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Robot fault diagnosis method and system based on feature engineering

A technology of feature engineering and machine failure, applied in the direction of instruments, relational databases, special data processing applications, etc., can solve problems such as poor adaptability and rough clustering methods, and achieve accelerated landing speed, excellent classification effect, and excellent feature fusion effect Effect

Pending Publication Date: 2021-10-01
SHANGHAI JIAO TONG UNIV +1
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Problems solved by technology

The clustering method of this scheme is relatively rough, and the feature selection method is relatively random, and the overall adaptability is poor

Method used

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  • Robot fault diagnosis method and system based on feature engineering
  • Robot fault diagnosis method and system based on feature engineering
  • Robot fault diagnosis method and system based on feature engineering

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Embodiment Construction

[0054] The embodiments of the present invention are described in detail below. This embodiment is implemented on the premise of the technical solution of the present invention, and detailed implementation methods and specific operating procedures are provided, but the protection scope of the present invention is not limited to the following implementation example.

[0055] Such as figure 1 Shown is a flowchart of a robot fault diagnosis method based on feature engineering according to an embodiment of the present invention.

[0056] Please refer to figure 1 , the robot fault diagnosis method based on feature engineering of the present embodiment includes:

[0057] S11: Collect the operating data of the robot under normal operation and preset types of faults;

[0058] S12: Using the operating data obtained in S11 as sample data, extracting data features;

[0059] S13: Clean the data features to obtain the integrated data features;

[0060] S14: Select effective features fr...

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Abstract

The invention discloses a robot fault diagnosis method and system based on feature engineering. The method comprises the following steps: acquiring operation data of a robot during normal operation and operation under a preset type of fault; taking the operation data as sample data, and extracting original data features of a time domain and a frequency domain; cleaning the original data features to obtain integrated data features; according to the importance of the data features, selecting the integrated data features, and selecting effective features; performing dimensionality reduction on the effective features, and using a feature dimensionality reduction method to extract main feature components of the data; and carrying out classified learning on the main feature components by adopting a classifier to realize fault diagnosis. The invention has the advantages that data mining does not depend on an accurate data model, the feature selection effect is high in computing power and cost performance, self-adaptive multi-scale feature fusion is achieved, and the classification effect is good.

Description

technical field [0001] The invention relates to the technical field of robot state monitoring, in particular to a method and system for robot fault diagnosis based on feature engineering. Background technique [0002] Industrial robots are widely used in industrial automation production. Due to wear and tear caused by long-term operation of robots, loosening or failures caused by emergencies, robots will fail to work, thereby reducing industrial production efficiency and affecting corporate earnings. Therefore, it is of great significance to carry out accurate and effective fault detection for robots. [0003] The faults of industrial robots are various, which can be roughly divided into sudden faults and degenerative faults. Among them, sudden failures are often caused by sudden and destructive emergencies without symptoms, such as collisions, stalls, etc.; degenerative failures are often caused by equipment aging, wear, corrosion, creep, etc. , causing performance to dev...

Claims

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Application Information

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IPC IPC(8): G06F16/245G06F16/215G06F16/28
CPCG06F16/245G06F16/215G06F16/285
Inventor 付庄方子周琳慧管贻生
Owner SHANGHAI JIAO TONG UNIV
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