Assets Predictive Maintenance Using Convolutional Neural Networks
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
Predictive Maintenance (PdM) performs maintenance based on the asset's health status indicators. Sensors can measure an unusual pattern of these indicators, such as an increased motor's vibration level or higher energy consumption, and, in most cases, failures are preceded by an unusual pattern of these measurements. Convolutional Neural Network (CNN) is a Machine Learning technique capable of extracting data representation. This paper presents a CNN framework to tackle assets predictive maintenance problem and a method to transform 1-dimensional (1-D) data into an image-like representation (2-D). A data transformation step is very important to make the use of CNN feasible. To evaluate the proposed framework two datasets were obtained from fans, with distinct electrical pattern, from a building at Western University. The data was preprocessed, transformed in a image-like representation and fed to a tuned classifier. The results presented by the CNN-PdM framework showed that the combination of CNN with the proposed data transformation method outperformed traditional machine learning techniques (Random Forest, Support Vector Machine, and Multi-Layer Perceptron). The model created by the CNN-PdM framework achieved accuracy rates as high as 98% for one of the datasets and 95% for the other.
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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 it