Internet of models of things for situational analysis and forecasting dependent failures

To advance from the internal approach to assessing effectiveness of a new system (based on a set of indicators that often are not sufficiently grounded) to the external, much more objective, approach, when this assessment is made based on the results of functioning a new system as part of a metasystem, it is proposed to supplement the emerging Internet of Things (IoT) with a new sector – the Internet of Models of Things (IoMoT), that is, to implement the transition from the IoT to the IoTaM (Internet of Things and Models). This will allow developers of new devices and systems to justify safety from inclusion in the existing environment by analyzing their relationships with previously specified components.

Pushing Failure Mode Stimulus to Overcome the Limitation/Boundaries of Soft Defect Localization Tools

Temperature dependent failures are some of the most challenging cases that will be encountered by the analyst. Soft Defect Localization (SDL) is a technique used to analyze such temperature-dependent, ‘soft defect’ failures [1]. There are many literatures that discuss this technique and its different applications [2-7]. Dynamic Analysis by Laser Stimulation (DALS) is one of the known SDL implementations [8-11]. However, there are cases where the failure is occurring at a temperature where the laser alone is not sufficient to effectively induce a change of device behavior. In these situations, the analyst needs to think out of the box by understanding how the device will react to external conditions and to make necessary adjustments in DALS settings. This paper will discuss three cases that presents different challenges such as performing DALS analysis where the failing temperature is too high for the laser to induce a change of behavior from ambient temperature, cold temperature failure, complex triggering (Serial Peripheral Interface, SPI), and using an internal signal as input for DALS analysis. The approach used for a successful DALS analysis of each case will be discussed in detail.

Role of Geomechanics in Identification of Possible Mechanisms for Non-productive Times NPT and Improving Drilling Operations in a Mature Field in Offshore Sarawak, Malaysia

This paper presents a case study on the role of geomechanics to identify possible failure mechanisms for non-productive time (NPT), avoid drilling risks and minimize costs in a field development drilling campaign, Offshore Sarawak Malaysia. Drilling optimization and reducing NPT for the drilling campaign was one of the key focus for maintaining the drilling time and costs. Drilling of moderately to highly deviated wells in this field has proven to be extremely challenging. Numerous lost-time incidents due to tight hole, stuck pipe, pack-off, casing held up were experienced, particularly when drilling through the shallow overburden shales and deeper reservoirs interbedded with shales and coals. Faced with continually increased NPTs, a geomechanical model was developed using regional offset wells to understand the mechanism of failures. A geomechanical model was developed to quantify the minimum recommended mud weights and optimize the wellbore trajectories. The outcome of this study was used as key input for casing and mud design. The in-situ stress state derived from field wide geomechanical model indicates the field is associated with a normal faulting stress regime, i.e., Shmin < SHmax < SV. The presence of relatively weak rocks means the field is potentially subject to stress-induced wellbore instability problems. However, observations of numerous time-dependent failures imply secondary influences must also be considered to arrive at possible remediation strategies. It was observed that the combination of weak rocks and numerous time-dependent failures using different types of mud system have contributed to wellbore stability problems. The wellbore stability is due to reactive shale, which is time sensitive as majority of the drilling problems are observed after drilling. The major contributor to the time-dependent deterioration process is mechanical and chemical imbalances between shale and drilling fluids compounded by large open-hole exposure area and contact time resulting in rising pore pressure caused by the invasion of drilling fluid into the formations, and then exacerbated by less-than-optimal drilling practices. This finding, together with improved geomechanical understanding of the field helped to evaluate the safe mud weight windows, formulate the mud designs and optimize drilling practices. All the planned wells were drilled successfully without any loss time incidents and non-productive time. This paper presents an integrated approach and workflow that combines the drilling data and formation response to identify the most likely causative mechanisms of the time-delayed wellbore instabilities in a mature field. This knowledge was then used to develop strategies for optimizing future drilling operations in the field.

