Telematics data-driven prognostics system for construction heavy equipment health monitoring and assessment
Bibliographic record
Abstract
Construction heavy equipment is a valuable asset for construction and equipment rental companies, which requires continuous monitoring and assessment for potential failures. Predictive maintenance has recently been proposed to as an alternative to preventive maintenance strategy by scheduling maintenance tasks just before a predicted failure of the equipment. Such predictive approach is dependent on the existence of a data collection and analysis system that monitors the equipment performance, compares it to the previous history, and predicts the failure events before their occurrence. This paper presents the development and validation efforts of a data-driven prognostics system that utilizes timely collected telematics data to monitor the equipment health condition and predict its failure hazard. The system is designed to utilize equipment telematics data to develop regression-based Cox’s proportional hazards functions. Regression analyses are performed for the historical telematics data to develop time-varying hazard functions for the successive life intervals of the equipment to generate dynamic predictions of its failure events. Accordingly, the outcome of the system would be the predicted probability of the equipment failure event considering the timely collected telematics data. The proposed prognostics system was validated by developing the hazard functions of two fleets of dozers and backhoes that provided high fit to the observed data and high prediction accuracy for the testing data. For both analyzed fleets, higher predictive and data fitting performance were achieved for later life intervals due the increased reliability of failure prediction for equipment with longer survival lives.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".