets that increases the diversity of the fault propagation paths excited by the various tests per fault. A novel algorithm tries to identify different propagating paths (if such a path exists) for each one of the multiple (n) detections of the same fault. The proposed method can be applied to any linear, to the circuit size, static or dynamic fault model for multiple fault detections, such as the stuck-at or transition delay fault models, and avoids any path or path segment enumeration. Experimental results show the effectiveness of the approach in increasing the number of fault propagating paths when compared to traditional n-detect test sets.

Data-driven fault detection with process topology for fault identification

In this paper a fault diagnosis framework based on detection with feature extraction methods and identification based on data-driven process topology methods was investigated. A simulation of a simple system consisting of two tanks with heat exchangers was used to generate data for normal operating conditions and a number of faults. Fault detection methods included principal component analysis and kernel principal component analysis feature extraction with Shewhart, cumulative sum and exponentially weighted moving average monitoring charts. Process topology information was extracted with linear cross-correlation, partial cross-correlation and transfer entropy. Connectivity maps were constructed to identify possible fault propagation paths to aid root cause analysis and changes in connectivity structure due to faults were exploited for fault identification. Kernel principal component analysis with a CUSUM chart gave the best detection performance, while connectivity graphs based on partial correlation gave an accurate representation of the system and assisted fault identification.

Progressive multi-block modelling for enhanced fault isolation in batch processes

A multi-block progressive modelling approach is proposed for enhanced fault isolation in batch processes. The unfolding of batch data typically leads to matrices with a large number of columns and this complicates contribution analysis. In order to rapidly focus fault isolation in batch processes, it would be desirable to employ multi-block modelling techniques. Multi-block model such as consensus principal component analysis (CPCA) can produce multiple monitoring charts for sub-blocks and block loadings and block scores can be obtained which can represent unique behaviour of each sub-block. CPCA model uses super score which is the same as score from normal principal component analysis (PCA) model and it does not produce enhanced monitoring performance. Multi-block PCA (MBPCA) model using block score for model calculation can represent sub-blocks’ character but block scores are obtained from super loading so it may not be the best way to describe sub-blocks. A new MBPCA model is proposed for better expression of each sub-block. Through progressive modelling and contribution analysis, variables related to or affected by the fault, as well as the associated time information, are gradually identified. This enables a fault propagation path being established. The proposed method is applied to a benchmark simulated penicillin production process, PenSim.

Probabilistic Safety Assessment of Tehran research reactor based on a synergy between plant topology and hierarchical evolutions

Probabilistic Safety Assessment (PSA) is a powerful means in assessing risk and reliability of nuclear plants to complement the achievement of safe operation. A software has been developed in this study to perform failure and reliability analysis (in a PSA context), which are extremely important elements for improving plant operation and safety. This software introduces many advantages such as causal relationships, integrating the analysis tool with plant topology, using plant topology as the basis for explaining the relationships, dynamic navigation through plant model and highlighting fault propagation paths. In the software, plant topology plays the major role. It defines the relationships among plant components, systems and structures and provides the system configurations and causal relationships needed to perform reliability and risk analyses. These advantages are achieved via using some hierarchical evolutions with integrating plant topology (i.e., causal, Part-whole, and Topological hierarchies) and dynamic piping and instrumentation diagrams (DP&IDs). As a case study to verify the software efficiency, PSA of Tehran Research Reactor (TRR) at full power is performed with both the proposed software and System Analysis Programs for Hands-On Integrated Reliability Evaluation (SAPHIRE) (which is used as benchmark software in U.S. Nuclear Regulatory Commission), and good agreement was found.

