Application of logistic regression for fault analysis in an industrial printing process

A statistical technique for fault analysis in industrial printing is reported. The method specifically deals with binary data, for which the results of the production process fall into two categories, rejected or accepted. The method is referred to as logistic regression, and is capable of predicting future fault occurrences by the analysis of current measurements from machine parts sensors. Individual analysis of each type of fault can determine which parts of the plant have a significant influence on the occurrence of such faults; it is also possible to infer which measurable process parameters have no significant influence on the generation of these faults. Information derived from the analysis can be helpful in the operator's interpretation of the current state of the plant. Appropriate actions may then be taken to prevent potential faults from occurring. The algorithm is being implemented as part of an applied self-learning expert system.

Fault analysis in four-wire distribution networks

The neutral wire in most existing power flow and fault analysis software is usually merged into phase wires using Kron's reduction method. In some applications, such as fault analysis, fault location, power quality studies, safety analysis, loss analysis etc., knowledge of the neutral wire and ground currents and voltages could be of particular interest. A general short-circuit analysis algorithm for three-phase four-wire distribution networks, based on the hybrid compensation method, is presented. In this novel use of the technique, the neutral wire and assumed ground conductor are explicitly represented. A generalised fault analysis method is applied to the distribution network for conditions with and without embedded generation. Results obtained from several case studies on medium- and low-voltage test networks with unbalanced loads, for isolated and multi-grounded neutral scenarios, are presented and discussed. Simulation results show the effects of neutrals and system grounding on the operation of the distribution feeders.

Expert systems for fault analysis

The initial aim of this research was to investigate the application of expert Systems, or Knowledge Base Systems technology to the automated synthesis of Hazard and Operability Studies. Due to the generic nature of Fault Analysis problems and the way in which Knowledge Base Systems work, this goal has evolved into a consideration of automated support for Fault Analysis in general, covering HAZOP, Fault Tree Analysis, FMEA and Fault Diagnosis in the Process Industries. This thesis described a proposed architecture for such an Expert System. The purpose of the System is to produce a descriptive model of faults and fault propagation from a description of the physical structure of the plant. From these descriptive models, the desired Fault Analysis may be produced. The way in which this is done reflects the complexity of the problem which, in principle, encompasses the whole of the discipline of Process Engineering. An attempt is made to incorporate the perceived method that an expert uses to solve the problem; keywords, heuristics and guidelines from techniques such as HAZOP and Fault Tree Synthesis are used. In a truly Expert System, the performance of the system is strongly dependent on the high quality of the knowledge that is incorporated. This expert knowledge takes the form of heuristics or rules of thumb which are used in problem solving. This research has shown that, for the application of fault analysis heuristics, it is necessary to have a representation of the details of fault propagation within a process. This helps to ensure the robustness of the system - a gradual rather than abrupt degradation at the boundaries of the domain knowledge.

Multiterminal DC wind farm collection grid internal fault analysis and protection design

The multiterminal dc wind farm is a promising topology with a voltage-source inverter (VSI) connection at the onshore grid. Voltage-source converters (VSCs) are robust to ac-side fault conditions. However, they are vulnerable to dc faults on the dc side of the converter. This paper analyzes dc faults, their transients, and the resulting protection issues. Overcurrent faults are analyzed in detail and provide an insight into protection system design. The radial wind farm topology with star or string connection is considered. The outcomes may be applicable for VSCs in the multi-VSC dc wind farm collection grid and VSC-based high-voltage direct current (HVDC) offshore transmission systems.

Fault-Diagnostic System using Support Vector Machines for Power Transmission Systems

This paper presents an approach for identifying the faulted line section and fault location on transmission systems using support vector machines (SVMs) for diagnosis/post-fault analysis purpose. Power system disturbances are often caused by faults on transmission lines. When fault occurs on a transmission system, the protective relay detects the fault and initiates the tripping operation, which isolates the affected part from the rest of the power system. Based on the fault section identified, rapid and corrective restoration procedures can thus be taken to minimize the power interruption and limit the impact of outage on the system. The approach is particularly important for post-fault diagnosis of any mal-operation of relays following a disturbance in the neighboring line connected to the same substation. This may help in improving the fault monitoring/diagnosis process, thus assuring secure operation of the power systems. In this paper we compare SVMs with radial basis function neural networks (RBFNN) in data sets corresponding to different faults on a transmission system. Classification and regression accuracy is reported for both strategies. Studies on a practical 24-Bus equivalent EHV transmission system of the Indian Southern region is presented for indicating the improved generalization with the large margin classifiers in enhancing the efficacy of the chosen model.

