Abnormal detection for brake of mining electric wheel dump-trucks
In response to the demand for abnormal working condition detection of the brake system of mining electric wheel dump trucks,a brake status acquisition system was designed,and feature extraction and abnormal detection of monitoring data were achieved based on a long short-term memory network.The brake status acquisition system is mainly composed of an onboard monitoring terminal,a cloud service ter-minal,and a user terminal,which realizes data acquisition,transmission,and abnormal warning of the brake system.Based on the monitoring data,a long short-term memory network was used to construct an abnormal working condition detection model,which extracted features from the multi-dimensional time se-ries data generated during the actual operation of mining trucks.The model propagates early input sequence information to later memory units,effectively solving the long-term dependency problem of time series da-ta.Experimental results show that the proposed method has an accuracy rate of abnormal condition recogni-tion of over 93％,which is significantly better than threshold-based detection methods.
mining electric wheel dump-trucksabnormal detection of brake systemlong short-term mem-ory network
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