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Fault diagnosis method for explosion-proof forklift based on machine learning and cluster information fusion

A fault diagnosis and machine learning technology, applied in the field of construction machinery, can solve problems such as difficulty in troubleshooting and long diagnosis time period, and achieve the effect of avoiding cumbersomeness and widening the scope.

Active Publication Date: 2022-05-10
ZHEJIANG UNIV
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The fault diagnosis of existing explosion-proof forklifts is mainly carried out manually by professional technicians, which often takes a long time for diagnosis and is difficult to troubleshoot

Method used

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  • Fault diagnosis method for explosion-proof forklift based on machine learning and cluster information fusion
  • Fault diagnosis method for explosion-proof forklift based on machine learning and cluster information fusion

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specific Embodiment approach

[0035] Such as figure 1 Shown, the present invention comprises the following steps:

[0036] 1) Select multiple explosion-proof forklift parts of the same type and with different failure levels;

[0037] 2) Measure the temperature rise data of one type of explosion-proof forklift parts from the beginning of work to the end of work, as the temperature rise information of explosion-proof forklift parts, the network fault level parameter A of explosion-proof forklift parts with different fault levels is different;

[0038] Step 2) is specifically:

[0039] 2.1) Select a plurality of explosion-proof forklift parts with the same type of normal, minor faults and severe faults, the network fault level parameter A of the normal explosion-proof forklift parts satisfies A=0, the network fault level parameter of the explosion-proof forklift parts with mild faults A satisfies A=0.5, and the network failure level parameter A of explosion-proof forklift parts with severe faults satisfies ...

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Abstract

The invention discloses an explosion-proof forklift fault diagnosis method based on machine learning and cluster information fusion. The method includes the following steps: 1) input the temperature rise information of different types of explosion-proof forklift parts and the corresponding network fault level parameter A to the one-dimensional convolutional neural network model for training, and obtain different types of trained one-dimensional convolutional neural network models. Network model; 2) Calculate and obtain the part cluster information corresponding to different types of explosion-proof forklift parts; 3) At the work site, collect the temperature rise information of one type of explosion-proof forklift parts during normal operation, and obtain the network faults of the explosion-proof forklift parts respectively The level parameter A and the part cluster information are used to calculate the final diagnosis result C to obtain the failure level of the explosion-proof forklift part. The invention uses the temperature sensor for safety monitoring of each part in the explosion-proof forklift to obtain temperature information, is economical and has wide applicability, and can be used for fault diagnosis of various types of explosion-proof forklift parts.

Description

technical field [0001] The invention belongs to the field of engineering machinery and relates to a fault diagnosis method for explosion-proof forklifts, in particular to a fault diagnosis method for explosion-proof forklifts based on machine learning and cluster information fusion. Background technique [0002] Forklifts are common production and handling vehicles in the industry. They are widely used in ports, stations, airports, freight yards, factory workshops, warehouses, distribution centers, and distribution centers. They can complete the loading and unloading, stacking, and short-distance transportation of palletized goods. Operation. The circuit equipment of ordinary forklifts is prone to sparks during the working process, and the heat release of some parts during the working process may cause local high temperatures. These two problems also determine that ordinary forklifts cannot be used in some places with high dust concentration, or in the production, transport...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/27G06K9/62G06N3/04G06N3/08G06Q10/00G06Q10/06G01M17/007G06F119/08
CPCG06F30/27G06Q10/06393G06Q10/20G06N3/08G01M17/007G06F2119/08G06N3/045G06F18/2415
Inventor 邹俊林方烨
Owner ZHEJIANG UNIV