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Research on Online Condition monitoring for Complex System based on Modified Broad Learning Systems

Wang Chong, Liu Jie

Eksploatacja i Niezawodność

2024

Vol. 26, no. 2

art. no. 183600

Complex systems contain numerous interacting components, thus deep learning methods with powerful performance and complex structure are often used to achieve condition monitoring. However, the deep learning methods are always too time-consuming and hardware-demanding to be loaded into complex systems for online training and updates. To achieve accurate and timely monitoring of complex system state, based on broad learning system (BLS), an online condition monitoring method is proposed in this paper. GeneralBLSs are based on a randomly generated hidden-layer, usually perform poorly in high-dimensional data classification tasks. In this work, based on correlation and causality, two modified BLSs are proposed and mixed to establish the online monitoring system. Specifically, logistic regression (LR) and structural causal model (SCM) are considered to form rough predictions of the system state, thus to replace the randomly generated ones with no practical significance. The effectiveness of the proposed online monitoring method is verified by both simulation data and real data.

Classification and inspection of milling surface roughness based on a broad learning system

Fang Runji, Yi Huaian, Wang Shuai, Niu Yilun

Metrology and Measurement Systems

2022

Vol. 29, nr 3

483--503

Current vision-based roughness measurement methods are classified into two main types: index design and deep learning. Among them, the computation procedure for constructing a roughness correlation index based on image data is relatively difficult, and the imaging environment criteria are stringent and not universally applicable. The roughness measurement method based on deep learning takes a long time to train the model, which is not conducive to achieving rapid online roughness measurement. To tackle with the problems mentioned above, a visual measurement method for surface roughness of milling workpieces based on broad learning system was proposed in this paper. The process began by capturing photos of the milling workpiece using a CCD camera in a normal lighting setting. Then, the train set was augmented with additional data to lower the quantity of data required by the model. Finally, the broad learning system was utilized to achieve the classification prediction of roughness. The experimental results showed that the roughness measurement method in this paper not only had a training speed incomparable to deep learning models, but also could automatically extract features and exhibited high recognition accuracy.