Intelligent Approach for Fault Diagnosis in Power Transmission Systems Using Support Vector Machines

This paper presents an approach for identifying the faulted line section and fault location on transmission systems using support vector machines (SVMs) for diagnosis/post-fault analysis purpose. Power system disturbances are often caused by faults on transmission lines. When fault occurs on a transmission system, the protective relay detects the fault and initiates the tripping operation, which isolates the affected part from the rest of the power system. Based on the fault section identified, rapid and corrective restoration procedures can thus be taken to minimize the power interruption and limit the impact of outage on the system. The approach is particularly important for post-fault diagnosis of any mal-operation of relays following a disturbance in the neighboring line connected to the same substation. This may help in improving the fault monitoring/diagnosis process, thus assuring secure operation of the power systems. In this paper we compare SVMs with radial basis function neural networks (RBFNN) in data sets corresponding to different faults on a transmission system. Classification and regression accuracy is reported for both strategies. Studies on a practical 24-Bus equivalent EHV transmission system of the Indian Southern region is presented for indicating the improved generalization with the large margin classifiers in enhancing the efficacy of the chosen model.

Protection Assessment using Reduced Power System Fault Data

Wavelet transforms provide basis functions for time-frequency analysis and have properties that are particularly useful for the compression of analogue point on wave transient and disturbance power system signals. This paper evaluates the compression properties of the discrete wavelet transform using actual power system data. The results presented in the paper indicate that reduction ratios up to 10:1 with acceptable distortion are achievable. The paper discusses the application of the reduction method for expedient fault analysis and protection assessment.

Flexible fault ride through strategy for wind farm clusters in power systems with high wind power penetration

This paper investigates a flexible fault ride through strategy for power systems in China with high wind power penetration. The strategy comprises of adaptive fault ride through requirements and maximum power restrictions of the wind farms with weak fault ride through capabilities. The slight faults and moderate faults with high probability are the main defending objective of the strategy. The adaptive fault ride through requirement in the strategy consists of two sub fault ride through requirements, a temporary slight voltage ride through requirement corresponding to a slight fault incident, with a moderate voltage ride through requirement corresponding to a moderate fault. The temporary overloading capability of the wind farm is reflected in both requirements to enhance the capability to defend slight faults and to avoid tripping when the crowbar is disconnected after moderate faults are cleared. For those wind farms that cannot meet the adaptive fault ride through requirement, restrictions are put on the maximum power output. Simulation results show that the flexible fault ride through strategy increases the fault ride through capability of the wind farm clusters and reduces the wind power curtailment during faults.

Discriminative pattern mining in software fault detection

We present a method to enhance fault localization for software systems based on a frequent pattern mining algorithm. Our method is based on a large set of test cases for a given set of programs in which faults can be detected. The test executions are recorded as function call trees. Based on test oracles the tests can be classified into successful and failing tests. A frequent pattern mining algorithm is used to identify frequent subtrees in successful and failing test executions. This information is used to rank functions according to their likelihood of containing a fault. The ranking suggests an order in which to examine the functions during fault analysis. We validate our approach experimentally using a subset of Siemens benchmark programs.

Short-circuit and ground fault analyses and location in VSC-based DC network cables

The application of high-power voltage-source converters (VSCs) to multiterminal dc networks is attracting research interest. The development of VSC-based dc networks is constrained by the lack of operational experience, the immaturity of appropriate protective devices, and the lack of appropriate fault analysis techniques. VSCs are vulnerable to dc-cable short-circuit and ground faults due to the high discharge current from the dc-link capacitance. However, faults occurring along the interconnecting dc cables are most likely to threaten system operation. In this paper, cable faults in VSC-based dc networks are analyzed in detail with the identification and definition of the most serious stages of the fault that need to be avoided. A fault location method is proposed because this is a prerequisite for an effective design of a fault protection scheme. It is demonstrated that it is relatively easy to evaluate the distance to a short-circuit fault using voltage reference comparison. For the more difficult challenge of locating ground faults, a method of estimating both the ground resistance and the distance to the fault is proposed by analyzing the initial stage of the fault transient. Analysis of the proposed method is provided and is based on simulation results, with a range of fault resistances, distances, and operational conditions considered.

