IT RISK IDENTIFICATION AND ASSESSMENT METHODOLOGY

There are numerous methods for risk identification and risk assessment phases. Which for risk identification includes historical and systematic approach and inductive or theoretical analysis. One of the main reasons why risk identification is very helpful is that it provides justification in many cases for any large IT investment and other large undertakings. Without it organization probably wouldn’t be able to come to conclusion. Also in this phase business recognize the threats, vulnerabilities, and assets associated with its IT systems. Together with risk assessment phase risk management specialist is responsible for determining asset value, what's the value of the asset business is protecting, and risk acceptance level. Risk assessment on the other hand examines impact or consequence, as well as examines and evaluates the likelihood or probability of that adverse event happening. Risk assessment includes methods like Bayesian analysis, Bow Tie Analysis, brainstorming or structured interviews, business impact analysis, cause and consequence, cause-and-effect analysis, Delphi method, event tree analysis, fault tree analysis, hazard analysis, hazard and operational studies, and finally structured what if technique or SWIFT process. Risk assessment has two distinctive assessment types- quantitative and qualitative assessment. Quantitative assessment tries to put a monetary value on all risks. Qualitative assessment on the other hand rather look at it from a range of values like low, medium, high. The results of these phases are going to be documented in the risk assessment report and reported to senior management

Data-based fault detection in chemical processes: Managing records with operator intervention and uncertain labels

Developing data-driven fault detection systems for chemical plants requires managing uncertain data labels and dynamic attributes due to operator-process interactions. Mislabeled data is a known problem in computer science that has received scarce attention from the process systems community. This work introduces and examines the effects of operator actions in records and labels, and the consequences in the development of detection models. Using a state space model, this work proposes an iterative relabeling scheme for retraining classifiers that continuously refines dynamic attributes and labels. Three case studies are presented: a reactor as a motivating example, flooding in a simulated de-Butanizer column, as a complex case, and foaming in an absorber as an industrial challenge. For the first case, detection accuracy is shown to increase by 14% while operating costs are reduced by 20%. Moreover, regarding the de-Butanizer column, the performance of the proposed strategy is shown to be 10% higher than the filtering strategy. Promising results are finally reported in regard of efficient strategies to deal with the presented problemPeer ReviewedPostprint (author's final draft

Too Trivial To Test? An Inverse View on Defect Prediction to Identify Methods with Low Fault Risk

Background. Test resources are usually limited and therefore it is often not possible to completely test an application before a release. To cope with the problem of scarce resources, development teams can apply defect prediction to identify fault-prone code regions. However, defect prediction tends to low precision in cross-project prediction scenarios. Aims. We take an inverse view on defect prediction and aim to identify methods that can be deferred when testing because they contain hardly any faults due to their code being "trivial". We expect that characteristics of such methods might be project-independent, so that our approach could improve cross-project predictions. Method. We compute code metrics and apply association rule mining to create rules for identifying methods with low fault risk. We conduct an empirical study to assess our approach with six Java open-source projects containing precise fault data at the method level. Results. Our results show that inverse defect prediction can identify approx. 32-44% of the methods of a project to have a low fault risk; on average, they are about six times less likely to contain a fault than other methods. In cross-project predictions with larger, more diversified training sets, identified methods are even eleven times less likely to contain a fault. Conclusions. Inverse defect prediction supports the efficient allocation of test resources by identifying methods that can be treated with less priority in testing activities and is well applicable in cross-project prediction scenarios.Comment: Submitted to PeerJ C

Development and Validation of Functional Model of a Cruise Control System

Modern automobiles can be considered as a collection of many subsystems working with each other to realize safe transportation of the occupants. Innovative technologies that make transportation easier are increasingly incorporated into the automobile in the form of functionalities. These new functionalities in turn increase the complexity of the system framework present and traceability is lost or becomes very tricky in the process. This hugely impacts the development phase of an automobile, in which, the safety and reliability of the automobile design should be ensured. Hence, there is a need to ensure operational safety of the vehicles while adding new functionalities to the vehicle. To address this issue, functional models of such systems are created and analysed. The main purpose of developing a functional model is to improve the traceability and reusability of a system which reduces development time and cost. Operational safety of the system is ensured by analysing the system with respect to random and systematic failures and including safety mechanism to prevent such failures. This paper discusses the development and validation of a functional model of a conventional cruise control system in a passenger vehicle based on the ISO 26262 Road Vehicles - Functional Safety standard. A methodology for creating functional architectures and an architecture of a cruise control system developed using the methodology are presented.Comment: In Proceedings FESCA 2016, arXiv:1603.0837

Low-cost, high-resolution, fault-robust position and speed estimation for PMSM drives operating in safety-critical systems

In this paper it is shown how to obtain a low-cost, high-resolution and fault-robust position sensing system for permanent magnet synchronous motor drives operating in safety-critical systems, by combining high-frequency signal injection with binary Hall-effect sensors. It is shown that the position error signal obtained via high-frequency signal injection can be merged easily into the quantization-harmonic-decoupling vector tracking observer used to process the Hall-effect sensor signals. The resulting algorithm provides accurate, high-resolution estimates of speed and position throughout the entire speed range; compared to state-of-the-art drives using Hall-effect sensors alone, the low speed performance is greatly improved in healthy conditions and also following position sensor faults. It is envisaged that such a sensing system can be successfully used in applications requiring IEC 61508 SIL 3 or ISO 26262 ASIL D compliance, due to its extremely high mean time to failure and to the very fast recovery of the drive following Hall-effect sensor faults at low speeds. Extensive simulation and experimental results are provided on a 3.7 kW permanent magnet drive

