Gas Turbine Diagnostics by Means of Convolutional Neural Networks Fed With Time Series Data Encoded As Images
Bibliographic record
Abstract
Abstract The presence of anomalies in time series, i.e., data points that deviate from the expected behavior, can be a symptom of an incoming failure that may lead to costly repair. Thus, the detection of anomalous data by means of diagnostic methodologies can both reduce maintenance actions and asset unscheduled stops. To tackle this challenge, for the first time in the literature, we exploit the capabilities of Convolutional Neural Networks (CNNs), by feeding them with images obtained from multivariate time series data, transformed by means of two different approaches, namely, Gramian Angular Summation Field (GASF) and Markov Transition Field (MTF). Two CNN architectures are investigated, i.e., VGG-19 and SqueezeNet. The performance of both CNNs fed with images is compared to that of i) a Temporal Convolutional Network (TCN) fed with time series data and ii) a Support Vector Machine (SVM) model. In this paper, we present the comprehensive framework, which starts from time series transformation, goes through CNN development and ends with anomaly detection. The framework is applied to field data taken during normal operation of ten SGT-800 gas turbines, operated in combined cycle, running at base load, and located in two different regions. The normal data covers 150 days of operation. Spike faults are implanted in two out of the twenty available measured variables, i.e., compressor discharge temperature and compressor discharge pressure, by considering nine combinations of maximum fault magnitude and number of implanted spikes in each time series. The analyses carried out in this paper demonstrate that both CNNs fed with images achieve significantly higher classification accuracy than both a TCN model fed with time series data and an SVM model. Moreover, the MTF method always proves more robust than GASF method, and also allows higher accuracy values, in the range from 0.85 to 0.99.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".