Pattern-growth based frequent serial episode discovery

Frequent episode discovery is a popular framework for pattern discovery from sequential data. It has found many applications in domains like alarm management in telecommunication networks, fault analysis in the manufacturing plants, predicting user behavior in web click streams and so on. In this paper, we address the discovery of serial episodes. In the episodes context, there have been multiple ways to quantify the frequency of an episode. Most of the current algorithms for episode discovery under various frequencies are apriori-based level-wise methods. These methods essentially perform a breadth-first search of the pattern space. However currently there are no depth-first based methods of pattern discovery in the frequent episode framework under many of the frequency definitions. In this paper, we try to bridge this gap. We provide new depth-first based algorithms for serial episode discovery under non-overlapped and total frequencies. Under non-overlapped frequency, we present algorithms that can take care of span constraint and gap constraint on episode occurrences. Under total frequency we present an algorithm that can handle span constraint. We provide proofs of correctness for the proposed algorithms. We demonstrate the effectiveness of the proposed algorithms by extensive simulations. We also give detailed run-time comparisons with the existing apriori-based methods and illustrate scenarios under which the proposed pattern-growth algorithms perform better than their apriori counterparts. (C) 2013 Elsevier B.V. All rights reserved.

面向Feistel密码的基于故障传播模式的差分故障分析

密码算法是信息安全研究的核心内容之一，其实际安全性不仅依赖于密码自身的数学特性，也依赖于具体的实现特性。基于实现的密码分析是一种有别于传统密码分析的新型密码分析方法，它利用算法实现时的信息泄露来恢复秘密信息。差分故障分析(Differential Fault Analysis，简称DFA)就是这样一类重要的密码分析方法。 现代密码学中，密码设计通常基于混淆和扩散这两大基本原则。对于一个分组密码而言，选择一个合适的轮函数并进行若干次迭代可以提供必要的混淆和扩散。因此，目前流行的分组密码均为迭代型密码，所采用的典型结构包括Feistel结构、SPN结构和广义Feistel结构等。这些密码结构及其所采用的基础密码组件(例如，S盒和P置换等)的性质，完全决定了故障在传播过程中所呈现的一些模式。直观上，这种内在特征可以用于挖掘DFA攻击和密码结构之间的关系。因此，完全可能利用这种特征来建立一种面向密码结构的系统化DFA攻击方法。 本文主要研究面向Feistel密码的差分故障分析方法，并探讨这类分析方法与已有可证明安全性理论分析结论之间的关系。为此，引入了故障传播路径(Fault Propagation Path，简称FPPath)和故障传播模式(Fault Propagation Pattern，简称FPPattern)的概念，给出了适用于Feistel结构的 FPPath 和 FPPattern 计算方法，建立了与已有可证明安全性理论结果之间的关系。在此基础上，提出了一种面向Feistel密码的基于故障传播模式的 系统化差分故障分析方法。使用该方法，可编程实现FPPath和FPPattern的自动计算，这将有助于针对Feistel密码的自动化DFA攻击的实施。这种情形下，可将FPPath的长度视作评估DFA攻击有效性的一种度量指标。此外，该系统化方法的必然结果是攻击性能的显著提高：不但攻击轮数有所减少，而且故障植入点数量也会减少，这将迅速降低实施一次成功攻击所需的故障密文数。最后，为验证该方法的正确性和有效性，以Camellia密码算法为具体实例，进行了相关模拟攻击实验研究，并给出了相应的数据复杂度分析和时间复杂度分析。通过充分利用Camellia算法中P置换的性质，在不需要穷举搜索的情况下，新攻击方法仅需要6个故障密文即可完全恢复出128位密钥，而成功恢复出192位或256位密钥所需要的故障密文数则为22个。结果表明，基于FPPattern的DFA方法要优于所有已有同类方法。

SMS4密码算法的差分故障攻击

SMS4是用于WAPI的分组密码算法,是国内官方公布的第一个商用密码算法.由于公布时间不长,关于它的安全性研究尚没有公开结果发表.该文研究SMS4密码算法对差分故障攻击的安全性.攻击采用面向字节的随机故障模型,并且结合了差分分析技术.该攻击方法理论上仅需要32个错误密文就可以完全恢复出SMS4的128比特种子密钥.因为实际中故障发生的字节位置是不可能完全平均的,所以实际攻击所需错误密文数将略大于理论值;文中的实验结果也验证了这一事实,恢复SMS4的128bit种子密钥平均大约需要47个错误密文.文章结果显示SMS4对差分故障攻击是脆弱的.为了避免这类攻击,建议用户对加密设备进行保护,阻止攻击者对其进行故障诱导.

SHACAL-2算法的差分故障攻击

该文采用面向字的随机故障模型,结合差分分析技术,评估了SHACAL-2算法对差分故障攻击的安全性。结果显示:SHACAL-2算法对差分故障攻击是不免疫的。恢复出32 bit子密钥的平均复杂度为8个错误密文,完全恢复出512 bit密钥的复杂度为128个错误密文。