Workflow completion patterns

The most common correctness requirement for a (business) workflow is the completion requirement, imposing that, in some form, every case-instance of the workflow reaches its final state. In this paper, we define three workflow completion patterns, called the mandatory, optional and possible completion. These patterns are formalized in terms of the temporal logic CTL*, to remove ambiguities, allow for easy comparison, and have direct applicability. In contrast to the existing methods, we do not look at the control flow in isolation but include some data information as well. In this way the analysis remains tractable but gains precision. Together with our previous work on data-flow (anti-)patterns, this paper is a significant step towards a unifying framework for complete workflow verification, using the well-developed, stable, adaptable, and effective model-checking approach

The Application of Downhole Vibration Factor in Drilling Tool Reliability Big Data Analytics - A Review

In the challenging downhole environment, drilling tools are normally subject to high temperature, severe vibration, and other harsh operation conditions. The drilling activities generate massive field data, namely field reliability big data (FRBD), which includes downhole operation, environment, failure, degradation, and dynamic data. Field reliability big data has large size, high variety, and extreme complexity. FRBD presents abundant opportunities and great challenges for drilling tool reliability analytics. Consequently, as one of the key factors to affect drilling tool reliability, the downhole vibration factor plays an essential role in the reliability analytics based on FRBD. This paper reviews the important parameters of downhole drilling operations, examines the mode, physical and reliability impact of downhole vibration, and presents the features of reliability big data analytics. Specifically, this paper explores the application of vibration factor in reliability big data analytics covering tool lifetime/failure prediction, prognostics/diagnostics, condition monitoring (CM), and maintenance planning and optimization. Furthermore, the authors highlight the future research about how to better apply the downhole vibration factor in reliability big data analytics to further improve tool reliability and optimize maintenance planning

Condition-based maintenance—an extensive literature review

This paper presents an extensive literature review on the field of condition-based maintenance (CBM). The paper encompasses over 4000 contributions, analysed through bibliometric indicators and meta-analysis techniques. The review adopts Factor Analysis as a dimensionality reduction, concerning the metric of the co-citations of the papers. Four main research areas have been identified, able to delineate the research field synthetically, from theoretical foundations of CBM; (i) towards more specific implementation strategies (ii) and then specifically focusing on operational aspects related to (iii) inspection and replacement and (iv) prognosis. The data-driven bibliometric results have been combined with an interpretative research to extract both core and detailed concepts related to CBM. This combined analysis allows a critical reflection on the field and the extraction of potential future research directions

Model-Driven Performance Analysis of Reconfigurable Conveyor Systems Used in Material Handling Applications

Abstract—Reconfigurable conveyors are increasingly being adopted in multiple industrial sectors for their immense flexibility in adapting to new products and product lines. Before modifying the layout of the conveyor system for the new product line, however, engineers and layout planners must be able to answer many questions about the system, such as maximum sustainable rate of flow of goods, prioritization among goods, and tolerances to failures. Any analysis capability that provides answers to these questions must account for both the physical and cyber artifacts of the reconfigurable system all at once. Moreover, the same system should enable the stakeholders to seamlessly change the layouts and be able to analyze the pros and cons of the layouts. This paper addresses these challenges by presenting a model-driven analysis tool that provides three important capa-bilities. First, a domain-specific modeling language provides the stakeholders with intuitive artifacts to model conveyor layouts. Second, an analysis engine embedded within the model-driven tool provides an accurate simulation of the modeled conveyor system accounting for both the physical and cyber issues. Third, generative capabilities within the tool help to automate the analysis process. The merits of our model-driven analysis tool are evaluated in the context of an example conveyor topology

Data transmission reduction in wireless sensor network for spatial event detection

Wireless sensor networks have found many applications in detecting events such as security threats, natural hazards, or technical malfunctions. An essential requirement for event detection systems is the long lifetime of battery-powered sensor nodes. This paper introduces a new method for prolonging the wireless sensor network’s lifetime by reducing data transmissions between neighboring sensor nodes that cooperate in event detection. The proposed method allows sensor nodes to decide whether they need to exchange sensor readings for correctly detecting events. The sensor node takes into account the detection algorithm and verifies whether its current sensor readings can impact the event detection performed by another node. The data are transmitted only when they are found to be necessary for event detection. The proposed method was implemented in a wireless sensor network to detect the instability of cargo boxes during transportation. Experimental evaluation confirmed that the proposed method significantly extends the network lifetime and ensures the accurate detection of events. It was also shown that the introduced method is more effective in reducing data transmissions than the state-of-the-art event-triggered transmission and dual prediction algorithms

Measurement and Evaluation for Prognostics and Health Management (PHM) for Manufacturing Operations – Summary of an Interactive Workshop Highlighting PHM Trends

Personnel from the National Institute of Standards and Technology (NIST) organized and led a Measurement and Evaluation for Prognostics and Health Management for Manufacturing Operations (ME4PHM) workshop at the 2019 Annual Conference of the Prognostics and Health Management Society held on September 23rd, 2019 in Scottsdale, Arizona. This event featured panel presentations and discussions from industry, government, and academic participants who are focused in advancing monitoring, diagnostic, and prognostic (collectively known as prognostic and health management (PHM)) capabilities within manufacturing operations. The participants represented a diverse cross-section of technology developers, integrators, end-users/manufacturers (from small to large), and researchers. These contributors discussed 1) what works well, 2) common challenges that need to be addressed, 3) where the community’s priorities should be focused, and 4) how PHM technological adoption can be sped in a cost-effective manner. This report summarizes the workshop and offers lessons learned regarding the current state of PHM. Based upon the discussions, recommended next steps to advance this technological domain are also presented

Performance evaluation of warehouses with automated storage and retrieval technologies.

