非稳态故障信号的深度挖掘方法仿真分析

机械设备在运转中转速总会存在波动或者发生较大变化,与转速相关的故障特征频率也会产生一定的波动,甚至是较大范围的跳跃,设备振动故障特征频率存在非线性,很难用模型公式描述.传统的采用小波分解的故障特征挖掘方法,将信号分解到不同频段上,判断故障特征频率的一致性,上述方法诊断条件单一、规则惟一,一旦故障信号不完整或信号波动,就无法进行诊断.为了避免上述缺陷,提出了一种融合诊断网络算法的非稳态故障信号的深度挖掘方法.利用小波变换方法,提取非稳态故障信号的特征.再利用融合诊断网络,获取故障信息,从而实现非稳态故障信号的深度挖掘.实验结果表明,利用改进算法能够有效提高非稳态故障信号挖掘的准确性,有利于故障的快速修复.     

Comparing a knowledge-based and a data-driven method in querying data streams for system fault detection: A hydraulic drive system application

The field of fault detection and diagnosis has been the subject of considerable interest in industry. Fault detection may increase the availability of products, thereby improving their quality. Fault detection and diagnosis methods can be classified in three categories: data-driven, analytically based, and knowledge-based methods.     

A review of procedures to evolve quantum algorithms

There exist quantum algorithms that are more efficient than their classical counterparts; such algorithms were invented by Shor in 1994 and then Grover in 1996. A lack of invention since Grover’s algorithm has been commonly attributed to the non-intuitive nature of quantum algorithms to the classically trained person. Thus, the idea of using computers to automatically generate quantum algorithms based on an evolutionary model emerged. A limitation of this approach is that quantum computers do not yet exist and quantum simulation on a classical machine has an exponential order overhead. Nevertheless, early research into evolving quantum algorithms has shown promise. This paper provides an introduction into quantum and evolutionary algorithms for the computer scientist not familiar with these fields. The exciting field of using evolutionary algorithms to evolve quantum algorithms is then reviewed.

基于数据驱动的故障诊断技术研究现状及展望

对基于数据驱动的过程故障诊断方法进行了总结和划分,其中包含多元统计方法、机器学习方法、流形学习方法等。将各类基于数据驱动的故障诊断方法的原理、研究进展及其在工业过程中的应用进行了描述和分析,最后指出这一领域中需要进一步解决的问题以及近期的研究热点。     

Machine learning: a review of classification and combining techniques

Supervised classification is one of the tasks most frequently carried out by so-called Intelligent Systems. Thus, a large number of techniques have been developed based on Artificial Intelligence (Logic-based techniques, Perceptron-based techniques) and Statistics (Bayesian Networks, Instance-based techniques). The goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various classification algorithms and the recent attempt for improving classification accuracy—ensembles of classifiers.     

基于粗糙集与K-均值聚类的故障知识挖掘

针对连续数据故障诊断知识挖掘,提出了一种将粗糙集理论与K-均值聚类算法相结合的故障诊断知识挖掘方法。该方法在提取设备状态参数数据的基础上,应用K-均值聚类算法将各状态参数下的连续数据离散化为有限类别,再应用粗糙集对所获得的离散数据表进行约简,获得由状态数据出发的故障诊断知识表格。应用于柴油机故障诊断数据知识挖掘,可以快速准确地获得故障诊断知识,方法简单易用。     

Evolutionary design of Evolutionary Algorithms

Manual design of Evolutionary Algorithms (EAs) capable of performing very well on a wide range of problems is a difficult task. This is why we have to find other manners to construct algorithms that perform very well on some problems. One possibility (which is explored in this paper) is to let the evolution discover the optimal structure and parameters of the EA used for solving a specific problem. To this end a new model for automatic generation of EAs by evolutionary means is proposed here. The model is based on a simple Genetic Algorithm (GA). Every GA chromosome encodes an EA, which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization are generated by using the considered model. Numerical experiments show that the EAs perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.     

