Real-time fault detection and isolation in industrial machines using learning vector quantization
Bibliographic record
Abstract
Abstract This paper presents a real-time approach to the problem of fault detection in industrial machines. In this study learning vector quantization (LVQ) of adaptive neural networks has been used to identify faults by relating patterns of fault signatures to their causes. The fault detection system is designed to operate based on steady state and transient responses obtained by monitoring sensitive parameters of an industrial machine. The steady state signal acts as a stimulus. When the signal exceeds a predetermined threshold it will initiate a non-destructive test on the machine during which a transient response of a second sensitive parameter will be captured. This transient pattern will be compared with the database of the patterns of fault signatures. The closest match will determine the cause of fault. The technique has been applied to a computer numerically controlled (CNC) machining centre. The diagnostic system is shown to be capable of first deciding whether the system is healthy or faulty; if faulty, it then decides whether one of the known faults or a novel fault, not seen before, is occurring. Having made the decision that one of the common faults is occurring it is then capable of deciding, from four different levels, the approximate severity level of the fault. In this approach, the use of LVQ significantly reduces the training time period of the network by about 90 per cent as compared to other learning methods such as back propagation (BP).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".