Bayesian network-based root cause analysis and fault pathway identification in complex industrial processes

Real-time root cause identification (RCI) of faults or abnormal events in industries gives plant personnel the opportunity to address the faults before they progress and lead to failure. RCI in industrial systems must deal with their complex behavior, variable interactions, corrective actions of control systems and variability in faulty behavior. Bayesian networks (BNs) is a data-driven graph-based method that utilizes multivariate sensor data generated during industrial operations for RCI. Bayesian networks, however, require data discretization if data contains both discrete and continuous variables. Traditional discretization techniques such as equal width (EW) or equal frequency (EF) discretization result in loss of dynamic information and often lead to erroneous RCI. To deal with this limitation, we propose the use of a dynamic discretization technique called Bayesian Blocks (BB) which adapts the bin sizes based on the properties of data itself. In this work, we compare the effectiveness of three discretization techniques, namely EW, EF and BB coupled with Bayesian Networks on generation of fault propagation (causal) maps and root cause identification in complex industrial systems. We demonstrate the performance of the three methods on the industrial benchmark Tennessee-Eastman (TE) process. For two complex faults in the TE process, the BB with BN method successfully diagnosed correct root causes of the faults, and reduced redundancy (up to 50%) and improved the propagation paths in causal maps compared to other two techniques.

Can Strategic Behaviour Facilitate Confusion Resolution? The Interplay Between Confusion and Metacognitive Strategies in Betty’s Brain

Confusion may benefit learning when it is resolved or partially resolved. Metacognitive strategies (MS) may help learners to resolve confusion when it occurs during learning and problem solving. This study examined the relationship between confusion and MS that students evoked in Betty’s Brain, a computer-based learning-by-modelling environment where elementary and middle school students learn science by building causal maps. Participants were sixth graders. Emotion data were collected from real-time observations by trained researchers. MS and task performance information were determined by analyzing the action logs. Pre- and post-tests were used to assess learning gains. The results revealed that the use of MS was a function of the state of student confusion. However, confusion resolution was not related to MS behaviour, and MS did not moderate the effect of confusion on student task performance in Betty’s Brain or on learning gains.

A Multimethodology for Supporting the Boundary Selection of System Dynamics Models

System dynamics models can produce knowledge for decision-makers and, consequently, provide better choices. To be effective in its purpose, a model must reproduce an observed problem situation effectively. Hence, the compatibility between the observed problem situation and the created model is essential and represents a considerable challenge. In this context, this paper aims to describe an adaptation of the problem structuring method ‘Strategic Options Development and Analysis’ (SODA), used in the Problem Articulation (Boundary Selection) step of the system dynamics modelling process. In summary, this adaptation consists of: (1) Selecting of stakeholders; (2) Capturing, aggregating and interpreting the insights using cognitive and causal maps, and (3) Using the interpretation of the causal maps for building a system dynamics model. The method proved to be satisfactory since it was able to direct the construction of a System Dynamics model based on a problem situation perceived by stakeholders acting in the native forests of the state of Santa Catarina, Brazil.

A Multimethodology for Supporting the Boundary Selection of System Dynamics Models

System dynamics models can produce knowledge for decision-makers and, consequently, provide better choices. To be effective in its purpose, a model must reproduce an observed problem situation effectively. Hence, the compatibility between the observed problem situation and the created model is essential and represents a considerable challenge. In this context, this paper aims to describe an adaptation of the problem structuring method ‘Strategic Options Development and Analysis’ (SODA), used in the Problem Articulation (Boundary Selection) step of the system dynamics modelling process. In summary, this adaptation consists of: (1) Selecting of stakeholders; (2) Capturing, aggregating and interpreting the insights using cognitive and causal maps, and (3) Using the interpretation of the causal maps for building a system dynamics model. The method proved to be satisfactory since it was able to direct the construction of a System Dynamics model based on a problem situation perceived by stakeholders acting in the native forests of the state of Santa Catarina, Brazil.

Causal Mapping for Strategy Execution: Pitfalls and Applications

Organizations can attempt to improve strategy implementation by developing strategy execution maps, which aim to translate strategic objectives into specific activities and provide sufficient clarity to inform employees’ decisions and actions. However, managers often encounter pitfalls, both in framing the process and in developing maps. This article suggests how to overcome these pitfalls, describes several applications of causal maps to further enhance strategy execution, and illustrates strategy execution maps for organizations with distinctive strategies.

