Precision Retaining Time Prediction of Machining Equipment Based on Operating Vibration Information

Dai, Wei; Sun, Jinping; Huang, Tingting; Lu, Zhiyuan; Zhu, Liandie

doi:10.1109/access.2020.3014334

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…e rows of each matrix are used to store the corresponding coordinates of each frame, and the columns of each matrix are used to store the corresponding coordinates of all frames. erefore, the number of rows of the matrix represents the frame number N of the bone movement sequence [8].…”

Section: Form Conversion Of Bone Node Datamentioning

confidence: 99%

Research on Tennis Motion Evaluation Method and System Based on Deep Learning

Chang

2022

International Transactions on Electrical Energy Systems

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The current tennis training lacks the guidance to standardize the user action evaluation method; it is difficult to use the human movement analysis related technology to evaluate the user action and feedback. In this paper, a real-time evaluation algorithm for human movements in tennis training with the monocular camera is studied. Aiming at solving the problem of making use of temporal and spatial information of behavior sequence, an improved preprocessing algorithm based on depth image and bone data was proposed, and bone joint feature vectors were constructed to describe the topological shape of the human skeleton. A five-channel convolutional neural network (5C-CNN) model is proposed to effectively train multimodal behavior data. In order to solve the problem of human motion direction, the counterclockwise rotation angle of limbs was proposed as a motion evaluation feature. The experimental test on the MSR Action 3D database shows that the accuracy of the method for tennis movement evaluation reaches 98.1%, which verifies the validity of the model construction.

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Section: Form Conversion Of Bone Node Datamentioning

confidence: 99%

Research on Tennis Motion Evaluation Method and System Based on Deep Learning

Chang

2022

International Transactions on Electrical Energy Systems

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“…Since the mid-1980s, various types of coal mine monitoring systems have been installed in various large and medium-sized mines in China, in this way, the concentration of methane (gas) and CO, wind speed, environmental parameters such as air volume and dust, as well as voltage, current, the operating conditions such as the opening and closing of the air door and the opening and stopping of important equipment are monitored and simply controlled [1]. With the development of information technology and computer network technology, use the existing coal mine monitoring system and the company's internal information network, realize the real-time transmission of monitoring information on INTRANET/INTERNET, and use database and data warehouse technology to save effective data on the local server, so in addition to the dispatching rooms of the bureau, mine, and well, provide real-time relevant underground dynamic working condition information for authorized institutions at all levels, and send the information to the remote service center through INTERNET, information is processed by different tools, in order to provide good decision support information.…”

Section: Introductionmentioning

confidence: 99%

Prediction of operation vibration state of coal mine mechatronics equipment based on data mining

Jia¹,

Deb²,

Sajja³

2022

J. vibroeng.

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In order to explore the prediction of the operation vibration state of coal mine mechatronics equipment, the author proposes a method based on data mining in response to the problems of large amount of data in the operating state of coal mine electromechanical equipment, low data utilization, and slow speed of single-machine massive data mining, using Map Reduce technology, a dual Map Reduce mining prediction framework is proposed, establish a data mining prediction model for the running state of dual Map Reduce, using MapReduce1 to extract features of monitoring data, use MapReduce2 to predict and analyze feature data. Finally, by building the Hadoop platform, reveal the relationship between Hadoop cluster nodes and parallel processing speed, the efficiency of the data mining prediction framework is verified: Perform an experimental comparative analysis of the single prediction model and the proposed AGB combined prediction model, the prediction accuracy of the AGB combined prediction model is verified.

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“…Compared to mechanical signals, such as acoustic signals [ 8 ], acceleration [ 9 ], vibration signals [ 10 ], and cutting forces [ 11 ], current signals [ 12 ] can better reflect information about the machining process of a part. Hence, the use of current sensors is an effective means to realize the monitoring of equipment.…”

Section: Introductionmentioning

confidence: 99%

Monitoring the Production Information of Conventional Machining Equipment Based on Edge Computing

Wang

Shen

Zhu

et al. 2022

Sensors

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A production status monitoring method based on edge computing is proposed for traditional machining offline equipment to address the deficiencies that traditional machining offline equipment have, which cannot automatically count the number of parts produced, obtain part processing time information, and discern anomalous operation status. Firstly, the total current signal of the collected equipment was filtered to extract the processing segment data. The processing segment data were then used to manually calibrate the feature vector of the equipment for specific parts and processes, and the feature vector was used as a reference to match with the real-time electric current data on the edge device to identify and obtain the processing start time, processing end time, and anomalous marks for each part. Finally, the information was uploaded to further obtain the part processing time, loading and unloading standby time, and the cause of the anomaly. To verify the reliability of the method, a prototype system was built, and extensive experiments were conducted on many different types of equipment in an auto parts manufacturer. The experimental results show that the proposed monitoring algorithm based on the calibration vector can stably and effectively identify the production information of each part on an independently developed edge device.

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Precision Retaining Time Prediction of Machining Equipment Based on Operating Vibration Information

Cited by 4 publications

References 11 publications

Research on Tennis Motion Evaluation Method and System Based on Deep Learning

Research on Tennis Motion Evaluation Method and System Based on Deep Learning

Prediction of operation vibration state of coal mine mechatronics equipment based on data mining

Monitoring the Production Information of Conventional Machining Equipment Based on Edge Computing

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