Risk based opportunistic maintenance model for complex mechanical systems

The essence of the complex mechanical system can be considered as an open system. There exist coupling relationships between various parts in the system and also between different fault modes, which result in multiple fault propagation paths. Considering the safety, benefits and maintenance loss, parameters such as downtime losses, minimal repair costs, corrective, preventive and opportunistic maintenance costs, should be analyzed comprehensively to investigate the influence of different maintenance strategies.A new risk based opportunistic maintenance (RBOM) model considering failure risk is proposed in the paper. It helps to convert the negative random factors caused by single faults to a favorable opportunity of preventive defense against failure for other slight degraded components in advance, so the overall economic losses could be reduced. The global optimization algorithm is further developed to realize RBOM policy. Case studies are provided to illustrate the proposed approaches, with sensitivity analysis of the position, time, style and criterion of the RBOM strategy. Comparative study with the widely used maintenance policy demonstrates the advantage of the proposed method can significantly reduce the maintenance cost and failure risk, and are expected to bring immediate benefits to the energy industry.

Signed directed graph‐based hierarchical modelling and fault propagation analysis for large‐scale systems

The signed directed graph (SDG) model can be considered as a qualitative model to describe the variables and their cause–effect relations in a continuous process. Such models can allow one to obtain the fault propagation paths using the method of graph search. In this way, the authors can use SDGs to model and analyse the propagation of faults in large-scale industrial systems. However, with increasing system scales, the time requirements of a graph search method would be too onerous. This can be alleviated by transforming a single-layer SDG model into a hierarchical model to improve search efficiency. The hierarchical model would be composed of three layers: the top layer would consist of independent sub-systems; the middle layer would have control systems configuration and the bottom layer would have all the variables. The possible root causes of faults can then be searched in this model, layer by layer according to the initial response of the system. The efficacy of the proposed approach is illustrated by application to a four-tank system and a generator system in a power plant. The methodology presented here can also be used in process hazard analysis.

A novel testability model for health management of heading attitude system

Prognostics and health management (PHM) is very important to guarantee the reliability and safety of aerospace systems, and sensing and test are the precondition of PHM. Integrating design for testability into early design stage of system early design stage is deemed as a fundamental way to improve PHM performance, and testability model is the base of testability analysis and design. This paper discusses a hierarchical model-based approach to testability modeling and analysis for heading attitude system health management. Quantified directed graph, of which the nodes represent components and tests and the directed edges represent fault propagation paths, is used to describe fault-test dependency, and quantitative testability information is assigned to nodes and directed edges. The fault dependencies between nodes can be obtained by functional fault analysis methodology that captures the physical architecture and material flows such as energy, heat, data, and so on. By incorporating physics of failure models into component, the dynamic process of a failing or degrading component can be projected onto system behavior, i.e., system symptoms. Then, the analysis of extended failure modes, mechanisms and effects is utilized to construct fault evolution-test dependency. Using this integrated model, the designers and system analysts can assess the test suite’s fault detectability, fault isolability and fault predictability. And heading attitude system application results show that the proposed model can support testability analysis and design for PHM very well.

Fault-propagate pattern based DFA on PRESENT and PRINTcipher

This article proposes an enhanced differential fault analysis (DFA) method named as fault-propagation pattern-based DFA (FPP-DFA). The main idea of FPP-DFA is using the FPP of the ciphertext difference to predict the fault location and the fault-propagation path. It shows that FPP-DFA is very effective on SPN structure block ciphers using bitwise permutation, which is applied to two block ciphers. The first is PRESENT with the substitution-permutation sequence. With the fault model of injecting one nibble fault into the r-2nd round, on average 8 and 16 faults can reduce the key search space of PRESENT-80/128 to 214.7 and 221.1, respectively. The second is PRINTcipher with the permutation-substitution sequence. For the first time, it shows that although the permutation of PRINTcipher is secret key dependent, FPP-DFA still works well on it. With the fault model of injecting one nibble fault into the r-2nd round, 12 and 24 effective faults can reduce the key search space of PRINTcipher-48/96 to 213.7 and 222.8, respectively.

