Sentence examples for fault categories from inspiring English sources

Automatic and accurate identification of rolling bearing fault categories, especially for the fault severities and compound faults, is a challenge in rotating machinery fault diagnosis.

The results show that the support vector machine identifies the fault categories of rolling element bearing more accurately and has a better diagnosis performance.

Automatic and accurate identification of rolling bearings fault categories, especially for the fault severities and fault orientations, is still a major challenge in rotating machinery fault diagnosis.

Then, typical performance indicators such as error detection and latency were presented for the different fault categories itemized by the location and means of detection.

The analysis and comparison results indicate that the proposed method is very effective in distinguishing the fault categories of rolling bearings and can get a higher identifying rate than the contrast methods.

Our solution estimates degrees of similarity among structural elements from heterogeneous log data, constructs combined Bayesian network (CBN), uses similarity based learning algorithm to compute probabilities in CBN, and classifies test log data into most probable fault categories based on the generated CBN.

Next, recognition and diagnosis can be solved by the simple SRC without additional classifier, exploiting the sparse nature that the key entries in sparse representation vector are assigned to the corresponding fault category for a test sample.

In the proposed method, P-SVDD can detect the unknown fault sample by particle swarm optimization (PSO) parameter optimization while P-KFCM is used in known sample category recognition and its modified partition coefficient (MPC) cluster validity is used in unknown fault category search.

Classification process is then followed to predict the fault category.

Through the laboratory experiments, we obtained 12 signal samples for every fault category, each of them having 1-s duration and 7,680 number of samples.

Another reason comes from analyzing RCPs from 9 to 12, where it is observed that the Normal (and in many cases Unknown) category achieves similar levels to a certain fault category.