Lightning search algorithm

This paper introduces a novel metaheuristic optimization method called the lightning search algorithm (LSA) to solve constraint optimization problems. It is based on the natural phenomenon of lightning and the mechanism of step leader propagation using the concept of fast particles known as projectiles. Three projectile types are developed to represent the transition projectiles that create the first step leader population, the space projectiles that attempt to become the leader, and the lead projectile that represent the projectile fired from best positioned step leader. In contrast to that of the counterparts of the LSA, the major exploration feature of the proposed algorithm is modeled using the exponential random behavior of space projectile and the concurrent formation of two leader tips at fork points using opposition theory. To evaluate the reliability and efficiency of the proposed algorithm, the LSA is tested using a well-utilized set of 24 benchmark functions with various characteristics necessary to evaluate a new algorithm. An extensive comparative study with four other well-known methods is conducted to validate and compare the performance of the LSA. The result demonstrates that the LSA generally provides better results compared with the other tested methods with a high convergence rate.     

基于仿真与数据挖掘的故障诊断方法研究

针对导弹武器系统故障信息的特点,提出了系统仿真与数据挖掘相结合的综合故障诊断方法.文中将导弹武器系统故障检测信息分为3类,即:离散交互特征信息、连续动态特征信息和离散事件特征信息.通过实例阐述了数据挖掘在导弹武器系统故障诊断中的应用.分析了基于仿真的故障诊断方法,在此基础上,结合仿真与数据挖掘各自在故障诊断方面的优势,进一步提出了基于仿真与数据挖掘的综合诊断方法,给出了方法的步骤和诊断流程.     

基于粗糙集理论的煤磨故障诊断模型

针对水泥煤磨系统的常见故障,将基于粗糙集理论的数据挖掘技术应用于水泥煤磨故障诊断。结合具体的数据,采用粗糙集的方式,建立了故障诊断系统,通过该系统对识别数据进行故障诊断实验,表明该系统能有效地对煤磨堵仓和钢球小故障进行诊断。诊断结果的精度可以满足现场应用的要求。     

Multi-objective PSO algorithm for mining numerical association rules without a priori discretization

In the domain of association rules mining (ARM) discovering the rules for numerical attributes is still a challenging issue. Most of the popular approaches for numerical ARM require a priori data discretization to handle the numerical attributes. Moreover, in the process of discovering relations among data, often more than one objective (quality measure) is required, and in most cases, such objectives include conflicting measures. In such a situation, it is recommended to obtain the optimal trade-off between objectives. This paper deals with the numerical ARM problem using a multi-objective perspective by proposing a multi-objective particle swarm optimization algorithm (i.e., MOPAR) for numerical ARM that discovers numerical association rules (ARs) in only one single step. To identify more efficient ARs, several objectives are defined in the proposed multi-objective optimization approach, including confidence, comprehensibility, and interestingness. Finally, by using the Pareto optimality the best ARs are extracted. To deal with numerical attributes, we use rough values containing lower and upper bounds to show the intervals of attributes. In the experimental section of the paper, we analyze the effect of operators used in this study, compare our method to the most popular evolutionary-based proposals for ARM and present an analysis of the mined ARs. The results show that MOPAR extracts reliable (with confidence values close to 95%), comprehensible, and interesting numerical ARs when attaining the optimal trade-off between confidence, comprehensibility and interestingness.     

