Daniel Bumblauskas

Big data analytics: transforming data to action

Purpose The purpose of this paper is to provide a conceptual model for the transformation of big data sets into actionable knowledge. The model introduces a framework for converting data to actionable knowledge and mitigating potential risk to the organization. A case utilizing a dashboard provides a practical application for analysis of big data. Design/methodology/approach The model can be used both by scholars and practitioners in business process management. This paper builds and extends theories in the discipline, specifically related to taking action using big data analytics with tools such as dashboards. Findings The authors’ model made use of industry experience and network resources to gain valuable insights into effective business process management related to big data analytics. Cases have been provided to highlight the use of dashboards as a visual tool within the conceptual framework. Practical implications The literature review cites articles that have used big data analytics in practice. The transitions required to reach the actionable knowledge state and dashboard visualization tools can all be deployed by practitioners. A specific case example from ESP International is provided to illustrate the applicability of the model. Social implications Information assurance, security, and the risk of large-scale data breaches are a contemporary problem in society today. These topics have been considered and addressed within the model framework. Originality/value The paper presents a unique and novel approach for parsing data into actionable knowledge items, identification of viruses, an application of visual dashboards for identification of problems, and a formal discussion of risk inherent with big data.

Maintenance and recurrent event analysis of circuit breaker data

Purpose – The purpose of this paper is to review cotemporary maintenance programs and analyze factory production data for an SF6 gas filled circuit breaker population. Various maintenance techniques and studies are reviewed to understand the reliability of circuit breaker models and the impact manufacturing can have on long term maintenance considerations.Design/methodology/approach – Production and field event data were analyzed using statistical analysis tools. The population data were formatted so that a recurrent event analysis could be conducted to establish the mean cumulative function (MCF) by model and product family (class). Average Field Two‐year Recorded Event Rate (AFTRER) is introduced and compared to commonly used Field Incident Rate (FIR) and Mean‐Time between Failure (MTBF) measures.Findings – Common managerial operating questions can be answered as exhibited for the provided circuit breaker population. This includes the longevity of field issues, the anticipated life cycle of a model or c...

A Case Study in Estimating Avionics Availability from Field Reliability Data

Under incentivized contractual mechanisms such as availability-based contracts the support service provider and its customer must share a common understanding of equipment reliability baselines. Emphasis is typically placed on the Information Technology-related solutions for capturing, processing and sharing vast amounts of data. In the case of repairable fielded items scant attention is paid to the pitfalls within the modelling assumptions that are often endorsed uncritically, and seldom made explicit during field reliability data analysis. This paper presents a case study in which good practices in reliability data analysis are identified and applied to real-world data with the aim of supporting the effective execution of a defence avionics availability-based contract. The work provides practical guidance on how to make a reasoned choice between available models and methods based on the intelligent exploration of the data available in practical industrial applications.

A Markov decision process model case for optimal maintenance of serially dependent power system components

Purpose – Using a case study for electrical power equipment, the purpose of this paper is to investigate the importance of dependence between series-connected system components in maintenance decisions. Design/methodology/approach – A continuous-time Markov decision model is formulated to find a minimum cost maintenance policy for a circuit breaker as an independent component while considering a downstream transformer as a dependent component. Maintenance of the dependent component is included implicitly in terms of the costs associated with certain state-action pairs. For policy and cost comparisons, a separate model is also formulated that considers only the circuit breaker as the independent component. After uniformizing the continuous-time models to discrete time, standard methods are used to solve for the average-cost-optimal policies of each model. Findings – The optimal maintenance policy and its cost differ significantly depending on whether or not the dependent component is considered. Research l...

An Overcurrent Protection Relay Based on Local Measurements

Power grid overcurrent relays are used to protect the interphase faults as well as single-phase-to-ground faults. However, these relays do not guarantee protection due to the rapidly increasing short circuits which may be the consequence of demanding more and more power usage by the commercial and residential users. In this paper, a method is presented to improve the functionality of the overcurrent relay. This method is based on locally accessible measurements. It does not require any online-information and communication facilities regarding varying short-circuit levels caused by distributed energy resource infeeds. This method is adaptive and uses a least square algorithm to determine the Thevenin circuit equivalent using local measurements. The proposed method is evaluated, and the results indicate that the Thevenin-equivalent-based method provides an improvement to the relay tripping time.

Smart Maintenance Decision Support Systems (SMDSS) based on corporate big data analytics

A framework for smart maintenance decision support system is provided.Applications using big data analytics and a specific case study for the electrical utility industry are detailed.An integrated expert system making use of Markov Decision Process and Analytical Hierarchy Process Models is developed. The purpose of this article is to outline the architectural design and the conceptual framework for a Smart Maintenance Decision Support System (SMDSS) based on corporate data from a Fortune 500 company. Motivated by the rapidly transforming landscape for big data analytics and predictive maintenance decision making, we have created a system capable of providing end users with recommendations to improve asset lifecycles. Methodologically, a cost minimization algorithm is used to analyze a large industry service and warranty data sets and two analytical decision models were developed and applied to a case study for an electrical circuit breaker maintenance problem. Some of these techniques can be applied to other industries, such as jet engine maintenance, and can be expanded to others with implications for robust decision analysis. The SMDSS provides a predictive analytical model that can be applied in manufacturing and service based industries. Our findings and results show that existing solution algorithms and optimization models can be applied to large data sets to lay out executable decisions for managers.

Applying forgotten lessons in field reliability data analysis to performance-based support contracts

Abstract Assumptions used in field reliability data analysis may be seldom made explicit or questioned in practice, yet these assumptions affect how engineering managers develop metrics for use in long-term support contracts. To address this issue, this article describes a procedure to avoid the pitfalls in employing the results of field data analysis for repairable items. The procedure is implemented with the aid of a simplified example based on a real case study in defense avionics and is streamlined so that the computations can be replicated in other applications.