Real-time Data Interpretation Research Articles

Development of Monitoring and Forecasting Technology Energy Efficiency of Well Drilling Using Mechanical Specific Energy

This article is devoted to the development of technology for improving the efficiency of directional well drilling by predicting and adjusting the system of static and dynamic components of the actual weight on the bit, based on the real-time data interpretation from telemetry sensors of the bottom hole assembly (BHA). Studies of the petrophysical and geomechanical properties of rock samples were carried out. Based on fourth strength theory and the Palmgren–Miner fatigue stress theory, the mathematical model for prediction of effective distribution of mechanical specific energy, using machine learning methods while drilling, was developed. An algorithm was set for evaluation and estimation of effective destruction of rock by comparing petrophysical data in the well section and predicting the shock impulse of the bit. Based on the theory provided, it is assumed that the given shock impulse is an actual representation of an excessive energy, conveyed to BHA. This excessive energy was quantitively determined and expressed as an adjusting coefficient for optimal weight on bit. The developed mathematical and predictive model helps to identify the presence of ineffective rock destruction and adjust drilling regime accordingly. Several well drilling datasets from the North Sea were analyzed. The effectiveness of the developed mathematical model and algorithms was confirmed by testing well drilling data.

Fracture Hits and Hydraulic-Fracture Geometry Characterization Using Low-Frequency Distributed Acoustic Sensing Strain Data

The propagation process and geometry of hydraulic fractures depend on complex interactions among the induced fractures and the pre-existing rock fabric, the heterogeneous rock properties, and the stress state. Accurate characterization of the resulting complex hydraulic-fracture geometry remains challenging. Fiber-optic-based distributed acoustic sensing (DAS) measurements have been used for monitoring hydraulic fracturing in adjacent treatment wells. DAS requires an optical fiber attached to the wellbore to transmit the laser energy into the reservoir. Each section of the fiber scatters a small portion of the laser energy back to a surface sensing unit, which uses interferometry techniques to determine strain changes along with the fiber. DAS data in offset wells fall in the low-frequency bands, which has been proven to be a powerful attribute for the characterization of the geometry of hydraulic fractures. Numerous recently published field examples demonstrate the potential of low-frequency DAS (LF-DAS) data for the detailed characterization of the hydraulic fracture geometry. Understanding the fracture-induced rock deformation associated with LF-DAS signals would be beneficial for the better interpretation of real-time data. However, interpretation of LF-DAS measurement is challenging due to the complexity of the subsurface conditions, in addition to potential unanticipated completion issues such as perforation failure, stage isolation failure, etc. All current research efforts focus on the qualitative interpretation of field data.In this study, we quantified the hydraulic fracture propagation process and described the fracture geometry by developing a geomechanical forward model and a Green’s function-based inversion model for the LF-DAS data interpretation, substantially enhancing the value of the LF-DAS data in the process. The work has a significant transformative potential, involving a tool package with developed forward and inversion models that can provide crucial insights for the optimization of hydraulic-fracturing treatments and reservoir development. Methodology The tool package can be used directly in the field to interpret LF-DAS data and monitor hydraulic fracture propagation. Raw data from the field measurement can be automatically processed. The geomechanics forward model we developed can quantify and analyze the strain-rate response from the LF-DAS measurements based on the 3D displacement discontinuity method. Fracture hits are detected by calculating three 1D features along the channel (location) axis, i.e., the maximum strain rate, the summation of strain rates, and the summation of strain-rate amplitudes. Channels with fracture hits usually exhibit significant peak values of these three features. We proposed general guide-lines for fracture-hit detection based on the quantitative analysis of strain/strain-rate responses during the multistage fracturing treatment. The details of the forward model can be found in Liu et al. (SPE 202482, 204457, AMRA-2020-1426). Additionally, we developed a novel Green’s function-based inversion model to qualify fracture width and height based on the determined fracture hits. The strain field that is estimated from the integration of the strain rates measured by the LF-DAS data along the offset monitoring well is related to the fracture widths through a geomechanics Green’s function. The resulting linear system of equations is solved by the least-square method. Details can be found in Liu et al. (SPE 204158, 205379, 204225).

