Vibration-Based Fault Diagnosis of Hydraulic Pump of Tractor Steering System by Using Energy Technique
Bibliographic record
Abstract
This paper focuses on a problem of vibration-based condition monitoring and fault diagnosis of pumps used in the tractor steering system. The vibration-based machine condition monitoring and fault diagnosis incorporate a number of machinery fault detection and diagnostic techniques. The vibration signal from a piezoelectric transducer was captured for the following conditions: Normal pump (GOOD), Journal-bearing with inner face wear (BIFW), gear with tooth face wear (GTFW), and Journal-bearing with inner face wear plus gear with tooth face wear (G&BW) for three working levels of pump speed (1000, 1500, and 2000 rpm). Then Power Spectral Density (PSD) of vibration spectra was calculated. According to the results, fault diagnosis of hydraulic pump is a difficult task using spectrum of vibration signals alone. Results showed that different faults were showed different PSD versus frequency diagram. Comparison of numerical data produced by calculation the area under PSD v. Frequency diagram show that energy technique is an effective method for fault diagnosis of external gear hydraulic pumps.
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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 | 0.000 | 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".