Designing Control and Protection Systems with Regard to Integrated Functional Safety and Cybersecurity Aspects

This article addresses current problems of risk analysis and probabilistic modelling for functional safety management in the life cycle of safety-related systems. Two main stages in the lifecycle of these systems are distinguished, namely the design and operation. The risk analysis and probabilistic modelling differ in these stages in view of available knowledge and data. Due to the complexity and uncertainty involved, both qualitative and quantitative information can be useful in risk analysis and probabilistic modelling. Some methodological aspects of the functional safety assessment are outlined that include modelling of dependent failures or cybersecurity and verifying the safety integrity level (SIL) under uncertainty. It is illustrated how the assumptions in the process of risk analysis and probabilistic modelling influence results obtained and, therefore, potentially the decisions taken in functional safety management. Programmable control and safety systems play an important role in mitigating and controlling risks in the operation of hazardous installations. This paper presents ways to deal with safety hazards involving such systems to be considered in risk analysis and integrated functional safety and cybersecurity management.

Using NI-PXI Test Platform in Soft Defect Localization of Temperature Dependent Failures

Soft Defect Localization (SDL) method has been a common failure analysis technique used in fault isolation of temperature dependent failures, however proper signal conditioning and conversion of the monitored signal into a pass/fail signal are critical in acquiring an accurate defect location. This paper presents case studies where LabVIEW software using NI-PXI test platform was successfully implemented to effectively convert the failure mode into a pass/fail signal which provided a reliable SDL result.

Software Reliability Model with Dependent Failures and SPRT

Software reliability and quality are crucial in several fields. Related studies have focused on software reliability growth models (SRGMs). Herein, we propose a new SRGM that assumes interdependent software failures. We conduct experiments on real-world datasets to compare the goodness-of-fit of the proposed model with the results of previous nonhomogeneous Poisson process SRGMs using several evaluation criteria. In addition, we determine software reliability using Wald’s sequential probability ratio test (SPRT), which is more efficient than the classical hypothesis test (the latter requires substantially more data and time because the test is performed only after data collection is completed). The experimental results demonstrate the superiority of the proposed model and the effectiveness of the SPRT.

A diagnostic decision-making protocol combines a new-generation of serological assay and PCR to fully resolve ambiguity in COVID-19 diagnosis

The capacity to accurately diagnosis COVID-19 is essential for effective public health measures to manage the ongoing global pandemic, yet no presently available diagnostic technologies or clinical protocols can achieve full positive predictive value (PPV) and negative predictive value (NPV) performance. Two factors prevent accurate diagnosis: the failure of sampling methods (e.g., 40% false negatives from PCR testing of nasopharyngeal swabs) and sampling-time-dependent failures reflecting individual humoral responses of patients (e.g., serological testing outside of the sero-positive stage). Here, we report development of a diagnostic protocol that achieves full PPV and NPV based on a cohort of 500 confirmed COVID-19 cases, and present several discoveries about the sero-conversion dynamics throughout the disease course of COVID-19. The fundamental enabling technology for our study and diagnostic protocol-termed SANE, for Symptom (dpo)-Antibody-Nucleic acid-Epidemiological history-is our development of a peptide-protein hybrid microarray (PPHM) for COVID-19. The peptides comprising PPHMCOVID-19 were selected based on clinical sample data, and give our technology the unique capacity to monitor a patient's humoral response throughout the disease course. Among other assay-development related and clinically relevant findings, our use of PPHMCOVID-19 revealed that 5% of COVID-19 patients are from an "early sero-reversion" subpopulation, thus explaining many of the mis-diagnoses we found in our comparative testing using PCR, CLIA, and PPHMCOVID-19. Accordingly, the full SANE protocol incorporates orthogonal technologies to account for these patient variations, and successfully overcomes both the sampling method and sampling time limitations that have previously prevented doctors from achieving unambiguous, accurate diagnosis of COVID-19