抽油机井下故障自动诊断软件设计

在介绍抽油机井下故障自动诊断在理的基础上,给出了该软件包的设计思路,并对其关键技术作了重点说明。     

内建自测试中多输入特征寄存器的硬件开销的减少

在内建自测试中,针对随机向量测试,本文提出了一种通过输出信号分组压缩来减少多输入特征寄存器MISR的硬件开销的方法。该方法是在分析输出信号之间相关性的基础上,根据给定的MISR阶数构造具有最小相关度的输出信号集合组,以此来减少输出信号分组压缩时的故障覆盖率损失。该方法不需附加任何辅助电路。     

Subjective evaluation of software evolvability using code smells: An empirical study

This paper presents the results of an empirical study on the subjective evaluation of code smells that identify poorly evolvable structures in software. We propose use of the term software evolvability to describe the ease of further developing a piece of software and outline the research area based on four different viewpoints. Furthermore, we describe the differences between human evaluations and automatic program analysis based on software evolvability metrics. The empirical component is based on a case study in a Finnish software product company, in which we studied two topics. First, we looked at the effect of the evaluator when subjectively evaluating the existence of smells in code modules. We found that the use of smells for code evaluation purposes can be difficult due to conflicting perceptions of different evaluators. However, the demographics of the evaluators partly explain the variation. Second, we applied selected source code metrics for identifying four smells and compared these results to the subjective evaluations. The metrics based on automatic program analysis and the human-based smell evaluations did not fully correlate. Based upon our results, we suggest that organizations should make decisions regarding software evolvability improvement based on a combination of subjective evaluations and code metrics. Due to the limitations of the study we also recognize the need for conducting more refined studies and experiments in the area of software evolvability.

Microcontroller-based process monitoring and management using embedded Petri-nets

This paper presents a methodology enabling the deployment and utilisation of a microcontroller based process monitoring system. This approach utilises the results of research into the embedding of Petri-net functions within microcontroller based architectures. This research is presented here through its application to the design, development and deployment of a process monitoring system into a laboratory based hydro-forming press test rig. The paper illustrates how this approach can provide real time online process information and demonstrates how the microcontroller-based architecture can be used to identify the cause and effects of process faults. The paper further considers how results are captured and processed, and how the management information that they enable can be displayed using a web page based architecture.     

Fault prediction and the discriminative powers of connectivity-based object-oriented class cohesion metrics

Context

Several metrics have been proposed to measure the extent to which class members are related. Connectivity-based class cohesion metrics measure the degree of connectivity among the class members.

Objective

We propose a new class cohesion metric that has higher discriminative power than any of the existing cohesion metrics. In addition, we empirically compare the connectivity and non-connectivity-based cohesion metrics.

Method

The proposed class cohesion metric is based on counting the number of possible paths in a graph that represents the connectivity pattern of the class members. We theoretically and empirically validate this path connectivity class cohesion (PCCC) metric. The empirical validation compares seven connectivity-based metrics, including PCCC, and 11 non-connectivity-based metrics in terms of discriminative and fault detection powers. The discriminative-power study explores the probability that a cohesion metric will incorrectly determine classes to be cohesively equal when they have different connectivity patterns. The fault detection study investigates whether connectivity-based metrics, including PCCC, better explain the presence of faults from a statistical standpoint in comparison to other non-connectivity-based cohesion metrics, considered individually or in combination.

Results

The theoretical validation demonstrates that PCCC satisfies the key cohesion properties. The results of the empirical studies indicate that, in contrast to other connectivity-based cohesion metrics, PCCC is much better than any comparable cohesion metric in terms of its discriminative power. In addition, the results also indicate that PCCC measures cohesion aspects that are not captured by other metrics, wherein it is considerably better than other connectivity-based metrics but slightly worse than some other non-connectivity-based cohesion metrics in terms of its ability to predict faulty classes.

Conclusion

PCCC is more useful in practice for the applications in which practitioners need to distinguish between the quality of different classes or the quality of different implementations of the same class.     

Fault-based test suite prioritization for specification-based testing

Context

Existing test suite prioritization techniques usually rely on code coverage information or historical execution data that serve as indicators for estimating the fault-detecting ability of test cases. Such indicators are primarily empirical in nature and not theoretically driven; hence, they do not necessarily provide sound estimates. Also, these techniques are not applicable when the source code is not available or when the software is tested for the first time.

Objective

We propose and develop the novel notion of fault-based prioritization of test cases which directly utilizes the theoretical knowledge of their fault-detecting ability and the relationships among the test cases and the faults in the prescribed fault model, based on which the test cases are generated.

Method

We demonstrate our approach of fault-based prioritization by applying it to the testing of the implementation of logical expressions against their specifications. We then validate our proposal by an empirical study that evaluates the effectiveness of prioritization techniques using two different metrics.

Results

A theoretically guided fault-based prioritization technique generally outperforms other techniques under study, as assessed by two different metrics. Our empirical results also show that the technique helps to reveal all target faults by executing only about 72% of the prioritized test suite, thereby reducing the effort required in testing.

Conclusions

The fault-based prioritization approach is not only applicable to the instance empirically validated in this paper, but should also be adaptable to other fault-based testing strategies. We also envisage new research directions to be opened up by our work.     

Intelligent fault inference for rotating flexible rotors using Bayesian belief network

Flexible rotor is a crucial mechanical component of a diverse range of rotating machineries and its condition monitoring and fault diagnosis are of particular importance to the modern industry. In this paper, Bayesian belief network (BBN) is applied to the fault inference for rotating flexible rotors with attempt to enhance the reasoning capacity under conditions of uncertainty. A generalized three-layer configuration of BBN for the fault inference of rotating machinery is developed by fully incorporating human experts’ knowledge, machine faults and fault symptoms as well as machine running conditions. Compared with the Naive diagnosis network, the proposed topological structure of causalities takes account of more practical and complete diagnostic information in fault diagnosis. The network tallies well with the practical thinking of field experts in the whole processes of machine fault diagnosis. The applications of the proposed BBN network in the uncertainty inference of rotating flexible rotors show good agreements with our knowledge and practical experience of diagnosis.     

Induction motors bearing fault detection using pattern recognition techniques

This paper proposes a systematic procedure based on a pattern recognition technique for fault diagnosis of induction motors bearings through the artificial neural networks (ANNs). In this method, the use of time domain features as a proper alternative to frequency features is proposed to improve diagnosis ability. The features are obtained from direct processing of the signal segments using very simple calculation. Three different cases including, healthy, inner race defect and outer race defect are investigated using the proposed algorithm. The ANNs are trained with a subset of the experimental data for known machine conditions. Once the network is trained, efficiency of the proposed method is evaluated using the remaining set of data. The obtained results indicate that using time domain features can be effective in accurate diagnosis of various motor bearing faults with high precision and low computational burden.     

用C#实现大文件切割与合并

我们经常需要将大文件切割成若干小文件来携带或传输,然后在另一台计算机上再度合并。本文介绍了文件切割与合并的原理,以及自动产生.exe合并程序和自动删除合并程序等技巧,并提供了关键部分的C≠源代码。     

加速寿命试验方法及评估

加速寿命试验是为缩短试验时间,在不改变故障模式和故障机理的条件下,用加大应力的方法进行的试验。随着元器件水平的迅速提高,高可靠、长寿命的产品越来越多,在正常应力水平下进行寿命试验来评定产品的可靠性已不能满足实际需要,代价很高,不现实,目前广泛采用了加速寿命试验。本文介绍了加速寿命试验方法及评估,并给出了案例。