Remote Monitoring of Heavy-duty Equipment for Predictive Control
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Controller Area Network (CAN) bus is considered the backbone of many mobile machines. It is connected to and communicates with every single Electronic Controller Unit (ECU) in the vehicle via CAN data frames. With modern technology, the demand for effectively utilizing this vital system can heavily reduce costs for all vehicle industries, while improving safety. Tracking each vehicle's ECU status such as motor speed, fuel consumption, and engine temperature, while logging and analyzing the data could help in monitoring and ultimately predicting vehicle failures before they happen. Building upon the CAN bus architecture, this paper presents a systematic approach that employs the J1939 protocol, to form a high layer protocol to log and parse data from the bus using the industry-standard Media Descriptor File (MDF) file format and utilizing an Internet of Things (IoT) device in addition to cloud computing. The proposed system was implemented and tested on a heavy-duty industrial snowblower as used in airports. This new system is being used in two industrial companies because of its early promising results.
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.
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 it