Cantilevered Piezoelectric Micro Generator Design Issues and Application to the Mining Locomotive
Bibliographic record
Abstract
This paper will present a complete discussion in recent design strategies for harvesting vibration energy using piezoelectric cantilever transducers. The interest in this primary energy source is due to its presence in non-negligible quantities in most of the engines used in the industrial process. Previous work has shown that it is possible to harvest significant amounts of energy capable of supplying a wireless sensor (WS) node. However, in most research, only one step of the energy conversion and utilization chain is studied. Starting from the definition of the different design issues for a piezoelectric micro generator (PMG), the leading optimization solutions will be reviewed in this paper. Based on the findings, the quantification of the data transmission range of wireless sensor nodes powered by a PMG is proposed to support the objectives envisioned by Industry 4.0. The vibration characteristics taken from mining locomotives that have not yet been treated previously are used to illustrate the improvement of the various optimization solutions. Through our objectives, this work offers a comprehensive discussion on the use of vibrational energy by wireless sensors, bringing together the fields of mechanics, electrical, electronics, and wireless communications. The theoretical basis for each design stage is provided through the design equations. Based on actual measurements of ambient vibration, it is demonstrated, considering an optimal design of the PMG, that a WS could transmit data beyond 1 km for physical phenomena to be controlled every 7 min.
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 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".