Fault diagnosis and failure prognosis for engineering systems: A global perspective
Bibliographic record
Abstract
Engineering systems, such as aircraft, industrial processes, manufacturing systems, transportation systems, electrical and electronic systems, etc., are becoming more complex and are subjected to failure modes that impact adversely their reliability, availability, safety and maintainability. Such critical assets are required to be available when needed, and maintained on the basis of their current condition rather than on the basis of scheduled or breakdown maintenance practices. Moreover, on-line, real-time fault diagnosis and prognosis can assist the operator to avoid catastrophic events. Recent advances in Condition-Based Maintenance and Prognostics and Health Management (CBM/PHM) have prompted the development of new and innovative algorithms for fault, or incipient failure, diagnosis and failure prognosis aimed at improving the performance of critical systems. This paper introduces an integrated systems-based framework (architecture) for diagnosis and prognosis that is generic and applicable to a variety of engineering systems. The enabling technologies are based on suitable health monitoring hardware and software, data processing methods that focus on extracting features or condition indicators from raw data via data mining and sensor fusion tools, accurate diagnostic and prognostic algorithms that borrow from Bayesian estimation theory, and specifically particle filtering, fatigue or degradation modeling, and real-time measurements to declare a fault with prescribed confidence and given false alarm rate while predicting accurately and precisely the remaining useful life of the failing component/system. Potential benefits to industry include reduced maintenance costs, improved equipment uptime and safety. The approach is illustrated with examples from the aircraft and industrial domains.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".