23,835 research outputs found
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Towards Trouble-Free Networks for End Users
Network applications and Internet services fail all too frequently. However, end users cannot effectively identify the root cause using traditional troubleshooting techniques due to the limited capability to distinguish failures caused by local network elements from failures caused by elements located outside the local area network.
To overcome these limitations, we propose a new approach, one that leverages collaboration of user machines to assist end users in diagnosing various failures related to Internet connectivity and poor network performance.
First, we present DYSWIS ("Do You See What I See?"), an automatic network fault detection and diagnosis system for end users. DYSWIS identifies the root cause(s) of network faults using diagnostic rules that consider diverse information from multiple nodes. In addition, the DYSWIS rule system is specially designed to support crowdsourced and distributed probes. We also describe the architecture of DYSWIS and compare its performance with other tools. Finally, we demonstrate that the system successfully detects and diagnoses network failures which are difficult to diagnose using a single-user probe.
Failures in lower layers of the protocol stack also have the potential to disrupt Internet access; for example, slow Internet connectivity is often caused by poor Wi-Fi performance. Channel contention and non-Wi-Fi interference are the primary reasons for this performance degradation. We investigate the characteristics of non-Wi-Fi interference that can severely degrade Wi-Fi performance and present WiSlow ("Why is my Wi-Fi slow?"), a software tool that diagnoses the root causes of poor Wi-Fi performance. WiSlow employs user-level network probes and leverages peer collaboration to identify the physical location of these causes. The software includes two principal methods: packet loss analysis and 802.11 ACK number analysis. When the issue is located near Wi-Fi devices, the accuracy of WiSlow exceeds 90%.
Finally, we expand our collaborative approach to the Internet of Things (IoT) and propose a platform for network-troubleshooting on home devices. This platform takes advantage of built-in technology common to modern devices --- multiple communication interfaces. For example, when a home device has a problem with an interface it sends a probe request to other devices using an alternative interface. The system then exploits cooperation of both internal devices and remote machines. We show that this approach is useful in home networks by demonstrating an application that contains actual diagnostic algorithms
A study on acoustic emission signal propagation of a small size multi-cylinder diesel engine
Acoustic emission has been found effective in offering earlier fault detection and improving identification capabilities of faults. However, the sensors are inherently uncalibrated. This paper presents a source to sensor paths calibration technique which can lead to diagnosis of faults in a small size multi-cylinder diesel engine. Preliminary analysis of the acoustic emission (AE) signals is outlined, including time domain, time-frequency domain, and the root mean square (RMS) energy. The results reveal how the RMS energy of a source propagates to the adjacent sensors. The findings lead to allocate the source and estimate its inferences to the adjacent sensor, and finally help to diagnose the small size diesel engines by minimising the crosstalk from multiple cylinders
Structural health monitoring for wind turbine foundations
The construction of onshore wind turbines has rapidly been increasing as the UK attempts to meet its renewable energy targets. As the UK’s future energy depends more on wind farms, safety and security are critical to the success of this renewable energy source. Structural integrity of the tower and its components is a critical element of this security of supply. With the stochastic nature of the load regime a bespoke low cost structural health monitoring system is required to monitor integrity of the concrete foundation supporting the tower. This paper presents an assessment of ‘embedded can’ style foundation failure modes in large onshore wind turbines and proposes a novel condition based monitoring solution to aid in early warning of failure. The most common failure modes are discussed and a low-cost remote monitoring system is presented
Space station automation of common module power management and distribution
The purpose is to automate a breadboard level Power Management and Distribution (PMAD) system which possesses many functional characteristics of a specified Space Station power system. The automation system was built upon 20 kHz ac source with redundancy of the power buses. There are two power distribution control units which furnish power to six load centers which in turn enable load circuits based upon a system generated schedule. The progress in building this specified autonomous system is described. Automation of Space Station Module PMAD was accomplished by segmenting the complete task in the following four independent tasks: (1) develop a detailed approach for PMAD automation; (2) define the software and hardware elements of automation; (3) develop the automation system for the PMAD breadboard; and (4) select an appropriate host processing environment
