DPC-based method and system for detecting abnormality of multi-door system of rail vehicle
An anomaly detection and rail vehicle technology, applied in the direction of railway vehicle testing, etc., can solve the problems of different parts, many parts, and inability to detect the abnormality of subway vehicle door system, and achieve good universality and reduce repetition.
Inactive Publication Date: 2019-02-22
NANJING KANGNI MECHANICAL & ELECTRICAL +1
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The invention discloses a DPC-based method and system for detecting abnormality of a multi-door system of a rail vehicle. The method comprises the following steps that: feature extraction is carried out on main track data of a vehicle door system, each door opening-closing process is segmented, time-domain feature extraction and frequency-domain feature extraction are carried out respectively on aplurality of motor parameter value of each segment, the time-domain features and frequency-domain features are combined to generate system state variables of the vehicle door system; on the basis ofa DPC method, a system multi-door health degree model is established for the extracted system state variables; and an e abnormal state of the vehicle door is identified by using an Euclidean distance.According to the invention, mathematical modeling is carried out based on a density peak clustering algorithm and the health degree model of the multi-vehicle door system is established; periodical horizontal comparison is carried out on the obtained multi-vehicle door model by using the established model to complete abnormality detection of the vehicle multi-door system. Therefore, the repetitive experimental design and data collection work are reduced. The density peak clustering algorithm is applied to the rail vehicle door fault detection technology first time; the influence on the vehicle door type or locking device type is eliminated; and the universality is high.
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