Vibration and acoustic frequency spectra for industrial process modeling using selective fusion multi-condition samples and multi-source features. (15th January 2018)

• Record Type: Journal Article
• Authors: Tang, Jian; Qiao, Junfei; Wu, ZhiWei; Chai, Tianyou; Zhang, Jian; Yu, Wen
• Is Part Of: Mechanical systems and signal processing. Volume 99(2017)
• Page Start: 142
• Page End: 168
• Publication Date: 2018-01-15
• Subjects: Mechanical vibration and acoustic signals -- Frequency spectrum -- Multi-layer selective ensemble -- Kernel partial least squares -- Genetic algorithm -- Selective information fusion; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2017.06.008
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A multi-layer selective ensemble (MLSEN) method for modeling mechanical signals is proposed. The objective of MLSEN is to simulate domain experts' cognitive process in industrial practice. Selective information fusion based multi-condition samples and multi-source features is realized. Abstract: Frequency spectral data of mechanical vibration and acoustic signals relate to difficult-to-measure production quality and quantity parameters of complex industrial processes. A selective ensemble (SEN) algorithm can be used to build a soft sensor model of these process parameters by fusing valued information selectively from different perspectives. However, a combination of several optimized ensemble sub-models with SEN cannot guarantee the best prediction model. In this study, we use several techniques to construct mechanical vibration and acoustic frequency spectra of a data-driven industrial process parameter model based on selective fusion multi-condition samples and multi-source features. Multi-layer SEN (MLSEN) strategy is used to simulate the domain expert cognitive process. Genetic algorithm and kernel partial least squares are used to construct the inside-layer SEN sub-model based on each mechanical vibration and acoustic frequency spectral feature subset. Branch-and-bound and adaptive weighted fusion algorithms are integrated to select and combine outputs of the inside-layer SEN sub-models. Then, the outside-layer SEN is constructed. Thus, "sub-sampling training examples"-based and "manipulating input features"-based ensemble construction methods are integrated, thereby realizing the selective information fusion process based on multi-condition history samples and multi-source input features. This novel approach is applied to a laboratory-scale ball mill grinding process. A comparison with other methods indicates that the proposed MLSEN approach effectively models mechanical vibration and acoustic signals.