Assets Predictive Maintenance Using Convolutional Neural Networks
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".