Collaboration and Optimization Processes Contribute to Ultra-ERD Offshore Well Success

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 202251, “Transforming the Mindset To Drill Ultra-ERD Wells With High Tortuosity,” by Barry Goodin, SPE, Duane Selman, and Andy Wroth, Vermilion Oil and Gas, et al., prepared for the 2020 SPE Asia Pacific Oil and Gas Conference and Exhibition, originally scheduled to be held in Perth, Australia, 20–22 October. The paper has not been peer reviewed. The complete paper describes the extensive integrated engineering collaboration and optimization process that allowed an operator to push the drilling and completion envelope to drill a pair of complex, ultra-extended-reach-drilling (ERD) wells in the mature Wandoo field in the Carnarvon Basin offshore Western Australia. The shallow reservoir depth, extreme ERD profile, and high tortuosity requirement for the wells posed significant challenges. These were overcome with extensive planning; integrated engineering designs; application of new technology; good-quality, real-time data interpretation; and strong execution support from both rig site and town. Introduction The Wandoo field, in 56 m of water off-shore Western Australia, was discovered in 1991 and subsequently developed and placed on production in 1993. The shallow unconsolidated sandstone reservoir consists of a heavily biodegraded oil column overlain by a gas cap and supported by a strong aquifer drive. Field infrastructure consists of a 15-well-slot manned production facility, Wandoo B, and a five-slot monopod, Wandoo A, which is tied back to Wandoo B by subsea in-field pipelines. In late 2018, the operator planned and executed a two-well drilling campaign consisting of two complex, ultra-ERD wells, Wandoo B15 and B16. Both wells were planned to be batch drilled for the top hole and intermediate hole sections, with the production hole sections to be drilled and completed sequentially. The primary objective for the B15 well was to recover unswept oil along the western flank of the field and track the well along the main Wandoo fault to the north to assess the structure and reserves from the northern tip of the field. The B16 well objective was to access unswept reserves through the center and down to the south of the field, essentially twinning the B11ST1 well, another ERD well drilled on an earlier campaign, to its eastern flank. To maximize recovery, both wells needed to be placed approximately 1 m below the top of the reservoir, except where overlain by the gas cap, in which case the wells were to be placed approximately 2 m below the gas/oil contact to avoid gas coning. Drilling Challenges and Solutions The first half of the complete paper presents a detailed discussion of the drilling challenges and solutions, illustrated with schematics, maps, charts, and graphs. Both Wells B15 and B16 were classified as ultra-ERD wells because the shallow true vertical depth (TVD) of the reservoir resulted in extreme stepout ratios and required highly complex well paths to access the remaining reserves. The complete paper lists various specific drilling- and systems-related challenges.

Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review.

Modern Intensive Care Units (ICUs) provide continuous monitoring of critically ill patients susceptible to many complications affecting morbidity and mortality. ICU settings require a high staff-to-patient ratio and generates a sheer volume of data. For clinicians, the real-time interpretation of data and decision-making is a challenging task. Machine Learning (ML) techniques in ICUs are making headway in the early detection of high-risk events due to increased processing power and freely available datasets such as the Medical Information Mart for Intensive Care (MIMIC). We conducted a systematic literature review to evaluate the effectiveness of applying ML in the ICU settings using the MIMIC dataset. A total of 322 articles were reviewed and a quantitative descriptive analysis was performed on 61 qualified articles that applied ML techniques in ICU settings using MIMIC data. We assembled the qualified articles to provide insights into the areas of application, clinical variables used, and treatment outcomes that can pave the way for further adoption of this promising technology and possible use in routine clinical decision-making. The lessons learned from our review can provide guidance to researchers on application of ML techniques to increase their rate of adoption in healthcare.

Developing an electronic geriatric assessment to improve care of older adults with cancer receiving radiotherapy.

Older adults make up a substantial proportion of patients diagnosed with cancer. Gaps in evidence of care for older adults with cancer leads to treatment heterogeneity and poor outcomes. Medical and Surgical Oncology clinics throughout the world are increasingly using Geriatric Assessment (GA) based approaches to treatment that are beginning to improve care through treatment decision-making communication, health-related quality of life outcomes, and reducing chemotherapy toxicities. Yet, GA based approaches are not often used in radiation oncology clinics. This manuscript aims to describe the ongoing development of an electronic patient-reported GA with real-time data interpretation and recommendation delivery to help increase the use of a personalized GA based approach to the care of older adults within radiation oncology clinics. Future studies demonstrating the utility and benefit of GA based approaches to help older adults undergoing radiotherapy for their cancers are still sorely needed.

