Sentence examples for failure classifications from inspiring English sources

The machinery failure classifications include: process condition changes; improper assembly/maintenance/installation: improper operating procedures; design deficiencies; and component wear out.

The causes of machinery failure can be grouped in the failure classifications that include: process condition changes; assembly/installation; operating procedures; design deficiencies; and component wear out.

The paper presents an approach to implement a failure classification and associated analyses for production ramp-up.

The paper builds the new formulas on a thorough discussion of the concepts of spurious activation, failure classification, and failure propagation in a safety-instrumented system.

Finally, an optimal SVM model is established and trained for engine failure classification by using the fault feature vectors of the noise signals.

The structural design of the failure classification is described in terms of a faceted classification by design principles to meet the requirements for the indexing of failure cases and the evaluation of specific failure facets.

We conclude that the reported failure classification method offers the possibility to compare fracture toughness values in multilayered coatings with multiple failure types, derived solely from acoustic emission energies.

We study the case of "inspect-all" policy, using off-line quality inspections to prevent non-conforming items from reaching the final consumer, in domains where an item is rejected upon first "failure" classification.

The modified RIFLE (risk, injury, failure, loss of kidney function, and end-stage renal failure) classification based on changes in serum creatinine (sCr) relative to the baseline condition may provide valuable data to determine patients in need for renal replacement therapy (RRT) ([1]).

According to RIFLE (Risk, Injury, Failure) classification 26.6% (95% CI 26.0 to 27.2%) of patients had AKI (RIFLE R-F).

When adjusting for potential confounders such as age, glucose level, duration of cardiopulmonary bypass and preoperative NYHA heart failure classification, this difference remained significant (P < 0.0001).