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A zero-shot diagnosis method based on semantic embedding that effectively overcomes the shortage of compound fault samples and the absence of labeled samples in bearing systems is proposed.
Results indicate that the proposed framework can provide field anomaly-warning and severity-oriented interpretation under weak-label monitoring conditions, however, it should not be interpreted as a replacement for disassembly-confirmed fault-type diagnosis or remaining useful life prediction.
A visual interpretation-based intelligent fault diagnosis framework is developed for switch-machine fault diagnosis using acoustic signals and achieves high diagnostic accuracy under different time–frequency representations, with several configurations reaching 100% accuracy under the current evaluation protocol.
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