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

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...

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, resulting in foam flotation. It is difficult to achieve reliable real-time judgment in the fault diagnosis of the selection process. With the rapid development of information technology and digital image processing technology, many data-driven fault diagnosis methods have emerged one after another.

The existing fault diagnosis methods are only aimed at various image features at a single moment. These methods have limitations in the data volume range, and do not take the industrial process as a dynamic process to extract its changing trend characteristics, and it is difficult to describe the fault occurrence in a multi-level and three-dimensional manner. Time-to-time mode change information makes it impossible to monitor abnormal working conditions in time

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