Industrial Pump Condition Monitoring with Audio Samples: a Low-Rank Linear Autoencoder Feature Extraction Approach
Bibliographic record
Abstract
Condition monitoring of industrial pumps plays a crucial role in predictive maintenance across various industries. From the wide array of techniques used for this task, those based on vibration monitoring through different sensing approaches have gained popularity for the cost effectiveness in the deployment of sensors. In this paper, we focus on the examination of a plant-specific case involving an industrial pump. The use of audio signals captured during pump operation for fault detection is investigated, leveraging signal processing and machine learning techniques. Specifically, an Autoencoder-based approach to extract a linear latent representation of the audio data, facilitating the characterization of pump degradation is presented. Experimental results demonstrate the efficacy of the proposed approach in capturing temporal variations in pump sound signatures and thus, it can be potentially used for early fault detection. Future research directions include expanding the dataset to include samples from machines in various stages of their life cycle to enable comprehensive characterization of pump behavior.
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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 | 0.000 |
| 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".