For such software systems, an independent assessment is put into effect (Independent Software Verification and Validation – ISVV) after the regular development lifecycle and V&V activities, aiming at finding residual faults and raising confidence in the software.
Many existing NHPP software reliability models [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26] have been carried out through the fault intensity rate function and the mean value functions (m(t)) within a controlled testing environment to estimate reliability metrics such as the number of residual faults, failure rate, and reliability of software.
Many existing NHPP software reliability models [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28] have been used through the fault intensity rate function and the mean value functions m(t) within a controlled testing environment to estimate reliability metrics such as the number of residual faults, failure rate, and reliability of the software.
In fault diagnosis system designers or engineers should analyse systems in order to define their input patterns, features, classes required for pattern recognition, and corresponding residuals, fault symptoms, faults needed for fault diagnosis, respectively.
The proposed algorithm incrementally learns the residuals-faults relationships and classifies the faults including multiple new classes, based on a dynamically weighted consult and vote mechanism that combines the outputs of the base-classifiers of the ensemble.
Although based on a common framework, the fundamental difference between the two algorithms is that one uses a global model for residual generation, fault isolation, and fault identification; whereas the other uses a set of minimal submodels computed using Possible Conflicts.
Figure 13 shows the residual error of wear fault between swash plate and slipper.
Figure 13 Residual error of wear fault between swash plate and slipper.
