کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4976747 | 1451836 | 2018 | 27 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Vibration and acoustic frequency spectra for industrial process modeling using selective fusion multi-condition samples and multi-source features
ترجمه فارسی عنوان
طیف فرکانس لرزش و صوتی برای مدل سازی صنعتی فرآیند با استفاده از نمونه های چند وضعیتی فیوژن انتخابی و ویژگی های چند منبع
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کلمات کلیدی
ارتعاش مکانیکی و سیگنال های صوتی، طیف فرکانس، گروه انتخابی چند لایه، کرنل جزئی ترین مربع، الگوریتم ژنتیک، ترکیب اطلاعات انتخابی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Mechanical Systems and Signal Processing - Volume 99, 15 January 2018, Pages 142-168
Journal: Mechanical Systems and Signal Processing - Volume 99, 15 January 2018, Pages 142-168
نویسندگان
Jian Tang, Junfei Qiao, ZhiWei Wu, Tianyou Chai, Jian Zhang, Wen Yu,