Criteria-based alarm flood pattern recognition using historical data from automated production systems (aPS)

The operation of industrial automated production systems (aPS) usually requires human operators that observe and, if necessary, intervene to keep aPS in steady operation. Inside the distributed control system (DCS) of an aPS, notifications are generated by an alarm management system (AMS) and visualized, informing operators about critical aPS situations, e.g. faults of a device. Since a huge number of notifications are usually configured inside the AMS, operators nowadays often face the problem of receiving more notifications than they can physically address. This paper proposes an approach, which allows automatic identification of alarm floods by using criteria-based search strategies. In order to address the problem statement, four hypotheses are stated. To evaluate the proposed algorithm regarding its ability to identify causally dependent notifications, historical notification logs of real industrial aPS are analyzed. For this purpose, notification logs of eight existing industrial aPS as well as the assessment of industrial experts are taken into account.     

Interaction information: linear and nonlinear interpretations

This paper addresses the problem of how the self can know the existence of external reality, an environmental whole called proto-reality, in relation to metaphysical properties, deterministic or not, of worlds that the self can know. The self can only construct an outside within itself. Hence, departing from a solipsistic world where no external reality is assumed, the principle-based generation of external reality is articulated from a given perceptual sequence consisting of indubitable, direct percepts. New, external symbols are derived by an algorithmic rule called inverse causality operating on a given perceptual sequence. Three forms of world arise depending on die applied level of operation of the algorithm: part (the self) or whole (the self plus perceived reality). These include a solipsistic world; a semi-deterministic (positivistic) world composed of the self and what the self can directly discriminate; and a deterministic world composed of the self and what the self can discriminate either directly or indirectly.     

The future of research in understanding and controlling work-related low back disorders

Our knowledge of low back disorder (LBD) causation has progressed well over the years with in-depth understanding accelerating in the traditional disciplines of biomechanics, psychology, psychophysics, psychosocial, physiology, genetics, organizational psychology and rehabilitation. However, each of these disciplines has studied LBD causality in isolation of other disciplines. The underlying assumption is that each discipline can fully explain causality and each discipline is treated as if it were mutually exclusive and exhaustive of the other disciplines. Hence, the body of knowledge has progressed along research silos where we have in-depth knowledge along given research tracks that are defined by the boundaries of the discipline. Furthermore, a wealth of knowledge has been amassed within each of these research silos. How can they all be correct if they are indeed mutually exclusive and exhaustive? The answer is: they cannot be. This brief review of the state-of-the art in LBD research applied to ergonomics, suggests that instead of observing LBD through the myopic lens of each discipline, we need to begin to view LBD causality as a system. Recent work attempting to understand the interaction between these traditional disciplines has demonstrated that many of the findings along these silos are really interrelated and can be explained in terms of changes in the biomechanical loading at the tissue level. It is argued that further efforts to understand these interactions represent the next level of understanding causality of LBDs.     

Causal relationship between energy consumption (EC) and GDP: A Markov-switching (MS) causality

This paper re-examines the causal relationship between energy use and Gross Domestic Product (GDP) in the United States for the period 1960-2005. To that end, we use Markov-switching vector autoregressive (MS-VAR) models, rather than vector autoregressive (VAR) models, which allows for regime shifts. These models are capable of detecting changes in the relationship between variables; in addition, the coefficients of the model are time dependent and they depend on the states of the variables. Therefore, in contrast to VAR and vector error correction models (VECM), which assume a stable relationship, the relationship between the variables could be different in the separate regimes.Results from the estimation of MS models show changes in the pattern of causality relationship between GDP and energy use. That is, we found evidence of bidirectional Granger causality (GC) between the variables in the first regime, while there is no GC between the variables in the second regime. The first regime consists of 1971-1975, 1977-1982, 1989-1995, and from 2001 to the end of the sample. This regime includes the energy crises in 1970s, the recessions in the early 1980s, 1990s, and the recession in 2001.     

Linear and nonlinear causality between sectoral electricity consumption and economic growth: Evidence from Taiwan

This study investigates the linear and nonlinear causality between the total electricity consumption (TEC) and real gross domestic production (RGDP). Unlike previous literature, we solve the undetermined relation between RGDP and electricity consumption by classifying TEC into industrial sector consumption (ISC) and residential sector consumption (RSC) as well as investigating how TEC, ISC, and RSC influence Taiwan's RGDP. By using the Granger's linear causality test, it is shown that (i) there is a bidirectional causality among TEC, ISC, and RGDP, but a neutrality between RSC and RGDP with regard to the linear causality and (ii) there is still a bidirectional causality between TEC and RGDP, but a unidirectional causality between RSC and RGDP with regard to the nonlinear causality. On the basis of (i) and (ii), we suggest that the electricity policy formulators loosen the restriction on ISC and limit RSC in order to achieve the goal of economic growth.     

Testing causal relationships between wholesale electricity prices and primary energy prices

We apply the lag-augmented vector autoregression technique to test the Granger-causal relationships among wholesale electricity prices, natural gas prices, and crude oil prices. In addition, by adopting a cross-correlation function approach, we test not only the causality in mean but also the causality in variance between the variables. The results of tests using both techniques show that gas prices Granger-cause electricity prices in mean. We find no Granger-causality in variance among these variables.     

CAUSALITY AND SOCIAL SYSTEMS ANALYSIS

In the study of social systems a causal orientation leads to the familiar two-variable analysis of components of the systems, rather than the analysis of the characteristics of the system as a whole. The result is that predictions made about social systems are usually either wrong or trivially correct. A general systems approach docs not lean on causal analysis and, therefore, provides the analytical framework for comprehending system characteristics, and predicting their state.     

On strong stabilization for linear time-varying systems

This paper deals with the strong stabilization problem for linear time-varying systems and gives a sufficient condition, in terms of the coprime factors, for the existence of strong stabilizers for such a system.     

基于因果图最小割集的故障分析方法研究

复杂系统的最小割集很多,对故障诊断很重要,虽然因果图能够进行故障诊断,但缺乏对最小割集的分析.通过最小割集可以进行故障诊断,本文打算研究基于最小割集的故障诊断方法,并将这种新方法成功运用于煤矿瓦斯爆炸的故障分析中.由于根据故障发生的概率大小依次进行诊断,利用了正常事件的剪枝信息和多故障联合诊断,因此这种方法的诊断速度较快,效率较高.研究表明,基于因果图最小割集的故障分析方法,充分结合多种信息,可以快速有效地进行故障诊断.     

Constructing structural VAR models with conditional independence graphs

In this paper graphical modelling is used to select a sparse structure for a multivariate time series model of New Zealand interest rates. In particular, we consider a recursive structural vector autoregressions that can subsequently be described parsimoniously by a directed acyclic graph, which could be given a causal interpretation. A comparison between competing models is then made by considering likelihood and economic theory.