Data analysis troubleshooting

Measurement Selection The identification of which measurements to make is an often overlooked aspect of plant-performance analysis. The end use of the data interpretation must be understood (i.e., the purpose for which the data, the parameters, or the resultant model will be used). For example, building a mathematical model of the process to explore other regions of operation is an end use. Another is to use the data to troubleshoot an operating problem. The level of data accuracy, the amount of data, and the sophistication of the interpretation depends upon the accuracy with which the result of the analysis needs to oe known. Daily measurements to a great extent and special plant measurements to a lesser extent are rarelv planned with the end use in mind. The result is typically too little data of too low accuracy or an inordinate amount with the resultant misuse in resources. 

Set the flow rate of the HPLC system to 1 ml/min across a 5-pm x 250-mm x 4.6-mm Prodigy ODS-3 column (or equivalent) at ambient temperature. Set the detector at 520 nm. Inject 50 pi sample into the HPLC system and start a gradient similar to that outlined in Table FI. 3.3. Analyze data as described (see Data Analysis in Critical Parameters and Troubleshooting). 

Process-control systems wiU become more integrated with condition-based monitoring systems that in turn link to CMMS/EAM systems or even back to the condition-based equipment suppliers for real-time troubleshooting. Condition-based monitoring systems wUl eventuaUy link data coUection directly back to suppliers such as CSI, ENTER, SKF, and others via the Internet or RF. Reliability data analysis by off-site reliability experts providing contract services wUl be as close as e-mail. 

Data collection, organization, and analysis are another approach that can be used to troubleshoot process problems. Check-sheets are used to collect large quantities of data. This quantitative data can be organized into graphics or trends to plot process variation or changes. Data analysis utilizes a variety of quality techniques to put all of the parts in place. Control charts are frequently used to assist process technicians in operating a complicated process. Quality tools include a variety of methods designed to improve product quality. 

Report results quickly to enable timely troubleshooting If results do not meet the acceptance criteria. Data analysis... 

The above assumes that the measurement statistics are known. This is rarely the case. Typically a normal distribution is assumed for the plant and the measurements. Since these distributions are used in the analysis of the data, an incorrect assumption will lead to further bias in the resultant troubleshooting, model, and parameter estimation conclusions. 

Corley, J. E., Troubleshooting Turbomachinery Problems Using a Statistical Analysis of Failure Data, Proceedings of the 19th Turbomachinery Symposium, Texas A M University, College Station, TX, 1990, pp, 149-158. 

If initial SST fails, the analyst should stop the sequence immediately, diagnose the problem, make necessary adjustments or repairs, and re-perform SST. Analysis of actual samples should commence only after passing all SST limits, not only the failed criteria. Most SST failures are traced to problems from the autosampler, pump, column, or mobile phase (see Chapter 10 on troubleshooting hints). If one of the interspersed SSS injections fails, data from all samples after the last passing SSS become invalid and must be repeated.15... 

Although complete and contemporaneous documentation of study events is critical from a regulatory perspective, it should not be viewed solely as a regulatory requirement. It is also essential to the bioanalytical laboratory in its effort to demonstrate that a method is reliable and can produce high-quality data. Furthermore, comprehensive recording of study events can assist the bioanalytical laboratory in troubleshooting efforts, when unexpected problems occur during routine sample analysis. 

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