Current Practices for Preventive Maintenance and Expectations for Predictive Maintenance in East-Canadian Mines
Bibliographic record
Abstract
Preventive maintenance practices have been proven to reduce maintenance costs in many industries. In the mining industry, preventive maintenance is the main form of maintenance, especially for mobile equipment. With the increase of sensor data and the installation of wireless infrastructure within underground mines, predictive maintenance practices are beginning to be applied to the mining equipment maintenance process. However, for the transition from preventive to predictive maintenance to succeed, researchers must first understand the maintenance process implemented in mines. In this paper, we conducted interviews with 15 maintenance experts from 7 mining sites (6 gold, 1 diamond) across East-Canada to investigate the maintenance planning process currently implemented in Canadian mines. We documented experts’ feedback on the process, their expectations regarding the introduction of predictive maintenance in mining, and the usability of existing computerized maintenance management software (CMMS). From our results, we compiled a summary of actual maintenance practices and showed how they differ from theoretical practices. Finally, we list the Key Performance Indicators (KPIs) relevant for maintenance planning and user requirements to improve the usability of CMMS.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".