Simulation of Interactions and Emergent Failure Behavior During Complex System Design

Emergent behavior is a unique aspect of complex systems, where they exhibit behavior that is more complex than the sum of the behavior of their constituent parts. This behavior includes the propagation of faults between parts, and requires information on how the parts are connected. These parts can include software, electronic and mechanical components, hence requiring a capability to track emergent fault propagation paths as they cross the boundaries of technical disciplines. Prior work has introduced the functional failure identification and propagation (FFIP) simulation framework, which reveals the propagation of abnormal flow states and can thus be used to infer emergent system-wide behavior that may compromise the reliability of the system. An advantage of FFIP is that it is used to model early phase designs, before high cost commitments are made and before high fidelity models are available. This has also been a weakness in previous research on FFIP, since results depend on arbitrary choices for the values of model parameters and timing of critical events. Previously, FFIP has used a discrete set of flow state values and a simple behavioral logic; this has had the advantage of limiting the range of possible parameter values, but it has not been possible to model continuous process dynamics. In this paper, the FFIP framework has been extended to support continuous flow levels and linear modeling of component behavior based on first principles. Since this extension further expands the range of model parameter values, methods and tools for studying the impact of parameter value changes are introduced. The result is an evaluation of how the FFIP results are impacted by changes in the model parameters and the timing of critical events. The method is demonstrated on a boiling water reactor model (limited to the coolant recirculation and steam outlets) in order to focus the analysis of emergent fault behavior that could not have been identified with previously published versions of the FFIP framework.

Early integration of safety to the mechatronic system design process by the functional failure identification and propagation framework

The research goal of this paper is to introduce a risk analysis methodology that can be applied at the early concept design phase, whose purpose is to identify fault propagation paths that cross disciplinary boundaries, and determine the combined impact of several faults in software-based automation subsystems, electric subsystems and mechanical subsystems. Specifically, the Functional Failure Identification and Propagation (FFIP) analysis framework is proposed to perform a simulation-based analysis of functional failure propagation. The focus is on risk assessment, the earliest activities of the safety process, in which hazards are identified and safety requirements are derived. It is argued that current risk assessment methods are not sufficient for concurrent integration of the safety process to the design process of a complex mechatronic system. In order to facilitate the integration of risk assessment to such systems at the earliest design stages, the design is expressed with syntax and semantics that is able to describe the propagation of failures throughout the system and especially across the boundaries of the mechatronic domains. A boiling water nuclear reactor (limited to the reactor core and steam outlets) is used as a case study. The results demonstrate the capability to handle several fault propagation paths in one scenario for hazard identification at the early, functional, design stage. Specifically, it is shown that FFIP is able to identify fault propagation paths that cross disciplinary boundaries, and which in turn is able to determine the combined impact of several faults in software-based automation subsystems, electric subsystems and mechanical subsystems. The impact is expressed in degradation or loss of safety related functions.

Progress in Root Cause and Fault Propagation Analysis of Large-Scale Industrial Processes

In large-scale industrial processes, a fault can easily propagate between process units due to the interconnections of material and information flows. Thus the problem of fault detection and isolation for these processes is more concerned about the root cause and fault propagation before applying quantitative methods in local models. Process topology and causality, as the key features of the process description, need to be captured from process knowledge and process data. The modelling methods from these two aspects are overviewed in this paper. From process knowledge, structural equation modelling, various causal graphs, rule-based models, and ontological models are summarized. From process data, cross-correlation analysis, Granger causality and its extensions, frequency domain methods, information-theoretical methods, and Bayesian nets are introduced. Based on these models, inference methods are discussed to find root causes and fault propagation paths under abnormal situations. Some future work is proposed in the end.

