Investigates Reliability vs Load in Window Computer Processor consumption been investigated in literature but "Reliability vs. Load" is rarely investigated. Measures reliability of Data History Polls and monitoring Such a reliability vs. load is observed on physical mockup, findings are hard to predict and close to actual scenarios.Reliability is an important criterion for the applications, especially when there are humans involved in the activity. There are a limited number of works addressing the reliability concerns of a digital industrial plant scenario with machine to machine (M2M) communications. Results on a windows embedded platform are rare as well. In this paper, we investigate the suitability of such devices for presenting the data of a digital oil rig to the humans within the scope of the TÜBİTAK KAMAK Project. There can be two types of reads.A simple one regarding current values of observed variables. Also, a period of their occurrences can be recalled, referred to as a Historic read. An algorithm can consider both techniques. Our observations indicate: the simple reads are more resilient than the historical ones that represent past data of a readable variable. Figure A. The name figure or table about given info and results
Investigates failure arrivals in a Windows Industrial Workshop Communication environment Among the rare observations for failure arrival times. Exponential can be used with reasonable error that paves way for Markovian Analysis.Using a reliability function with a known classical distribution, at an instance in time, the chances of an activity success can be predicted. Based on this curve, a proper reset period can be appointed. This would help to keep the risk under control. For this purpose, a test bed mimicking the machine to machine (M2M) communication of a Digital Oil Rig is employed. It contains an Embedded Computer Server and an ordinary computer acting as a client querying data from the server. On this testbed, as the communication protocol Open Platform Communications -Unified Architecture (OPC UA) is run. In "most occasions, the arrival processes are assumed as a Poisson Arrival Process. The Question of a concern: "Is this assumption valid in case of an Embedded Computer responding to queries?". Our results indicate that the failure arrivals are independent identically distributed (IID) and can be modeled with an Exponential Distribution by accepting a reasonable error. Figure A. or Table A The name figure or table about given info and results
While making reliability observations, more samples mean one can make a statistically representative prediction. It is possible to model the failure arrival characteristics statistically using this knowledge. As a natural product of many experiments, a mean and variance figure can be identified for modelling the different occurrences. Even though the different situations can be modelled with such parameters, it may not wholly outline the condition of the product being developed and under test. The variance calculation series derived from the original reliability observation series can be an important consideration. With a mean and a variance figure, a statistical prediction can be made. However, with the very same parameters, another reliability characteristic carrying product or subcomponent may exist. For this instance, identifying whether the variance calculation series is stationarity and incorporating it in calculations can yield a possible prediction of a more accurate statistical model. In this study, the variance calculation series at hand is shown to possess a stationary character yielding further modelling possibilities and emphasizing the importance of this consideration.
The series in economics or financial operations are time to time augmented to identify for a hidden character. Such manipulations are based on equal addition or subtraction on both sides of an auto regression formula accepted that the series are generated with. But, how useful these new ones be? Accepting an underlying hidden character, would an additional effort be needed? This paper tries to identify the impact of Dif-ferencing and Bi-sample mean operation on reliability observations series. Such an observations series is collected with a target to identify the probability of a failure for a period of active operation. From the tested manipulations , the differencing seems to be alleviating the trend and seasonality to a degree but shifting signal to heteroscedasticity. The bi-sample averaging is observed to be producing rather a negative impact. Therefore, if a series for reliability predictions depending upon interventions of this sort is at hand, then additional considerations could be convenient.
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