Fuzzy fault diagnosis method for zinc flotation process based on time series characteristics

A technology of time series and flotation process, which is applied in the direction of program control, instrumentation, electrical testing/monitoring, etc., and can solve problems such as lagging test results, long process flow, and limited data volume range

Active Publication Date: 2019-08-09
CENT SOUTH UNIV
View PDF8 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Since froth flotation is a complex industrial process, the process flow is long, the sub-processes are seriously coupled, and some parameters cannot be effectively measured. As a result, current technical means cannot monitor the occurrence of fluctuations in time. In addition, the rotation and actual operation of on-site operators The subjectivity and arbitrariness of the system are relatively large, which also leads to the lack of uniform standards for fault diagnosis.
Although it is possible to analyze concentrate and tailings grades through off-line assays, the assay results lag behind, and it often takes a long time for the concentrate grade to respond to failures from local faults to affect the fluctuation of flotation concentrate grades, resul

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Fuzzy fault diagnosis method for zinc flotation process based on time series characteristics
  • Fuzzy fault diagnosis method for zinc flotation process based on time series characteristics
  • Fuzzy fault diagnosis method for zinc flotation process based on time series characteristics

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0054] figure 1 It is a flowchart of the present invention.

[0055] Step 1: Use the flotation on-site image acquisition system to collect the froth video of zinc flotation at historical moments and convert the froth video into continuous images, and perform data preprocessing on the collected zinc flotation image data, as follows:

[0056] 1) Eliminate erroneous data that exceeds the normal change threshold;

[0057] 2) Eliminate incomplete data;

[0058] Step 2: Convert the foam image from an RGB color image to a grayscale image, and extract the grayscale mean value as the source image feature to obtain a time series image feature I=[I 1 ,I 2 ,...,I q ], q is the number of image features arranged in chronological order;

[0059] Step 3: Use the piecewise linearization algorithm for the image feature I of the time series, take all extreme points as endpoints, perform piecewise linearization on the time series, and extract its linear structural features, as follows:

[0...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to a fuzzy fault diagnosis method for a zinc flotation process based on time series characteristics, and belongs to the field of froth flotation. The invention discloses a fuzzyfault diagnosis mode in a flotation process, which is based on foam visual time sequence feature extraction, defines subsequences and submodes of a foam time sequence, establishes a historical featuretrend information set by adopting historical data information, measures the similarity of real-time trend features and the historical trend information set, and performs fuzzy diagnosis on the faultoccurrence probability by integrating sequence trend information. The invention provides a concept of fuzzy fault diagnosis, establishes a flotation condition state forecast representation model through reliability sequence selection and abnormal factor establishment, and provides a new solution for judgment of trend and possibility of numerical trend. The defect that the flotation process is statically described by the original foam characteristics is overcome, abnormal signs of the working conditions are found in time, the probability of faults at the future moment is displayed in a numerical mode, and the method is beneficial to workers to operate in time and stably optimize production.

Description

technical field [0001] The invention belongs to the technical field of froth flotation, and in particular relates to a fault diagnosis method in the zinc flotation process. Background technique [0002] Froth flotation is a mineral processing method widely used at home and abroad. This method can effectively separate the target minerals according to the difference between the hydrophilicity and hydrophobicity of the mineral surface. In the process of froth flotation, the target mineral and its symbiotic gangue are ground into particles of suitable size and then sent to the flotation tank. The surface properties of different mineral particles are adjusted by adding reagents, while the flotation process is continuously stirred and blown to make the slurry A large number of bubbles with different size, shape, texture and other characteristic information are formed in the flotation cell, so that useful mineral particles adhere to the surface of the bubbles, and the bubbles carry...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G05B23/02
CPCG05B23/0262G05B2219/24065
Inventor 唐朝晖范影张国勇张进张虎
Owner CENT SOUTH UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products