Fault Localization in Batch Processes through Progressive Principal Component Analysis Modeling

A technique for fault localization in batch processes using progressive principal component analysis (PCA) modeling is proposed in this paper. A PCA model is developed from normal process operation data and is used for online process monitoring. Once a fault is detected by the PCA model, process variables that are related to the fault are identified using contribution analysis. The time information on when abnormalities occurred in these variables is identified using a time series plot of the squared prediction errors (SPE) on these variables. These variables are then removed and another PCA model is developed using the remaining variables. If the faulty batch cannot be detected by the new PCA model, then the remaining variables are not related to the fault. If the faulty batch can still be detected by the new PCA model, then further variables associated with the fault are identified from SPE contribution analysis. The procedure is repeated until the faulty batch can no longer be detected using the remaining variables. Using the time information on when abnormalities presented in the variables associated with the fault, fault propagation paths can be established and the origin of the fault could be traced. The proposed method is tested on a benchmark simulated fed-batch penicillin production process, PenSim. The results demonstrate that the proposed method is particularly effective in isolating faults that have occurred on measured variables. For more complex faults that have occurred on unmeasured variables, the method can identify variables affected by the fault, and process knowledge is required to determine the fault.

Application of the Enhanced Dynamic Causal Digraph Method on a Three-Layer Board Machine

This brief presents an enhanced dynamic causal digraph (EDCDG) reasoning method for fault diagnosis. In order to improve the fault isolation ability of the dynamic causal digraph method, a new algorithm for separating the positive and negative fault effect contributions is proposed. The proposed method was tested with an application on a three-layer board machine process. The results show that the proposed method, compared to the conventional dynamic causal digraph method, is able to detect the correct nodes, to form a better fault propagation path and to identify the responsible arcs when the system is affected by a process fault.

Network Topology for the Application Research of Electrical Control System Fault Propagation

Fault diagnosis and failure analysis is crucial issue in the process of health management and predicative maintenance for large complex system. Fault propagation and its cause-effect relationship in the system are of priority with regard to the fault diagnosis. The fundamentals for modeling the network topology are presented according to the characteristics of the electrical control system connection relationship and its functionality, a complex network topology model was built subsequently. The cause and effect relationships of the components are mapped to the relationships of the vertices in a complex network. The search method for the fault propagation path from the source vertex to the target vertex is studied using node coordinates connections, and application research of electrical control system fault propagation is shown and discussed in the paper. The proposed method is practical in dealing with the fault analysis and evaluation for large complex systems.

Specifying the Directionality of Fault Propagation Paths Using Transfer Entropy

In continuous chemical processes, vanatlOns of process variables usually travel along propagation paths in the direction of flow. The aim of this study was to fmd a data-driven method for identifying the direction of variation propagation using historical process data. Transfer entropy is a recently proposed method based on the probability density function (PDF) that measures directionality of variation with respect to time. An industrial case study illustrates the method which detects the influence of a temperature controller on downstream temperature measurements. A reversal of directionality was noted during a disturbance and a physical explanation offered.

Efficient FFT network testing and diagnosis schemes

We consider offline testing, design-for-testability, and diagnosis for fast Fourier transform (FFT) networks. A practical FFT chip can contain millions of gates, so effective testing and fault-tolerance techniques usually are required in order to guarantee high-quality products. We propose M-testability conditions for FFT butterfly, omega, and flip networks at the double-multiply-subtract-add (DMSA) module level. A novel design-for-testability technique based on the functional bijectivity property of the specified modules to detect faults other than the cell faults is presented. It guarantees 100% combinational fault coverage with negligible hardware overhead-about 0.17% for an FFT network with 16-bit operand words, independent of the network size. Our design requires fewer test vectors compared with previous ones-a factor of up to 1/(6 /spl times/ 2/sup 5n/), where n is the word length. We also propose C-diagnosability conditions and a C-diagnosable FFT network design. By property exchanging and blocking certain fault propagation paths, a faulty DMSA module can be located using a two-phase deterministic algorithm. The blocking mechanism can be implemented with no additional hardware. Compared with previous schemes, our design reduces the diagnosis complexity from O(N) to O(1). For both testing and diagnosis, the hardware overhead for our approach is only about 0.43% for 16-bit numbers regardless of the FFT network size.