数据挖掘建模研究

数据挖掘和建模都是应用于众多问题的通用技术,而且数据挖掘建模是沟通实际问题与数据挖掘工具的桥梁。然而对于数据挖掘建模的概念有很多模糊的认识。本文从数据建模的概念入手,详细地阐述数据挖掘建模的概念,然后从数据挖掘工具与建模的角度把握数据挖掘建模,并详细介绍数据挖掘建模的一般步骤。     

Updating generalized association rules with evolving taxonomies

Mining generalized association rules among items in the presence of taxonomies has been recognized as an important model for data mining. Earlier work on mining generalized association rules, however, required the taxonomies to be static, ignoring the fact that the taxonomies of items cannot necessarily be kept unchanged. For instance, some items may be reclassified from one hierarchy tree to another for more suitable classification, abandoned from the taxonomies if they will no longer be produced, or added into the taxonomies as new items. Additionally, the analysts might have to dynamically adjust the taxonomies from different viewpoints so as to discover more informative rules. Under these circumstances, effectively updating the discovered generalized association rules is a crucial task. In this paper, we examine this problem and propose two novel algorithms, called Diff_ET and Diff_ET2, to update the discovered frequent itemsets. Empirical evaluation shows that the proposed algorithms are very effective and have good linear scale-up characteristics.     

Linear self-stabilizing algorithms for the independent and dominating set problems using an unfair distributed scheduler

This paper presents distributed self-stabilizing algorithms for the maximal independent and the minimal dominating set problems. Using an unfair distributed scheduler the algorithms stabilizes in at most max{3n−5,2n} resp. 9n moves. All previously known algorithms required O(n²) moves.     

Evaluating the change of software fault behavior with dataset attributes based on categorical correlation

Utilization of data mining in software engineering has been the subject of several research papers. Majority of subjects of those paper were in making use of historical data for decision making activities such as cost estimation and product or project attributes prediction and estimation. The ability to predict software fault modules and the ability to correlate relations between faulty modules and product attributes using statistics is the subject of this paper. Correlations and relations between the attributes and the categorical variable or the class are studied through generating a pool of records from each dataset and then select two samples every time from the dataset and compare them. The correlation between the two selected records is studied in terms of changing from faulty to non-faulty or the opposite for the module defect attribute and the value change between the two records in each evaluated attribute (e.g. equal, larger or smaller). The goal was to study if there are certain attributes that are consistently affecting changing the state of the module from faulty to none, or the opposite. Results indicated that such technique can be very useful in studying the correlations between each attribute and the defect status attribute. Another prediction algorithm is developed based on statistics of the module and the overall dataset. The algorithm gave each attribute true class and faulty class predictions. We found that dividing prediction capability for each attribute into those two (i.e. correct and faulty module prediction) facilitate understanding the impact of attribute values on the class and hence improve the overall prediction relative to previous studies and data mining algorithms. Results were evaluated and compared with other algorithms and previous studies. ROC metrics were used to evaluate the performance of the developed metrics. Results from those metrics showed that accuracy or prediction performance calculated traditionally using accurately predicted records divided by the total number of records in the dataset does not necessarily give the best indicator of a good metric or algorithm predictability. Those predictions may give wrong implication if other metrics are not considered with them. The ROC metrics were able to show some other important aspects of performance or accuracy.     

隐私保护数据挖掘算法综述

如何保护私有信息或敏感知识在挖掘过程中不被泄露,同时能得到较为准确的挖掘结果,目前已经成为数据挖掘研究中的一个很有意义的研究课题。本文通过对当前隐私保护数据挖掘中具有代表性的算法按照数据分布对其中的数据更改方法、数据挖掘算法、数据或规则隐藏等进行了详细阐述,并对各自的优缺点进行了分析和比较,总结出了各种算法的特性。此外,通过对比提出了隐私保护数据挖掘算法的评价标准,即保密性、规则效能、算法复杂性、扩展性,以便在今后的研究中提出新的有效算法。     

基于遗传算法的DM在故障诊断中的应用研究

介绍基于遗传算法的数据挖掘在设备故障诊断中的应用,并在应用中根据实际问题改进遗传算法。依据石化企业采集到的关键设备运转状态的历史数据,应用优化的遗传算法构建分类模型。利用建立的分类模型可以对新数据进行判别归类,识别出设备故障的种类,依此找到故障的原因并消除故障,为工程技术人员和决策者提供有力的决策支持。