Availability based maintenance scheduling in Domestic Hot water of HVAC system
Bibliographic record
Abstract
Reliability centered maintenance is an analytical tool for preventive maintenance planning. The availability based maintenance method is a branch of reliability centered maintenance, which considers mean time to failure (MTTF) and mean time to repair (MTTR). MTTF of a system is identified from the reliability distribution of its components, and MTTR defines maintenance period of components. In this sense, a reliability function is determined from historic failure data of components during their operation period (in form of a bathtub curve). MTTF is calculated based on this reliability function. This thesis is based on availability based maintenance on the domestic hot water (DHW) of HVAC system, which incorporates the time needed for maintenance of components in availability analysis. The Keeping system availability (KSA) method provides maintenance scheduling by considering the outcomes of the maintenance on the DHW system, while maintaining the availability of the current system. This method has been developed in the maintenance scheduling of power plants as the continual availability of the power generation systems is a critical issue. We have adopted this approach for DHW system of HVAC, which is a critical component in provision of hot water during long cold seasons in Canada. The availability based maintenance approach with KSA decision process has been developed to optimize the maintenance schedule of components in order to prevent over-maintenance. For this purpose, we rely on MTTF and MTTR. MTTF is quantified by the reliability function in a component, and its value should be modified based on pre-defined scenarios, which indicate average maintenance interval (AMI) types. Then, the existing components with a different maintenance times are sorted according to the maintenance effect on keeping the availability of the system, while reducing the maintenance cost. The sorting list is divided into two groups: top loop (components with low maintenance effect), and bottom loop (components with high maintenance effect). After running the KSA decision process, the outcomes consist of different “STEP NO” with different combinations of maintenance scenarios in the existing components in the DHW system. The main criterion in selection of the “STEP NO” is to have the modified availability (system with maintenance plan) equal to or greater than the current system availability (system without maintenance plan). In the next step, we examine changing the arrangement of the heat transfer sub-system from parallel to standby in order to reduce maintenance cost, while keeping the availability of the system at the same level. In addition, a replacement analysis is performed on the heat exchanger to identify the replacement time in its repairable subcomponents. Finally, a life cycle cost (LCC) analysis is performed to compare the maintenance cost and replacement cost between these two options.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".