Causal maps versus correlation maps: visual analysis of tropical-extratropical atmospheric teleconnections using causal discovery

<p>In climatology, correlation maps are often used to study the relationships between one 1D time series and a (spatiotemporal) 2D or even 3D field. However, correlation measures do not necessarily capture causal relationships and similarities in correlation maps obtained from different indices may appear if the set of indices contains correlated variables. Causal discovery tools such as the Peter and Clark – Momentary conditional independence (PCMCI) algorithm can help in disentangling spurious from causal links in both linear and nonlinear frameworks. In the linear case considered in the present work, PCMCI extends standard correlation analysis by removing the confounding effects of autocorrelation, indirect links and common drivers. Combining PCMCI and Causal Effect Networks on a 2D field helps identifying, and subsequently discarding the spurious correlations and thereby allows to retain only the causal links. The resulting visualization technique is referred to as a “causal map”.</p><p>In this presentation, we illustrate the application of causal maps in combination with maximum covariance analysis to assess how tropical convection interacts with mid-latitude circulation during boreal summer at different intraseasonal timescales. The obtained causal maps reveal the dominant patterns of interaction and highlight specific mid-latitude regions that are most strongly connected to tropical convection. In general, the identified causal teleconnection patterns are only mildly affected by ENSO variability and the tropical-mid-latitude linkages remain similar under different types of ENSO phases. Still, La Niña strengthens the South Asian monsoon generating a stronger response in the mid-latitudes, while during El Niño periods, the western North Pacific summer monsoon pattern is reinforced. Our study paves the way for a process-based validation of boreal summer teleconnections in (sub-)seasonal forecast models and climate models and therefore provides important clues towards improved sub-seasonal and climate projections.</p><p> </p><p>Reference: G. Di Capua, J. Runge, R.V. Donner, B. van den Hurk, A.G. Turner, R. Vellore, R. Krishnan, D. Coumou: Dominant patterns of interaction between the tropics and mid-latitudes in boreal summer: Causal relationships and the role of time-scales. Weather and Climate Dynamics, 1, 519-539 (2020)</p>

Application of systems dynamics and group model building to identify barriers and facilitators to acute care delivery in a resource limited setting

Background Group model building (GMB) is a method to facilitate shared understanding of structures and relationships that determine system behaviors. This project aimed to determine the feasibility of GMB in a resource-limited setting and to use GMB to describe key barriers and facilitators to effective acute care delivery at a tertiary care hospital in Malawi. Methods Over 1 week, trained facilitators led three GMB sessions with two groups of healthcare providers to facilitate shared understanding of structures and relationships that determine system behaviors. One group aimed to identify factors that impact patient flow in the paediatric special care ward. The other aimed to identify factors impacting delivery of high-quality care in the paediatric accident and emergency room. Synthesized causal maps of factors influencing patient care were generated, revised, and qualitatively analyzed. Results Causal maps identified patient condition as the central modifier of acute care delivery. Severe illness and high volume of patients were identified as creating system strain in several domains: (1) physical space, (2) resource needs and utilization, (3) staff capabilities and (4) quality improvement. Stress in these domains results in worsening patient condition and perpetuating negative reinforcing feedback loops. Balancing factors inherent to the current system included (1) parental engagement, (2) provider resilience, (3) ease of communication and (4) patient death. Perceived strengths of the GMB process were representation of diverse stakeholder viewpoints and complex system synthesis in a visual causal pathway, the process inclusivity, development of shared understanding, new idea generation and momentum building. Challenges identified included time required for completion and potential for participant selection bias. Conclusions GMB facilitated creation of a shared mental model, as a first step in optimizing acute care delivery in a paediatric facility in this resource-limited setting.

Fast-growth firms and successful environmental entrepreneurs in emerging countries

Growth is a broad area and many aspect of growth is under research especially in smaller and entrepreneurial firms. Many research show that growth and fast growth happens randomly and is not a continuous phenomenon. In this study, we investigate how successful entrepreneurs grow their firms. The investigation involved ten fast-growth firm cases in Iran -as an emerging economy- in different industries. The research is qualitative and data gathered through semi structured in-depth interviews. After coding, all interviews are mapped. By using Decision Explorer® all causal maps are analyzed. Analysis show that fast growth is a direct outcome of entrepreneurial marketing (EM) practices and indirectly influenced by serendipity. Serendipity is an element observed in many firms that generally occurs at the initial phase of firm formation and could bring great opportunities but indirectly associated with fast-growth. However, serendipity itself is not important but the ability to recognize and exploit opportunity is a crucial activity that entrepreneurs are really great at it. The ability of entrepreneurs to exploit serendipitous opportunities and use entrepreneurial marketing practices in terms of innovative products and activities/process lead to fast growth. Other elements, such as network, innovation, and perseverance, could either directly or indirectly influence growth.