Data process status

Evaluation of the process status in chemical technology (from spectroscopic and chemical analytical data)... 

Autocorrelation in data affects the accuracy of the charts developed based on the iid assumption. One way to reduce the impact of autocorrelation is to estimate the value of the observation from a model and compute the error between the measured and estimated values. The errors, also called residuals, are assumed to have a Normal distribution with zero mean. Consequently regular SPM charts such as Shewhart or CUSUM charts could be used on the residuals to monitor process behavior. This method relies on the existence of a process model that can predict the observations at each sampling time. Various techniques for empirical model development are presented in Chapter 4. The most popular modeling technique for SPM has been time series models [1, 202] outlined in Section 4.4, because they have been used extensively in the statistics community, but in reality any dynamic model could be used to estimate the observations. If a good process model is available, the prediction errors (residual) e k) = y k)—y k) can be used to monitor the process status. If the model provides accurate predictions, the residuals have a Normal distribution and are independently distributed with mean zero and constant variance (equal to the prediction error variance). 

Figure 8.15. Process status for the validation data set. Reprinted from [62]. Copyright 2001 with permission from Elsevier.

The Nuplex 80+ CRTs are driven by the Data Processing System (DPS) described in CESSAR-DC Section 7.7.1.7. The DPS is a computer-based system that provides plant data and status information to the operator, derived or processed from plant sensors including the Post Accident Monitoring Instrumentation sensors. The information is available on both a real-time and historical basis. SPDS and other information necessary for the handling of emergency plant conditions and assessment of their consequences is also provided by the DPS to the TSC and EOF when they are activated and manned, as described in CESSAR-DC Sections 13.3.3.1 and 13.3.3.2 respectively. Key parameter values processed by the DPS are also indicated directly via the Discrete Indication and Alarm System (CESSAR-DC Section 7.7.1.4) on discrete indicators located on the main control room and remote shutdown panels. 

Surveillance Status of radar surveillance on ground and final approach VHF Com Runway Controller Availability of the controller s VHF Com system FDP/EFS—Status of clearance input for both aircraft into the Flight Data Processing/Electronic Flight Strip system as well as status of the FDP/EFS system CATC Availability Availability of CATC system CATC Alert—Whether CATC gives an alert... 

Software can be imagined as a finite automate. The status of the automate is the entirety of all of the data recorded in the memory and that either came from the outside (sensor data, the status of external devices etc.) or that were derived as a result of the software processes themselves (calculation results, commands that were derived by the software and have been directed to external devices, the point at which the software has arrived in the program sequence). All information is discrete and depends only on the entire history of the system, but not on its future. 

OpenMADS was used to compute system availability of the DSPNs generated by the translation process. We use arbitrary (but reasonable) input parameters, since the purpose of this example is to show the applicability of OpenMADS. Note that all these parameters are defined through MARTE armotations and block properties and they are assumed to be exponentially distributed except by the monitoring trigger interval to check the Data Center status, which is deterministic. The availability and downtime (hours per year) of the Web Server system and each of its components are shown in Table 1. One should note that Asys is not equal to the product of the availability for each component because they are not independent. The results show that the Web Servers have the lowest availability among all components in the system, being therefore one of the dependability bottlenecks in the modeled environment. 

A consultation is initiated by a quarry that can be inputted automatically. The inference process is fired by a quarry that is automatically called once the required data has been entered. As the inference process proceeds a data file is generated that hold the current processing status. The quarry is in the form of procedure rule, "IF quarry anything, THEN check anything" and uhen the statement quarry best robot is read, the floating variable anything assumes the value best robot. A combination of forward and backuardchaining is used to reach a conclusion. 

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