The paper presents a motor bearing fault diagnosis method based on MSICA (Multi-scale Independent Principal Component Analysis) and LSSVM (Least Squares Support Vector Machine). MSICA is introduced into motor fault diagnosis. First, wavelet decomposition is used, and then ICA models are built by wavelet coefficients in each scale, which detect fault, and finally LSSVM is used to classify fault type. Conclusions are obtained from the analysis of the experimental data provided by Case Western Reserve University’s Bearing Data Website. And it indicates that the proposed method is simple and effective.
This paper put forward a method on the premise of not changing the hardware circuit of the HPLC communication unit of the online electrical meter. In the HPLC communication unit of the electrical meter, the electrical meter address is modulated to the electric-power line through a high-speed carrier signal by the method of on-off keying. In the intelligent molded case circuit breaker of the meter box, after the new-type instrument transformer is used to extract the signal, the optimization algorithm is adopted to extract the electrical meter address to achieve physical topology identification of the meter box, forming a physical topographic map of low-voltage distribution area at the meter box level, which has the characteristics of low cost and high identification rate. This method is particularly suitable for the situation where HPLC communication units have been used in large quantities. Based on the current meter box operating method, it can effectively achieve the physical topology identification for the low-voltage distribution area, improve the physical topology identification rate for the meter box and reduce the identification cost.
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