Online Defect Tracking System

For Improving Software Reliability, Defect Tracking System (DTS) gives the facility to define the tasks and allow the managers to track the Defects and time spent by each employee for that particular task. This tool can help managers for Defects (Bugs) estimation per project. This tool also helps employees to document their Defects and analyze the quality of their output. Moreover the project aims at creation of a Defect Tracking System which will be accessible to all developers and its facility allows to focusing on creating the database schema and while letting the application server define table based on the fields in JSP and relationships between them. The objectives of this system are to keep track of employee skills and based on the skills, assignment of the task is done to an employee. Employee does Defects capturing. It can be done on daily basis

Learning from accidents : machine learning for safety at railway stations

In railway systems, station safety is a critical aspect of the overall structure, and yet, accidents at stations still occur. It is time to learn from these errors and improve conventional methods by utilizing the latest technology, such as machine learning (ML), to analyse accidents and enhance safety systems. ML has been employed in many fields, including engineering systems, and it interacts with us throughout our daily lives. Thus, we must consider the available technology in general and ML in particular in the context of safety in the railway industry. This paper explores the employment of the decision tree (DT) method in safety classification and the analysis of accidents at railway stations to predict the traits of passengers affected by accidents. The critical contribution of this study is the presentation of ML and an explanation of how this technique is applied for ensuring safety, utilizing automated processes, and gaining benefits from this powerful technology. To apply and explore this method, a case study has been selected that focuses on the fatalities caused by accidents at railway stations. An analysis of some of these fatal accidents as reported by the Rail Safety and Standards Board (RSSB) is performed and presented in this paper to provide a broader summary of the application of supervised ML for improving safety at railway stations. Finally, this research shows the vast potential of the innovative application of ML in safety analysis for the railway industry

Condition Monitoring of Power Cables

A National Grid funded research project at Southampton has investigated possible methodologies for data acquisition, transmission and processing that will facilitate on-line continuous monitoring of partial discharges in high voltage polymeric cable systems. A method that only uses passive components at the measuring points has been developed and is outlined in this paper. More recent work, funded through the EPSRC Supergen V, UK Energy Infrastructure (AMPerES) grant in collaboration with UK electricity network operators has concentrated on the development of partial discharge data processing techniques that ultimately may allow continuous assessment of transmission asset health to be reliably determined

Framework for a space shuttle main engine health monitoring system

A framework developed for a health management system (HMS) which is directed at improving the safety of operation of the Space Shuttle Main Engine (SSME) is summarized. An emphasis was placed on near term technology through requirements to use existing SSME instrumentation and to demonstrate the HMS during SSME ground tests within five years. The HMS framework was developed through an analysis of SSME failure modes, fault detection algorithms, sensor technologies, and hardware architectures. A key feature of the HMS framework design is that a clear path from the ground test system to a flight HMS was maintained. Fault detection techniques based on time series, nonlinear regression, and clustering algorithms were developed and demonstrated on data from SSME ground test failures. The fault detection algorithms exhibited 100 percent detection of faults, had an extremely low false alarm rate, and were robust to sensor loss. These algorithms were incorporated into a hierarchical decision making strategy for overall assessment of SSME health. A preliminary design for a hardware architecture capable of supporting real time operation of the HMS functions was developed. Utilizing modular, commercial off-the-shelf components produced a reliable low cost design with the flexibility to incorporate advances in algorithm and sensor technology as they become available

Diagnosis of Multiple Scan-Chain Faults in the Presence of System Logic Defects

We present a combined hardware-software based approach to scan-chain diagnosis, when the outcome of a test may be affected by system faults occurring in the logic outside of the scan chain. For the hardware component we adopt the double-tree scan (DTS) chain architecture, which has previously been shown to be effective in reducing power, volume, and application time of tests for stuck-at and delay faults. We develop a version of flush test which can resolve a multiple fault in a DTS chain to a small number of suspect candidates. Further resolution to a unique multiple fault is enabled by the software component comprising of fault simulation and analysis of the response of the circuit to test patterns produced by ATPG. Experimental results on benchmark circuits show that near-perfect scan-chain diagnosis for multiple faults is possible even when a large number of random system faults are injected in the circuit

Simulation based formal verification of cyber-physical systems

Cyber-Physical Systems (CPSs) have become an intrinsic part of the 21st century world. Systems like Smart Grids, Transportation, and Healthcare help us run our lives and businesses smoothly, successfully and safely. Since malfunctions in these CPSs can have serious, expensive, sometimes fatal consequences, System-Level Formal Verification (SLFV) tools are vital to minimise the likelihood of errors occurring during the development process and beyond. Their applicability is supported by the increasingly widespread use of Model Based Design (MBD) tools. MBD enables the simulation of CPS models in order to check for their correct behaviour from the very initial design phase. The disadvantage is that SLFV for complex CPSs is an extremely time-consuming process, which typically requires several months of simulation. Current SLFV tools are aimed at accelerating the verification process with multiple simulators working simultaneously. To this end, they compute all the scenarios in advance in such a way as to split and simulate them in parallel. Furthermore, they compute optimised simulation campaigns in order to simulate common prefixes of these scenarios only once, thus avoiding redundant simulation. Nevertheless, there are still limitations that prevent a more widespread adoption of SLFV tools. Firstly, current tools cannot optimise simulation campaigns from existing datasets with collected scenarios. Secondly, there are currently no methods to predict the time required to complete the SLFV process. This lack of ability to predict the length of the process makes scheduling verification activities highly problematic. In this thesis, we present how we are able to overcome these limitations with the use of a simulation campaign optimiser and an execution time estimator. The optimiser tool is aimed at speeding up the SLFV process by using a data-intensive algorithm to obtain optimised simulation campaigns from existing datasets, that may contain a large quantity of collected scenarios. The estimator tool is able to accurately predict the execution time to simulate a given simulation campaign by using an effective machine-independent method

Digital twin and its implementations in the civil engineering sector

Digital Twin (DT) concept has recently emerged in civil engineering; however, some problems still need to be addressed. First, DT can be easily confused with Building Information Modelling (BIM) and Cyber-Physical Systems (CPS). Second, the constituents of DT applications in this sector are not well-defined. Also, what the DT can bring to the civil engineering industry is still ambiguous. To address these problems, we reviewed 468 articles related to DT, BIM and CPS, proposed a DT definition and its constituents in civil engineering and compared DT with BIM and CPS. Then we reviewed 134 papers related to DT in the civil engineering sector out of 468 papers in detail. We extracted DT research clusters based on the co-occurrence analysis of paper keywords' and the relevant DT constituents. This research helps establish the state-of-the-art of DT in the civil engineering sector and suggests future DT development