Fault estimate

We used the logic specification, the set of faults found and the test specifications to evaluate our fault estimation and reliability prediction methods. 

Having parameterised the model it is possible to convert a coverage measure into a residual fault estimate. Using the logic simulator we measured the coverage achieved using the customer tests that were applied to the Intermediate version of PLC logic implementation. The customer tests are summarised in Table 2. 

BertoHno, A., Strigini, L. Assessing the risk due to software faults estimates of failure rate vs evidence of perfection. Software Testing, Verihcation and Reliability 8(3), 155 166 (1998)... 

Other information that can be obtained from such map is the location of faults, the status and location of wells and the location of the fluid contacts. Figure 5.45 shows some of the most frequently used map symbols. Structural maps are used in the planning of development activities such as well trajectories/targets and the estimation of reserves. 

Comparison of these environmental compartment concentrations with the actual measurements made at a variety of locations show reasonable agreement but indicate that emission estimates are rather high. It is likely that the fault Hes with worst case estimates for losses from outdoor appHcations and the washing of PVC flooring. In addition a large proportion of the phthalates lost by these routes will not enter rivers because they will be removed by wastewater treatment plants. 

Fault Tree Analysis Faiilt tree analysis permits the hazardous incident (called the top event) frequency to be estimated from a logic model of the failure mechanisms of a system. The top event is traced downward to more basic failures using logic gates to determine its causes and hkelihood. The model is based on the combinations of fail-... 

Frequency Estimation There are two primary sources for estimates of incident frequencies. These are historical records and the apphcation of fault tree analysis and related techniques, and they are not necessarily applied independently. Specific historical data can sometimes be usehiUy applied as a check on frequency estimates of various subevents of a fault tree, for example. 

Identification and quantitative estimation of common-cause failures are general problems in fault tree analysis. Boolean approaches are generally better smted to mathematically handle common-cause failures. 

Isermann R., Process Fault Detection Based on Modeling and Estimation Methods—A Survey, Automatica, 20(4), 1984, 387 04 (Fault detection survey article)... 

Wei, C.N., Diagnose Process Problems, Chemical Engineeiing Piogiess, September 1991, 70-74. (Parameter estimate monitoring for fault detection)... 

History The histoiy of a plant forms the basis for fault detection. Fault detection is a monitoring activity to identify deteriorating operations, such as deteriorating instrument readings, catalyst usage, and energy performance. The plant data form a database of historical performance that can be used to identify problems as they form. Monitoring of the measurements and estimated model parameters are typic fault-detection activities. 

At this point, analysts have a set of adjusted measurements that may better represent the unit operation. These will ultimately be used to identify faults, develop a model, or estimate parameters. This automatic reconciliation is not a panacea. Incomplete data sets, unknown uncertainties and incorrec t constraints all compromise the accuracy of the adjustments. Consequently, preliminary adjustments by hand are still recommended. Even when automatic adjustments appear to be correct, the resiilts must be viewed with some skepticism. 

Overview Interpretation is the process for using the raw or adjusted unit measurements to troubleshoot, estimate parameters, detect faults, or develop a plant model. The interpretation of plant performance is defined as a discreet step but is often done simultaneously with the identification of hypotheses and suitable measurements and the treatment of those measurements. It is isolated here as a separate process for convenience of discussion. 

Parameter estimation is a procedure for taking the unit measurements and reducing them to a set of parameters for a physical (or, in some cases, relational) mathematical model of the unit. Statistical interpretation tempered with engineering judgment is required to arrive at realistic parameter estimates. Parameter estimation can be an integral part of fault detection and model discrimination. 

Parameter Estimation Relational and physical models require adjustable parameters to match the predicted output (e.g., distillate composition, tower profiles, and reactor conversions) to the operating specifications (e.g., distillation material and energy balance) and the unit input, feed compositions, conditions, and flows. The physical-model adjustable parameters bear a loose tie to theory with the limitations discussed in previous sections. The relational models have no tie to theory or the internal equipment processes. The purpose of this interpretation procedure is to develop estimates for these parameters. It is these parameters hnked with the model that provide a mathematical representation of the unit that can be used in fault detection, control, and design. 

