Rejoinder to: “An overview of some classical and discussion of the signature‐based models of preventive maintenance”
Bibliographic record
Abstract
First, we express our sincere thanks to all the discussants for their constructive, interesting points, and promising comments.Receiving 11 discussions on the paper itself shows that the topic of optimal preventive maintenance of technical systems is one of the most interesting topics in reliability engineering due to its wide applications in practice.The discussants have provided very important comments on the paper, proposing new lines of future research on the signature-based maintenance of complex systems.In the sequel, we present our responses to the discussions to the main comments of the discussants.Extending the application of signatures to maintenance techniques other than age-based maintenance, such as condition-based maintenance and predictive maintenance, has been proposed in discussions by Zhang and Liu, Castro, Alfonso Surez-Llorens and Chahkandi.Because of the importance of condition-based maintenance in reliability engineering, this direction of research would of course be of great interest.We should mention that signature-based maintenance discussed in our review paper relies on the assumption that the lifetimes of the components of the system have a common continuous distribution.As long as the signature-based representation of system reliability is met (see the discussion by Spizzichino in this regard), then a condition-based maintenance approach may be scheduled based on various concepts of signature.Of course, associated mathematical computations may be complicated and tedious.As we have already mentioned in the paper, Hashemi and Asadi (2020) have proposed a type of condition-based maintenance for a coherent system equipped with a warning light that counts the number of failed components in the system.In the proposed strategy, the type of repair depends both on the age of the system and the number of failed components (see also Hashemi et al. (2020) and the references in the discussion by Castro for other condition-based maintenance policies of a system with different types of components using the notions of signature and survival signature).Zhang and Liu have commented on the efficient computation of signatures.Although assessing the signature of coherent systems has been discussed in the literature, the topic still needs to be investigated further to develop efficient algorithms for listing signature vectors of coherent systems of different orders.Navarro and Rubio 1 already provided a list of signatures of coherent systems with five components.A Monte-Carlo simulation approach has been discussed by Gertsbakh and Shpungin 2 to estimate the signature of systems (networks).Aslett 3 has developed a package called 'ReliabilityTheory' in R to evaluate the signature and survival signature of a coherent system.Here, we would like to state that future works should focus on explicitly quantifying the time complexity of the whole maintenance optimization procedure based on signatures.Castro has raised an interesting point, which is important from a practical point of view.She has mentioned the possibility of exploring how signature-based techniques can be employed in finite time horizon maintenance problems.We believe that this will certainly be an interesting line of research.She has also suggested that using a signature approach, different optimality criteria (other than renewal techniques) can be compared concerning computational time involved in the calculation of the signature vector.As we stated above, these important issues could be directions for further investigation in the future.Coolen et al. have made a detailed discussion on possible problems for survival signature-based approaches in maintenance strategies, in particular when the replacement of components is involved (see also Zhang and Liu for the same
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".