FiLSTM: Fuzzy Rule Induction for LSTM Model: The Case of Predictive Maintenance
Bibliographic record
Abstract
Predictive Maintenance (PdM) is crucial in manufacturing, enabling early detection of equipment failures and optimized maintenance to improve reliability and reduce costs. Yet, extracting accurate and explainable predictions from complex time-series data remains challenging. Deep learning models such as Long Short-Term Memory (LSTM) networks offer strong predictive power but act as ‘black boxes,’ while fuzzy systems provide interpretability but lack robustness in dynamic industrial settings. To address these limitations, recent studies have integrated fuzzy inference with LSTMs. In this paper, we propose FiLSTM (Fuzzy Rule Induction for LSTM), a hybrid model that leverages Decision Trees to generate fuzzy rules and membership functions directly from data. This approach enhances both interpretability and prediction, achieving higher accuracy, faster computation, and lower execution time compared to FLSTM. FiLSTM advances PdM systems toward real-time, scalable, and transparent industrial applications.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".