Caution on real-time data interpretation of aerosol generation during phacoemulsification

We commend Lee et al. on their attempt using a real-time particle monitor to detect possible aerosol generation from phacoemulsification.1 Such devices can be powerful tools to assist with bioaerosol exposure assessment, but the generated real-time data should only be used as surrogate data and interpreted with great caution. The very low total cumulative particle counts from very short-grab sampling collection time of 21-second windows might not have sufficient signal-to-noise ratio for numeric quantification. The typical estimated limit of detection—the value above which it is considered truly detectable—and limit of quantification—value above which it is considered quantifiable—are 3× and 10× SD, respectively.2 Because the short sampling duration, particle counts were near or below limit of detection, thus offering low confidence to the reported values. Moreover, because the measured mean particle counts for all experiments were below limit of quantification, the reported numbers could be neither accurately quantified nor compared. To improve accuracy, a longer sampling time of several minutes is recommended. To rule out buildup of bioaerosol from multiple operations, full-shift (8 hours) personal monitoring at the breathing zones of the operating personnel is also recommended. The volume of the room along with air change rate should be stated to help standardize equivalent observation in other room settings. The number of occupants in the room, temperature, relative humidity, and so on should also be noted to ensure that the observed data would not be affected by interday variations. SARS-CoV-2 virus particle sizes were reported to exist as a continuum from less than 0.3 to more than 5 μm.3 An aerodynamic characterization study at Wuhan Hospital suggested virus particles found in the personal protective equipment doffing room were predominantly small particles between 0.25 and 0.5 μm.4 Therefore, it is crucial for any particle size study to accurately capture bioaerosol at all size ranges including size less than 0.3 μm. The data from Lee et al., suggesting no elevation of bioaerosol observed from less than 0.3 to 10 μm, was based on the TROTEC PC200 (Trotec GmbH) optical particle counter, which has a stated detection range between 0.3 and 10 μm diameter. Although it is claimed that the monitor can quantify particles below 0.3 μm, the accuracy of particle detection of less than 0.3 μm is highly questionable. As per instrument specification, the counting efficiency of 0.3 μm particle was reportedly only 50% at best.5 It is, therefore, unsure whether the monitor can detect particles smaller than 0.3 μm. If so, what is the counting efficiency for these smaller particles? Thus, the low count of particles at less than 0.3 μm range in the study might be insufficient to rule out fine bioaerosol generation during simulated phacoemulsification. Recommendations to lower personal protective equipment might be premature, especially when the major part of bioaerosol production in the operating environment is expected to be generated from the respiratory tracts of patients and staff, which is not captured in this simulation study.

Visible Light Communication and Augmented Reality for Underground Positioning System

Visible Light Communication (VLC) technology allows wireless data transmission piggybacked by illumination. Highly accurate and reliable systems based on VLC, as Indoor Positioning System (IPS) have been already developed by academics and specialized companies. Underground Positioning System (UPS) addressed here is embedded into the protection equipment, compulsory to be used underground, being therefore important to workers in potential dangerous spaces since fast data communication and real-time data interpretation is therefore possible. This paper presents the VLC technology implemented in mining underground specific environment for an accurate positioning and fast data communication for underground navigation with the main aim of developing a real time warning and alarming system based on Augmented Reality (AR) and Neural Networks (NNs) principles.

Representing Operational Modes for Situation Awareness

Operating complex plants is an increasingly demanding task for human operators. Diagnosis of and reaction to on-line events requires the interpretation of real time data. Vast amounts of sensor data as well as operational knowledge about the state and design of the plant are necessary to deduct reasonable reactions to abnormal situations. Intelligent computational support tools can make the operator’s task easier, but they require knowledge about the overall system in form of some model.While tools used for fault-tolerant control design based on physical principles and relations are valuable tools for designing robust systems, the models become too complex when considering the interactions on a plant-wide level. The alarm systems meant to support human operators in the diagnosis of the plant-wide situation on the other hand fail regularly in situations where these interactions of systems lead to many related alarms overloading the operator with alarm floods. Functional modelling can provide a middle way to reduce the complexity of plant-wide models by abstracting from physical details to more general functions and behaviours. Based on functional models the propagation of failures through the interconnected systems can be inferred and alarm floods can potentially be reduced to their root-cause. However, the desired behaviour of a complex system changes due to operating procedures that require more than one physical and functional configuration. In this paper a consistent representation of possible configurations is deduced from the analysis of an exemplary start-up procedure by functional models.The proposed interpretation of the modelling concepts simplifies the functional modelling of distinct modes. The analysis further reveals relevant links between the quantitative sensor data and the qualitative perspective of the diagnostics tool based on functional models. This will form the basis for the ongoing development of a novel real-time diagnostics system based on the on-line adaptation of the underlying MFM model.