Use of COTS functional analysis software as an IVHM design tool for detection and isolation of UAV fuel system faults

This paper presents a new approach to the development of health management solutions which can be applied to both new and legacy platforms during the conceptual design phase. The approach involves the qualitative functional modelling of a system in order to perform an Integrated Vehicle Health Management (IVHM) design – the placement of sensors and the diagnostic rules to be used in interrogating their output. The qualitative functional analysis was chosen as a route for early assessment of failures in complex systems. Functional models of system components are required for capturing the available system knowledge used during various stages of system and IVHM design. MADe™ (Maintenance Aware Design environment), a COTS software tool developed by PHM Technology, was used for the health management design. A model has been built incorporating the failure diagrams of five failure modes for five different components of a UAV fuel system. Thus an inherent health management solution for the system and the optimised sensor set solution have been defined. The automatically generated sensor set solution also contains a diagnostic rule set, which was validated on the fuel rig for different operation modes taking into account the predicted fault detection/isolation and ambiguity group coefficients. It was concluded that when using functional modelling, the IVHM design and the actual system design cannot be done in isolation. The functional approach requires permanent input from the system designer and reliability engineers in order to construct a functional model that will qualitatively represent the real system. In other words, the physical insight should not be isolated from the failure phenomena and the diagnostic analysis tools should be able to adequately capture the experience bases. This approach has been verified on a laboratory bench top test rig which can simulate a range of possible fuel system faults. The rig is fully instrumented in order to allow benchmarking of various sensing solution for fault detection/isolation that were identified using functional analysis

Improving the Knowledge on Seismogenic Sources in the Lower Tagus Valley for Seismic Hazard Purposes

The Lower Tagus Valley, that includes the metropolitan area of Lisbon, has been struck by several earthquakes which produced significant material damage and loss of lives. Their exact location remains unknown. Our goal is to shed some light into the seismogenic sources in the area using seismic reflection and geological data. In areas with no seismic coverage, potential-field data interpretation was carried out. Seismicity was overlaid to the potential seismogenic structures and high-resolution data was acquired in order to confirm which structures have been active into the Quaternary. Three major fault-zones affecting the Neogene were identified: V. F. Xira, Samora-Alcochete and Pinhal Novo. For the first fault, strong evidences suggest it is active. The other two fault-zones and other structures previously unknown can be correlated with several epicentres. Empirical relationships between maximum moment magnitude and fault area indicate that MW > 6.5 earthquakes can be expected for the larger structures

Alternative sweetener from curculigo fruits

This study gives an overview on the advantages of Curculigo Latifolia as an alternative sweetener and a health product. The purpose of this research is to provide another option to the people who suffer from diabetes. In this research, Curculigo Latifolia was chosen, due to its unique properties and widely known species in Malaysia. In order to obtain the sweet protein from the fruit, it must go through a couple of procedures. First we harvested the fruits from the Curculigo trees that grow wildly in the garden. Next, the Curculigo fruits were dried in the oven at 50 0C for 3 days. Finally, the dried fruits were blended in order to get a fine powder. Curculin is a sweet protein with a taste-modifying activity of converting sourness to sweetness. The curculin content from the sample shown are directly proportional to the mass of the Curculigo fine powder. While the FTIR result shows that the sample spectrum at peak 1634 cm–1 contains secondary amines. At peak 3307 cm–1 contains alkynes

Fault detection, identification and accommodation techniques for unmanned airborne vehicles

Unmanned Airborne Vehicles (UAV) are assuming prominent roles in both the commercial and military aerospace industries. The promise of reduced costs and reduced risk to human life is one of their major attractions, however these low-cost systems are yet to gain acceptance as a safe alternate to manned solutions. The absence of a thinking, observing, reacting and decision making pilot reduces the UAVs capability of managing adverse situations such as faults and failures. This paper presents a review of techniques that can be used to track the system health onboard a UAV. The review is based on a year long literature review aimed at identifying approaches suitable for combating the low reliability and high attrition rates of today’s UAV. This research primarily focuses on real-time, onboard implementations for generating accurate estimations of aircraft health for fault accommodation and mission management (change of mission objectives due to deterioration in aircraft health). The major task of such systems is the process of detection, identification and accommodation of faults and failures (FDIA). A number of approaches exist, of which model-based techniques show particular promise. Model-based approaches use analytical redundancy to generate residuals for the aircraft parameters that can be used to indicate the occurrence of a fault or failure. Actions such as switching between redundant components or modifying control laws can then be taken to accommodate the fault. The paper further describes recent work in evaluating neural-network approaches to sensor failure detection and identification (SFDI). The results of simulations with a variety of sensor failures, based on a Matlab non-linear aircraft model are presented and discussed. Suggestions for improvements are made based on the limitations of this neural network approach with the aim of including a broader range of failures, while still maintaining an accurate model in the presence of these failures