Nowadays the industrial Internet of things (IIoT) has been successfully utilized in smart manufacturing. The massive data in IIoT promotes the development of deep learning-based health monitoring for industrial equipment. Since monitoring data for mechanical fault diagnosis collected on different working conditions or equipment have domain mismatch, models trained with training data may not work in practical applications. Therefore, it is essential to study fault diagnosis methods with domain adaptation ability. In this paper, we propose an intelligent fault diagnosis method based on an improved domain adaptation method. Specifically, two feature extractors concerning feature space distance and domain mismatch are trained using maximum mean discrepancy (MMD) and domain adversarial training respectively to enhance feature representation. Since separate classifiers are trained for feature extractors, ensemble learning is further utilized to obtain final results. Experimental results indicate that the proposed method is effective and applicable in diagnosing faults with domain mismatch.

Data-driven fault diagnosis is essential for the reliability and safety of industry equipment. However, the lack of real labeled fault data make the machine learning based diagnosis methods difficult to carry out. To solve this problem, this paper proposes a new fault diagnosis framework called multi-label 1D generation adversarial network (ML1D-GAN). In our method, Auxiliary Classifier GAN (AC-GAN) is utilized first for real damage data generation. Then the generated and real damage data are both used to train the fault classifier. Experimental results reveal that the generated data is applicable, and ML1D-GAN improves the diagnosing accuracy for real bearing faults from 95% to 98% when trained with the generated data. The scalability of the learning model is also proven in the experiment.

Massive industrial data collected from industrial Internet of things (IIoT) assets improves data-driven methods for prognostics and health management (PHM) systems. As an important role in PHM, remaining useful life (RUL) prediction is essential to maintain the reliability and safety of industrial manufacture. However, recent data-driven approaches for bearing RUL prediction do not weight the contributions of data from different sensors and time steps, which decreases the efficiency in the big data era. In this context, we present a deep learning-based RUL prediction method with attention mechanism to weight sequence data’s representations. Specifically, the proposed method weights different industrial sensors and time steps respectively based on distributed attention mechanism. Then temporal convolution modules with the shared weights are used for feature extraction of time series. Finally, performance of the proposed method is verified using a popular dataset C-MAPSS. Experimental results reveal that the proposed method has high accuracy and is efficient in practical applications.

Industrial internet of things (IIoT) obtains big data from industrial facilities. Based on these data, health conditions for facilities can be predicted using machine learning methods, which in turn improves the trustworthiness of IIoT. Intelligent fault diagnosis is developing with this process. Since machine damages always happen under different circumstances, the designed intelligent fault diagnosis methods should have domain adaption ability. In this paper, a data-driven fault diagnosis method called domain adaption network (DAN) is present, in which the difference between training and testing data can be minimized while the maximum training accuracy can be maintained. First, DAN uses a combined loss for consistent feature learning and optimum training classification performance. Then a DAN retraining (DAN-R) strategy is employed based on weighted pseudo-labeled testing dataset. Finally, three experiments are performed on two fault diagnosis datasets. The results reveal that DAN-R is applicable and has outstanding domain adaption ability.

Kun Li, Chengbo Chen, Xiaojun Quan, Qing Ling, Yan Song. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020.