Recursive signal denoising method for predictive maintenance of equipment by using deep learning based temporal masking

• Published in: Computers & industrial engineering 2024-02, Vol.188, p.109921, Article 109921
• Main Authors: Ren, Jie, Zhang, Jie, Wang, Junliang, Zhao, Xueyi
• Format: Article
• Language: English
• Subjects: Denoising; Manufacturing equipment; Signal decomposition; Signal recognition; Winding machine
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2024.109921

•Uncertainty of noise increases the difficulty of equipment signal noise reduction.•Two-dimensional masking technique facilitates the decomposition of signal components.•Recursive strategy realizes source-by-source decomposition of signal components.•Model with adjustable parameters achieves adaptive recognition of signal sources.•Signal denoising is a prerequisite for fault prediction of equipment in enterprise. In the data driven predictive maintenance, high quality data is the premise of high accuracy diagnosis and prediction. In the industrial practice, reducing the noise is of great significance to ensure data quality. This paper proposes a recursive denoising method for manufacturing equipment signals in the data driven predictive maintenance. First, in signal decomposition method, equipment mixed signal is decomposed by temporal masking with dilated convolution neural network to generate a noise mask, which realizes signal decomposition of using recursive operation of temporal masking model. Second, in signal components recognition method, signal component features similarities are calculated, which act on the parameter regulation of signal recognition meta-learning model. The experimental results demonstrated that the proposed method effectively solves the noise reduction problem of the equipment signal. Further engineering tests of a chemical winding machine vibration signal decomposition and recognition show that the proposed method has strong adaptive performance for noise reduction.