Data Fusion for Real-time Multimodal Emotion Recognition through Webcams and Microphones in E-Learning

This article describes the validation study of our software that uses combined webcam and microphone data for real-time, continuous, unobtrusive emotion recognition as part of our FILTWAM framework. FILTWAM aims at deploying a real-time multimodal emotion recognition method for providing more adequate feedback to the learners through an online communication skills training. Herein, timely feedback is needed that reflects on the intended emotions they show and which is also useful to increase learners’ awareness of their own behavior. At least, a reliable and valid software interpretation of performed face and voice emotions is needed to warrant such adequate feedback. This validation study therefore calibrates our software. The study uses a multimodal fusion method. Twelve test persons performed computer-based tasks in which they were asked to mimic specific facial and vocal emotions. All test persons’ behavior was recorded on video and two raters independently scored the showed emotions, which were contrasted with the software recognition outcomes. A hybrid method for multimodal fusion of our multimodal software shows accuracy between 96.1% and 98.6% for the best-chosen WEKA classifiers over predicted emotions. The software fulfils its requirements of real-time data interpretation and reliable results.

Abstract PR14: Harnessing the power of big data to advance pediatric cancer care

Abstract DNA and RNA sequencing is increasingly applied in clinical trials to find new therapeutic leads for children with incurable cancers. However, compared to similar studies in adults, these trials have yielded fewer new treatment options, because pediatric and adult malignancies are distinct biologically, and far less data are available on the genomics of pediatric tumors. Initiatives such as the National Cancer Institute's (NCI) Therapeutically Applicable Research to Generate Effective Treatments (TARGET) project, and the Medulloblastoma Advanced Genomics International Consortium (MAGIC) have generated large cohorts through collaboration, however they are limited to specific diseases. In addition, there is no mechanism to integrate these data with genomic data collected in prospective precision medicine trials. The failure to share data has meant that available genomic information is not being utilized to its full potential, resulting in missed therapeutic opportunities. The UC Santa Cruz Treehouse Childhood Cancer Project integrates genomic data generated by pediatric research studies, such as TARGET, MAGIC, the Pediatric Cancer Genome Project, and the Childhood Brain Tumor Tissue Consortium with genomic data generated by clinical trials. Treehouse also makes it possible to compare these data with large adult datasets, including The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC), and Stand Up to Cancer (SU2C). Together, these datasets provide access to the genomic information from over 15,000 individual tumors that can be used as context for real-time data interpretation from individual patients in clinical genomics trials. This work presents a case report that illustrates how integrating multiple pediatric and adult gene expression datasets with similar data collected from patients in a prospective clinical trial can provide new clinical leads for children with difficult-to-treat cancers. The data are integrated and analyzed using TumorMap, an unsupervised clustering and visualization approach that has been shown to reveal new clinical insights into adult cancers as part of the TCGA Pan-Cancer effort. The expression of individual genes and their relationship with phenotypic features in the combined cohort can be visualized using the UCSC Xena Bowser. New clinical leads can be recommended for individual patients based on the similarity of their molecular profiles to those of other cancers with available treatment options, as shown in the TumorMap. We propose to extend our case study to a framework of how genomic datasets collected from adult and pediatric patients in research and clinical settings can be used to inform the care of pediatric patients prospectively. This framework will provide new hope for children with difficult to treat cancers so that no therapeutic option is overlooked in the fight to save their lives. Citation Format: Olena Morozova, Yulia Newton, Melissa Cline, Stephen Yip, Arjun Rao, Josh Stuart, Ted Goldstein, Sofie Salama, Rebecca Deyell, S. Rod Rassekh, David Haussler. Harnessing the power of big data to advance pediatric cancer care. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Pediatric Cancer Research: From Mechanisms and Models to Treatment and Survivorship; 2015 Nov 9-12; Fort Lauderdale, FL. Philadelphia (PA): AACR; Cancer Res 2016;76(5 Suppl):Abstract nr PR14.

