ABSTRACT

In the filtering process, we classify errors into two categories: obvious and hidden errors. Obvious errors are the errors that can be easily determined with applying one-by-one data check. Changing data orderings make this process even faster. Examples for such errors are unknown assembly dates, invalid time-to-failure values, and quality related errors. In contrary to the obvious errors, hidden errors and their probable sources cannot be directly determined from the data. Examples for hidden errors are significant changes in product manufacturing process, data loss, and inappropriate data recording in services. Hidden errors are fatal and overwhelm obvious errors in terms the number of occurrences. This obligates us to eliminate hidden errors before starting statistical analysis. In this work, we propose a new systematic approach to determine and eliminate both obvious and hidden errors. After eliminating the errors, now we have “filtered data” that is accurate and ready to be used in our statistical modeling.