Gene

Quantum mechanics

Detector

Biology

Robustness (evolution)

Generative adversarial network

Telecommunications

Power (physics)

Biochemistry

Computer science

Fault (geology)

Seismology

Geology

Paleontology

Physics

Generative grammar

Discriminator

Reliability (semiconductor)

Artificial intelligence

Generator (circuit theory)

Chemistry

Deep learning

Convolutional neural network

Machine learning

Adversarial system

Mechanical Systems and Signal Processing

Fault detection and diagnosis of gear systems using vibration measurements play an important role in ensuring their functional reliability and safety. Computational intelligence, leveraging upon classification through various surrogate models, has recently demonstrated certain level of success. Major challenge however remains. The establishment of surrogate models generally requires large size of training data with specific labels corresponding to explicitly known gear fault conditions, which may not be available in practical applications. Both the size of available data and the respective labels may be quite limited due to the high cost, which hinders the diagnosis of unseen/unexpected faults with desired reliability. In this research we synthesize a deep convolutional generative adversarial network (DCGAN) to tackle this challenge. This new approach follows the semi-supervised learning concept, the performance of which is significantly enhanced by introducing additionally the inexpensive unlabeled data. The balanced adversarial effect between the discriminator and generator in DCGAN is realized by appropriately designing their architectures, which as a result can enable the high accuracy of diagnosis with scarce labeled data. More importantly, by taking full advantage of the rich fault signatures in the unlabeled data that point to the diverse unseen faults, the intrinsic correlation of underlying physics between the unseen and known faults can be implicitly elucidated via unique semi-supervised learning strategy featured in DCGAN. Therefore, the extended capability in diagnosing the unseen faults that are beyond the known faults in training dataset can be realized, which bears practical significance. Systematic case studies using experimental data acquired from a lab-scale gear system are carried out to validate the new diagnosis framework.

Deep convolutional generative adversarial network with semi-supervised learning enabled physics elucidation for extended gear fault diagnosis under data limitations