Real-Time Computer Analytical Application in Pressure Transient Analysis of Petroleum Reservoirs

Today with certainty, the petroleum industry is fostering sanguinely the fields’ development programs for the optimization of reservoir characterization through worth-full appliances of computer analysis techniques. The time element is of prime importance for optimistic petroleum development projects. Therefore, the frontline of “Real-time Analysis” is added into the applications of computer solving techniques for achieving and sketching up the real-time cost effectiveness in analyzing field development programs. It focuses on the phases of real-time well test data acquisition system, real-time secure access to the well test data either on field or in office and real-time data interpretation unit. This interface will yield the productive results for the field of reservoir’s pressure transient analysis and wells’ systems analysis by following the up-to-date preferred, accurate and effective well test analytical principles with modern real-time computer applications and techniques. It also lays emphasis for the comfort and reliability of data in creating best interpersonal working modes within a reputable and esteemed petroleum development organization.

Improving Real-Time Image-Data Quality With A Telemetry Model

Summary The introduction of new measurements constantly increases the amount of real-time data from logging-while-drilling (LWD) services, providing better information for informed decision making. This is matched by telemetry technologies that provide data rates from bits per second to megabits per second. During a deployment, the bandwidth is shared between several services requiring different data rates, leaving the user with the task of aligning telemetry technology and shared data rates to the application. High-volume services often allow for data compression. Lossless or near-lossless compression provides the best data quality but requires large bandwidth. Lossy compression reduces bandwidth usage dramatically, but may result in degraded data quality. Thus, telemetry planning must be tied closely to service objectives and requires expert advice. We present a planning approach in which we model the realtime-data compression of an LWD high-resolution imaging tool that uses a flexible compression algorithm. This system allows lossy compression for telemetry rates as low as 1 bps, while at the same time delivering memory-data quality using high-speed-telemetry technology. The compression rate determines the image resolution and must be adjusted to the detail required in the transmitted image. Using offset-well data or simulated memory data, the expected real-time image is simulated and its level of detail quantified. For instance, the presence and appearance of fractures in real time can be forecasted. This enables us to optimize telemetry usage and define compression and drilling parameters for a successful deployment of the imaging service. This paper will outline the methods and benchmark the technology of the imaging tool, using real-time data measured during field deployments. We will then use parameter variations to show how increasing net bandwidth improves the amount of detail in the images, and how these are used for different levels of realtime-data interpretation.

Cardiac Ultrasound Imaging in Heart Failure: Recent Advances

Several new imaging modalities are being utilized in the management of heart failure. Echocardiography and speckle tracking imaging offer clinician the benefits of easy accessibility, real time data interpretation and objective quantification of heart function. Accordingly, this article reviews the current evidence base related to the use of echocardiography and other advanced ultrasonography techniques in heart failure, and discusses applications as well as limitations of these emerging technologies. The role of cardiac resynchronization therapy (CRT) and implications of the PROSPECT (Predictors of Response to CRT) trial in management of heart failure are also reviewed. The article concludes with a discussion about the evolving role of echocardiography in diagnosis and management of subclinical heart disease, so that preventive strategies may be devised.

Implementation of a Real-Time Drilling-Problem Diagnostic Program

This article, written by Assistant Technology Editor Karen Bybee, contains highlights of paper OTC 20652, ’Field Implementation of a Real Time Drilling Problem Diagnostic for Deepwater Exploratory Wells,’ by Roni Gandelman, Alex Waldmann, Andre L. Martins, SPE, Gleber Teixeira, and Atila Aragao, Petrobras, and Mauricio Rezende and Alexandre de Mari, ESSS Scientific Software, originally prepared for the 2010 Offshore Technology Conference, Houston, 3-6 May. The paper has not been peer reviewed. The full-length paper details the field-implementation experience of an inhouse-developed system for drilling-problems detection and identification. Starting from real-time drilling data, the system was designed to investigate reasons for deviations in important measured variables (e.g., downhole and pumping pressures, temperatures, and torque and drag) during drilling operations. Based on a hybrid approach, including multiphase-hydraulics and torque-and-drag modeling, case-history matching, and knowledge of specialists, the system should identify undesirable events. Introduction Deep- and ultradeepwater exploratory drilling is a very risky and costly operation in which every effort to guarantee performance and operational safety is welcome. Low fracture gradients, abnormal pressures, losses, and unstable formations are among the items that make well design complex and well construction a continuous challenge. Several exploratory prospects currently ongoing in Petrobras face very narrow operational windows, and well construction will demand unconventional techniques. The anticipation and remediation of potential hole problems is an ultimate goal of most real-time measurement devices installed on drilling rigs. Much effort has been spent in downhole sensors and data-transmission systems, but there is a common sense in the industry that very little is available in real-time data interpretation. Pressure-while-drilling (PWD) data, for instance, is used in a subjective manner, and interpretation depends to a great extent on the philosophy of the operator. Some preliminary implementation efforts at rigsites and at onshore decision-support centers have demonstrated the potential of drilling-data-interpretation systems in reducing operational costs and risks. In 2006, as a strategy to preserve the knowledge retained by experienced professionals and to achieve effective gains from real-time data, a development project was started to generate a drilling-interpretation system. Starting from a long-term experience in developing steady-state and transient-hydraulics models, an initial development of a real-time hydraulics model for vertical wells to guarantee pressures inside the operational window was proposed. Soon, the team understood that a reliable analysis would have to go beyond hydraulics. A multidisciplinary team, including software developers, data-communication professionals, artificial-intelligence specialists, and petroleum engineers, was established to define the requirements and objectives for the system. The idea was to make full use of measurement-while-drilling/PWD and mud-logging data for analyzing the drilling operation. Logging-while-drilling data would be considered in a further step because the company already had a team for real-time geopressure analysis.

