Immunity Inspired Hybrid Fault Diagnosis and Conflict Resolution
Bibliographic record
Abstract
This article develops a hybrid fault diagnosis (FD) method for industrial machines. The method considers the traits of reliable information from models and multidomain features of signals, and optimally incorporates the capabilities of intelligent processing techniques. The approach is inspired by a multilayer biological immune system, and consists of generalized (nonspecific) and specialized FD subsystems. First, a genetic algorithm-optimized artificial immune system technique is presented, which uses signal processing to extract multiperspective system features and selects low-dimensional features for intelligent fault detection. Second, a system identification approach is employed, which incorporates adaptive thresholding-based fault detection, and a fault severity index for fault identification. The developed hybrid FD technique opts for a synergy-based coordination approach of nonspecific intelligent fault detection and specific model-based FD. Specifically, it analyzes the data-parallel operation of the two methods and incorporates a comprehensive self-assessment-based conflict resolution mechanism to achieve improved and reliable FD in case of incomplete dataset knowledge and model discrepancies. The efficacy of the developed method, in FD, is assessed using systems with broken rotor and bearing fault.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".