Real Time Fault Detection and Diagnostics Using FPGA-Based Architecture
Errors within circuits caused by radiation continue to be an important concern to developers. A new methodology of real time fault detection and diagnostics utilizing FPGA based architectures while under radiation were investigated in this research. The contributions of this research are focused on three areas; a full test platform to evaluate a circuit while under irradiation, an algorithm to detect and diagnose fault locations within a circuit, and finally to characterize Triple Design Triple Modular Redundancy (TDTMR), a new form of TMR. Five different test setups, injected fault test, gamma radiation test, thermal radiation test, optical laser test, and optical flash test, were used to assess the effectiveness of these three research goals. The testing platform was constructed with two FPGA boards, the Device Under Test (DUT) and the controller board, to generate and evaluate specific vector sets sent to the DUT. The testing platform combines a myriad of testing and measuring equipment and work hours onto one small reprogrammable and reusable FPGA. This device was able to be used in multiple test setups. The controlling logic can be interchanged to test multiple circuit designs under various forms of radiation. The detection and diagnostic algorithm was designed to determine fault locations in real time. The algorithm used for diagnosing the fault location uses inverse deductive elimination. By using test generation tools, fault lists were developed. The fault lists were used to narrow \ the possible fault locations within the circuit. The algorithm is able to detect single stuck at faults based on these lists. The algorithm can also detect multiple output errors but not able to diagnose multiple stuck at faults in real time
Symbolic QED Pre-silicon Verification for Automotive Microcontroller Cores: Industrial Case Study
We present an industrial case study that demonstrates the practicality and
effectiveness of Symbolic Quick Error Detection (Symbolic QED) in detecting
logic design flaws (logic bugs) during pre-silicon verification. Our study
focuses on several microcontroller core designs (~1,800 flip-flops, ~70,000
logic gates) that have been extensively verified using an industrial
verification flow and used for various commercial automotive products. The
results of our study are as follows: 1. Symbolic QED detected all logic bugs in
the designs that were detected by the industrial verification flow (which
includes various flavors of simulation-based verification and formal
verification). 2. Symbolic QED detected additional logic bugs that were not
recorded as detected by the industrial verification flow. (These additional
bugs were also perhaps detected by the industrial verification flow.) 3.
Symbolic QED enables significant design productivity improvements: (a) 8X
improved (i.e., reduced) verification effort for a new design (8 person-weeks
for Symbolic QED vs. 17 person-months using the industrial verification flow).
(b) 60X improved verification effort for subsequent designs (2 person-days for
Symbolic QED vs. 4-7 person-months using the industrial verification flow). (c)
Quick bug detection (runtime of 20 seconds or less), together with short
counterexamples (10 or fewer instructions) for quick debug, using Symbolic QED
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Impact of accidents on organizational aspects of nuclear utilities
This paper applies the Beer Viable Systems Model (VSM) approach to the study of nuclear accidents. It relates how organizational structures and rules are affected by accidents in the attempt to improve safety and reduce risk. The paper illustrates this process with reference to a number of accidents. The dynamic cybernetic aspect of the VSM approach to organizations yields a better understanding of the need for good decision-making to minimize risk and how organizations really operate
Smart Asset Management for Electric Utilities: Big Data and Future
This paper discusses about future challenges in terms of big data and new
technologies. Utilities have been collecting data in large amounts but they are
hardly utilized because they are huge in amount and also there is uncertainty
associated with it. Condition monitoring of assets collects large amounts of
data during daily operations. The question arises "How to extract information
from large chunk of data?" The concept of "rich data and poor information" is
being challenged by big data analytics with advent of machine learning
techniques. Along with technological advancements like Internet of Things
(IoT), big data analytics will play an important role for electric utilities.
In this paper, challenges are answered by pathways and guidelines to make the
current asset management practices smarter for the future.Comment: 13 pages, 3 figures, Proceedings of 12th World Congress on
Engineering Asset Management (WCEAM) 201
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