A susceptible-infected model of early detection of respiratory infection outbreaks on a background of influenza

The threat of biological warfare and the emergence of new infectious agents spreading at a global scale have highlighted the need for major enhancements to the public health infrastructure. Early detection of epidemics of infectious diseases requires both real-time data and real-time interpretation of data. Despite moderate advancements in data acquisition, the state of the practice for real-time analysis of data remains inadequate. We present a nonlinear mathematical framework for modeling the transient dynamics of influenza, applied to historical data sets of patients with influenza-like illness. We estimate the vital time-varying epidemiological parameters of infections from historical data, representing normal epidemiological trends. We then introduce simulated outbreaks of different shapes and magnitudes into the historical data, and estimate the parameters representing the infection rates of anomalous deviations from normal trends. Finally, a dynamic threshold-based detection algorithm is devised to assess the timeliness and sensitivity of detecting the irregularities in the data, under a fixed low false-positive rate. We find that the detection algorithm can identify such designated abnormalities in the data with high sensitivity with specificity held at 97%, but more importantly, early during an outbreak. The proposed methodology can be applied to a broad range of influenza-like infectious diseases, whether naturally occurring or a result of bioterrorism, and thus can be an integral component of a real-time surveillance system.

Future Directions in Instrumentation, Control and Automation in the Water and Wastewater Industry

The need to optimize treatment plant performance and to meet increasingly stringent effluent criteria are two key factors affecting future development of instrumentation, control and automation (ICA) applications in the water and wastewater industry. Two case studies are presented which highlight the need for dynamic modelling and simulation software to assist operations staff in developing effective instrumentation control strategies, and to provide a training environment for the evaluation of such strategies. One of the limiting factors to date in realizing the potential benefits of ICA has been the inability to adequately interpret the large number of existing instrumentation inputs available at treatment facilities. The number of inputs can exceed the number of control loops by up to three orders of magnitude. The integration of dynamic modelling and expert system software is seen to facilitate the interpretation of real-time data, allowing both quantitative (instrumented) and qualitative (operator input) information to be integrated for process control. Improvements in sensor reliability and performance, and the development of biological monitoring sensors and control algorithms are also discussed.

An abductive expert system for interpretation of real-time data (of substations)

DINT, a data interpretation expert system which can provide an explanation of the real-time operating state of a substation to its operators, is described. DINT is based on a generalized version of the set covering model for diagnostic problem solving. This abductive reasoning model is an object-based, frame-like knowledge representation paradigm. Test cases illustrate the application of the model to the real-time operation of a substation. >

Utilization of quality data for cost cutting

This paper describes an uncommon approach to Quality Assurance based on the rapid interpretation of Real Time Quality Data for achieving optimized cost cutting. Visibility is established of status and trends in the factors that determine the Total Life Cycle Costs and the Cost of Ownership. Rapid interpretation of Real Time data gleans the intelligence inherent in the time sequence of failure events in a cumulative stress environment. Planned screening stresses are designed to degrade incipient failure flaws to a detectable threshold level and to establish a stress profile during the factory operation that provides a deterministic reference for the interpretation of factory data. Screening theory basic to the approach is given and Cost Cutting versus the Wooden Round Concept is discussed.