FIG. 30-25 Trend in model parameter developed during fault detection parameter estimation. 

This is a simple calculation to determine the maximum symmetrical fault level of a system, to select the type of equipment, devices and bus system etc. But to decide on a realistic protective scheme, the asymmetrical value of the fault current must be estimated by including all the likely impedances of the circuit. 

As a rule and as recommended by lEC 60255-6, the POC may be chosen within 30% of the minimum estimated ground fault eurrent. When the. scheme is required to deteet only a ground fault, a single-pole relay is conneeted between all the CTs shorted ends (Figure 15.29). All the CTs now fall in parallel. 

Layer of protection analysis (LOPA) is a simplified form of event tree analysis. Instead of analyzing all accident scenarios, LOPA selects a few specific scenarios as representative, or boundary, cases. LOPA uses order-of-magnitLide estimates, rather than specific data, for the frequency of initiating events and for the probability the various layers of protection will fail on demand. In many cases, the simplified results of a LOPA provide sufficient input for deciding whether additional protection is necessary to reduce the likelihood of a given accident type. LOPAs typically require only a small fraction of the effort required for detailed event tree or fault tree analysis. 

Frequency Phase 3 Use Branch Point Estimates to Develop a Ere-quency Estimate for the Accident Scenarios. The analysis team may choose to assign frequency values for initiating events and probability values for the branch points of the event trees without drawing fault tree models. These estimates are based on discussions with operating personnel, review of industrial equipment failure databases, and review of human reliability studies. This allows the team to provide initial estimates of scenario frequency and avoids the effort of the detailed analysis (Frequency Phase 4). In many cases, characterizing a few dominant accident scenarios in a layer of protection analysis will provide adequate frequency information. 

The variation in the on and off potentials or the potential difference along the pipeline will usually indicate faults that prevent the attainment of complete cathodic protection. The protection current requirement of the pipeline may be estimated from experience if the age and type of pipeline is known (see Fig. 5-3). Figure 3-20 shows the variation in the on and off potentials of a 9-km pipeline section DN 800 with 10-mm wall thickness. At the end of the pipeline, at 31.84 km, an insulating unit is built in. The cathodic protection station is situated at 22.99 km. Between this and the end of the pipeline there are four pipe current measuring points. The applied protection current densities and coating resistances of individual pipeline sections are calculated from Eqs. (3-40) and (3-41). In the upper diagram the values of... 

A standard for the minimum acceptable process capability index for any component/characteristic is normally set at = 1.33, and this standard will be used later to align costs of failure estimates. If the characteristics follow a Normal distribution, Cp = 1.33 corresponds to a fault probability of ... 

Scherrer equation to estimate the size of organized regions Imperfections in the crystal, such as particle size, strains, faults, etc, affect the X-ray diffraction pattern. The effect of particle size on the diffraction pattern is one of the simplest cases and the first treatment of particle size broadening was made by Scherrer in 1918 [16]. A more exact derivation by Warren showed that. 

VIEW is the quantification module. All minimal cutsets are stored in the speciiic libraries for the fault trees, supercomponents and sequences. VIEW recalculates the point estimates. It computes and displays the Fussel-Vesely importance, risk increase and risk reduction measures. 

The Systems Module constructs and displays fault trees using EASYFLOW which aic read automatically to generate minimal cutsets that can be transferred, for solution, to SETS. CAFT A. or IRRAS and then transferred to RISKMAN for point estimates and uncertainty analysi,s using Monte Carlo simulations or Latin hypercube. Uncertainty analysis is performed on the systems lev el using a probability quantification model and using Monte Carlo simulations from unavailability distributions. 

The frequency of an initiating event is usually based on industrial experience. If the process is new or rare, it may be estimated by a system model of the process steps (e.g., a fault tree) and using data from similar experience to give the probability of failure of the steps. Either of these estimates should consider the possibility of mitigating actions to prevent the hazard from having detrimental effects. 

---