Hybrid Data Fusion DBN for Intelligent Fault Diagnosis of Vehicle Reducers

To solve the problem of low accuracy in traditional fault diagnosis methods, a novel method of combining generalized frequency response function(GFRF) and convolutional neural network(CNN) is proposed. In order to accurately characterize system state information, this paper proposed a variable step size least mean square (VSSLMS) adaptive algorithm to calculate the second-order GFRF spectrum values under normal and fault states; In order to improve the ability of fault feature extraction, a convolution neural network (CNN) with gradient descent learning rate and alternate convolution layer and pooling layer is designed to extract the fault features from GFRF spectrum. In the proposed method, the second-order GFRF spectrum of each state of Permanent Magnet Synchronous Motor (PMSM) is obtained by VSSLMS; Then, the two-dimension GFRF spectrum, which is regarded as the gray value of the image,will be further transformed into image. Finally, the CNN is trained with learning rate by gradient descent way to realize the fault diagnosis of PMSM. Experimental results indicate that the accuracy of proposed method is 98.75%, which verifies the reliability of the proposed method in application of PMSM fault diagnosis.

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“…[18], [29], deep belief neural (DBN)network in Ref. [30] and recurrent neural network(RNN) in Ref. [31] are adopted to diagnose PMSM faults.…”

Section: Comparison With Other Learning Networkmentioning

confidence: 99%

A novel method of combining generalized frequency response function and convolutional neural network for complex system fault diagnosis

2020

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“…Under the semi-structured environment of the Dutch greenhouse [35], the authors designed a two-wheel differential drive AGV called the BigPan [36]. The architecture of the self-designed AGVfor pesticide spraying is illustrated in Figure. 2 and Table. 1.…”

Section: B Organizationmentioning

confidence: 99%

A High-Precision Collaborative Control Algorithm for Multi-Agent System Based on Enhanced Depth Image Fusion Positioning

et al. 2020

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The collaborative control of the multi-agent system (MAS) marks the trend of intelligent transportation system (ITS). However, the collaborative control of MAS with flexible sampling periods remains a challenge, because under-driven systems are prone to random delays, data loss and sensor failures in semi-unstructured environment. Against the background of the semi-unstructured environment in a Dutch greenhouse, this paper puts forward a universal collaborative motion control algorithm for the MAS of automated guided vehicles (AGVs), in the light of the first-order dynamics of the system. The proposed algorithm is called continuous-step-rotate-run (CSRR). Besides, the enhanced depth image fusion positioning (EDIFP) scheme was designed to mitigate the disturbances on the control algorithm, arising from flexible sampling periods and data loss. To verify its effectiveness, the CSRR control algorithm was tested on an MAS of three under-driven BigPan AGVs. The results demonstrate that our algorithm can collaboratively control the AGVs in an effective and stable manner. The simple algorithm offers a desirable solution to the collaborative control of various MASs. INDEX TERMS Multi-agent system (MAS), collaborative control, automated guided vehicle (AGV), enhanced depth image fusion positioning (EDIFP). II. MAS COOPERATIVE CONTROL UNDER NON-UNIFORM SAMPLING CONDITIONS A. ''BIGPAN'' AGV SYSTEM

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“…(4) Repeat the "move-search" process according to the planned route until all tasks have been completed or reached a "termination point". (Note: The user can manually set the termination node or when the ITS needs to return to the service station when it detects a failure [55]. )…”

Section: Vision-based Navigation Systemmentioning

confidence: 99%

“…We designed and implemented a complete ITS system based on the above analysis and given the greenhouse application scenario [55]. Figure 10a shows the schematic diagram of a full-featured prototype.…”

Section: An Its Instance For Greenhouse Spraying Applicationmentioning

confidence: 99%

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Efficiency Analysis and Improvement of an Intelligent Transportation System for the Application in Greenhouse

et al. 2019

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In view of the future lack of human resources due to the aging of the population, the automatic, Intelligent Mechatronic Systems (IMSs) and Intelligent Transportation Systems (ITSs) have broad application prospects. However, complex application scenarios and limited open design resources make designing highly efficient ITS systems still a challenging task. In this paper, the optimal load factor solving solution is established. By converting the three user requirements including working distance, time and load into load-related factors, the optimal result can be obtained among system complexity, efficiency and system energy consumption. A specialized visual navigation and motion control system has been proposed to simplify the path planning, navigation and motion control processes and to be accurately calculated in advance, thereby further improving the efficiency of the ITS system. The validity of the efficiency calculation formula and navigation control method proposed in this paper is verified. Under optimal conditions, the actual working mileage is expected to be 99.7%, and the energy consumption is 83.5% of the expected value, which provides sufficient redundancy for the system. In addition, the individual ITS reaches the rated operating efficiency of 95.86%; in other words, one ITS has twice the ability of a single worker. This proves the accuracy and efficiency of the designed ITS system.

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Hybrid Data Fusion DBN for Intelligent Fault Diagnosis of Vehicle Reducers

Cited by 18 publications

References 48 publications

A novel method of combining generalized frequency response function and convolutional neural network for complex system fault diagnosis

A novel method of combining generalized frequency response function and convolutional neural network for complex system fault diagnosis

A High-Precision Collaborative Control Algorithm for Multi-Agent System Based on Enhanced Depth Image Fusion Positioning

Efficiency Analysis and Improvement of an Intelligent Transportation System for the Application in Greenhouse

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