In this dissertation, we study the performance evaluation of two automated warehouse material handling (MH) technologies - automated storage/retrieval system (AS/RS) and autonomous vehicle storage/retrieval system (AVS/RS). AS/RS is a traditional automated warehouse MH technology and has been used for more than five decades. AVS/RS is a relatively new automated warehouse MH technology and an alternative to AS/RS. There are two possible configurations of AVS/RS: AVS/RS with tier-captive vehicles and AVS/RS with tier-to-tier vehicles. We model the AS/RS and both configurations of the AVS/RS as queueing networks. We analyze and develop approximate algorithms for these network models and use them to estimate performance of the two automated warehouse MH technologies. Chapter 2 contains two parts. The first part is a brief review of existing papers about AS/RS and AVS/RS. The second part is a methodological review of queueing network theory, which serves as a building block for our study. In Chapter 3, we model AS/RSs and AVS/RSs with tier-captive vehicles as open queueing networks (OQNs). We show how to analyze OQNs and estimate related performance measures. We then apply an existing OQN analyzer to compare the two MH technologies and answer various design questions. In Chapter 4 and Chapter 5, we present some efficient algorithms to solve SOQN. We show how to model AVS/RSs with tier-to-tier vehicles as SOQNs and evaluate performance of these designs in Chapter 6. AVS/RS is a relatively new automated warehouse design technology. Hence, there are few efficient analytical tools to evaluate performance measures of this technology. We developed some efficient algorithms based on SOQN to quickly and effectively evaluate performance of AVS/RS. Additionally, we present a tool that helps a warehouse designer during the concepting stage to determine the type of MH technology to use, analyze numerous alternate warehouse configurations and select one of these for final implementation

Prädiktion einer langfristigen Fahrzeugzustandsänderung anhand virtueller datengetriebener Sensormodelle

Die immer weiterwachsende Digitalisierung in der Automobilindustrie ermöglicht eine vermehrte Nutzung und Analyse von Fahrzeug(flotten)daten. Die Nutzung dieser Flottendaten verspricht ein hohes Wertschöpfungspotenzial für zukünftige Mehrwertdienste. Dem Kunden können frühzeitig umfangreiche prädiktive Wartungs- und Reparaturinformationen mit Hilfe von datengetriebenen Analysemethoden bereitgestellt werden. In dieser Arbeit wird eine langfristige Fahrzeugzustandsänderung anhand virtueller datengetriebener Sensormodelle untersucht. Als Grundlage dafür werden dynamische CAN-Daten von internen Fahrzeugflotten verwendet. Im weiteren Verlauf wird ein Konzept entworfen, welches die Schritte der Datenvorverarbeitung und des Data-Minings in Anlehnung an den Prozess der Knowledge Discovery in Databases (KDD) konkretisiert. Mit Hilfe geeigneter Vorverarbeitungen wie z.B. Clusterverfahren und Merkmalsextraktionen kann die Menge der Eingangsdaten reduziert werden. Im Rahmen dieser Vorverarbeitung werden die unterschiedlichen Signale unüberwacht gruppiert. Aus Sequenzen werden statistische Merkmale extrahiert und zur weiteren Verarbeitung genutzt. Unter Anwendung von Regressionsmethoden ist eine Extraktion relevanter Muster und Regeln aus den Daten möglich. Anhand eines konkreten Beispiels aus der Automobilindustrie wird dieses Vorgehen validiert. Diese Arbeit kann dazu beitragen den steigenden Durchsatz digitaler Daten gezielt zu reduzieren. Es wird gezeigt, dass durch die Verwendung geeigneter Methoden des maschinellen Lernens die Eingangsdatenmenge um ein Vielfaches reduziert und gezielt für (Alterungs-) Vorhersagen genutzt werden kann.The digitization in the automotive industry enables analysis of vehicle (fleet) data. The use of this fleet data for future value-added services promises high value creation potential. Furthermore, the customer can be provided with extensive predictive maintenance and repair information at an early stage using data-driven analysis methods. In this work, a long-term vehicle state change is investigated using virtual data-driven sensor models. Dynamic CAN data from internal vehicle fleets are used as a basis for this. In the further course, a concept will be designed that specifies the steps of data preprocessing and data mining based on the process of knowledge discovery in databases (KDD). The amount of input data can be reduced with the help of suitable preprocessing such as cluster methods and feature extraction. As part of this preprocessing, the different signals are grouped unsupervised. Statistical features are extracted from sequences and used for further processing. Relevant patterns and rules can be extracted from the data using regression methods. This procedure is validated using a concrete example from the automotive industry. This work can help to reduce the increasing throughput of digital data in a targeted manner. It is shown that by using suitable methods of machine learning, the amount of input data can be reduced many times over and used specifically for (aging) predictions